Drawing on my work in evolutionary ecology, wilderness ethics, and machine learning, I reflected on the tension between our desire to intervene and our limited ability to forecast long-term ecological outcomes. Using examples like the Chernobyl exclusion zone—where many species are thriving in the absence of people despite nuclear contamination—I argued that ecological recovery is often less about precision intervention and more about restraint. We discussed how machine learning can help us simulate alternative futures and understand potential tradeoffs, but that ultimately, the most powerful conservation tool may be humility. More wilderness, not more control, might be the best way to meet the uncertainties ahead.

Listen to the episode here or wherever you get your podcasts.

Analyzing qualitative data is challenging. Such analyses are even more difficult when the topic is controversial and the results will drive important policy decisions. This post explores AI methods for qualitative research, using chatGPT for categorization, embeddings to find hidden topics, and long-context summarization with Claude2 on a case study analyzing free-text public comments to a controversial Environmental Impact decision.

Background

Quite a while ago, I detailed why replacing wolves on Isle Royale National Park was a bad policy decision back by even worse science. Since then, the National Park Service (NPS) decided to commit to wolf replacement anyway, dropping 19 new wolves on the island in 2018 and 2019. The results were expected. The new wolves killed the last original male wolf in 2019, almost certainly ensuring that the new wolf population will be genetically disconnected from the prior population. Of the 20 wolves that NPS attempted to relocate, one died before making it to the island, one voluntarily crossed the ice back to the mainland*, and four others died by the end of 2019. The surviving 14 wolves successfully bred and the population now stands at 31. So, in the end, we have a new, synthetic wolf population that is entirely disjunct from a genetic and ecological perspective. As I predicted in my original post: “in reality, this is not a genetic rescue project, it is a genetic replacement project,” which violates both the scientific and management purpose of the Park.

* This contradicts one of the primary justifications for replacing the wolves. Proponents argued that the lack of ice due to climate change would make natural repopulation impossible.

But neither science nor policy drove NPS’s decision. Management of charismatic mammals, especially in a well-known National Park, is largely a matter of public sentiment. In fact, it is a codified part of the decision process. Federal managers are required to seek public comments as part of the NEPA process.

In general, I am a huge supporter of public voices in important conservation decisions (I’ve even written papers advocating for it). But, sometimes I worry about how advocacy groups can skew the perception of organic public sentiment. That’s what I’d like to analyze in this post.

All of the public comments submitted to NPS on the Isle Royale wolf-moose management plan are public record. You can download and read all 1117 pages of comments.

But 1117 pages is a lot of text to read and digest. In this post, I want to show how you can easily process lots of text using AI (both generative large-language models (LLM), like chatGPT, and LLM embeddings) to make quantitative (or semi-quantitative) analyses.

Basic analyses

Visit my GitHub repo for this project for a fully reproducible analysis.

First, we’ll set up the environment and load in necessary packages.

# Load libraries
library(pdftools) # We will use 'pdftools' to convert the pdf to plain text
library(tidyverse)
library(stringr)
library(RColorBrewer)

# Set up the directory structure:
make_new_dir <- 
     function(DIR_TO_MAKE){
          if(dir.exists(DIR_TO_MAKE) == FALSE){
               dir.create(DIR_TO_MAKE)
          }else{
               print("Directory exists")
          }
     }

make_new_dir("./data/")
make_new_dir("./figs/")

We can download the comments from the NPW website.

download.file(
     url = "https://parkplanning.nps.gov/showFile.cfm?projectID=59316&MIMEType=application%252Fpdf&filename=ISRO%5FMWVPlan%5FAllCorrespondence%5FPEPC%2Epdf&sfid=232552",
     destfile = "./data/ISRO_MWVPlan_AllCorrespondence_PEPC.pdf",
mode = "wb"
)

The first step to analyze the public comments is to parse the pdf into text. This is a tedious process. I won’t show it here, but you can follow all of the steps on my GitHub repo for this project.

You can download my pre-processed dataset to short-cut the the PDF parsing steps.

download.file(
     url = "https://www.azandisresearch.com/wp-content/uploads/2023/09/EIS_comments.csv",
     destfile = "./data/EIS_comments2.csv"
)

EIS_comments <- read.csv("./data/EIS_comments.csv")

The formatting follow the same structure for every comment. I’ve extracted the ‘Comment ID’, ‘Received’ date time, ‘Correspondence Type’, and ‘Correspondence’ text into a dataframe. I’ve also truncated the longest comments (…comment 68 looks like someone copy and pasted their term paper) to 12,000. This will be important later because the context window for chatGPT is 4000 tokens.

EIS_comments %>% glimpse()
Rows: 2,776
Columns: 4
$ ID             <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,…
$ Received       <dttm> 2015-07-12 20:45:30, 2015-07-14 23:18:34, 2015-07-15 12:03:55, 2015-07-15 13:14:52, 2015-07-15 13:35:47, …
$ Correspondence <chr> "Web Form Correspondence", "Web Form Correspondence", "Web Form Correspondence", "Web Form Correspondence"…
$ Content        <chr> "The alternatives are complete enough as a starting point. The issues will be related to the details. The …

We can do some basic summary analysis on these initial variables. The most comments were submitted in the week before the comment deadline on Sept 1. The vast majority of comments were received through the web form. Less than 10% of comments were physical letters and 51 of the 2777 comments were form cards given to Park visitors.

Often, large influxes of web and email comments are the product of advocacy groups encouraging their members to submit pre-written comments. I’ve used this tactic myself in conservation campaigns, so I won’t cast dispersions. But, I’ll also be the first to admit that a copy-and-pasted form letter is far less sincere than a uniquely crafted opinion.

After checking for matches among the comments, it is clear that there were two archetypical pre-written texts.  These include 733 near identical comment in favor of wolf replacement (i.e. Alternative B), likely from National Parks Conservation Association:

EIS_comments %>%
+   filter(grepl("I care about the wildlife at our national parks, including the wolves and moose at Isle Royale. Right now there are only three", Content)) %>%
+   group_by(Content) %>%
+   tally() %>%
+   arrange(desc(n)) %>%
+   ungroup() %>%
+   filter(row_number() == 1) %>%
+   .$Content %>% 
+   cat()
Dear Superintendent Green, I care about the wildlife at our national parks, including the wolves and moose at Isle Royale. Right now there are only three wolves left at the park- -the lowest number of wolves in more than 50 years- -threatening the overall ecosystem health of this iconic national park. I support management Alternative B to bring new wolves to the island, but urge the Park Service to do this as needed, rather than one time only. Without wolves, the moose population on the island will continue to increase, eating until the food sources are gone. If we bring new wolves to the island, they will help keep the moose population from rapidly expanding and minimize impacts to the native vegetation. This option is much less intrusive in this wilderness park than culling moose, removing moose from the island, or having to replant native vegetation once the moose consume it. As stewards of this park, the National Park Service should take the least intrusive action that results in the biggest benefit to the island's wildlife and ecosystem. I support the Park Service taking action to bring new wolves to the park immediately, before the population vanishes altogether. Thank you for considering my concerns. Sincerely,

And 55 nearly identical comments in favor of Wilderness (i.e. Alternative A), likely from Wilderness Watch:

EIS_comments %>%
+   filter(grepl("Isle Royale's wilderness designation requires that we protect the area's unmanipulated, untrammeled wilderness character. Wild", Content)) %>%
+   group_by(Content) %>%
+   tally() %>%
+   arrange(desc(n)) %>%
+   ungroup() %>%
+   filter(row_number() == 1) %>%
+   .$Content %>% 
+   cat()
Isle Royale's wilderness designation requires that we protect the area's unmanipulated, untrammeled wilderness character. Wilderness designation means we let Nature call the shots. Transplanting wolves from the mainland to Isle Royale is a major manipulation of the Isle Royale Wilderness and must not be done. Alternative Concept A, the No Action Alternative, is the best alternative to protect Isle Royale's unmanipulated, untrammeled wilderness character.

It is important to flag these duplicated comments because the methods that we will use later on will not behave correctly with nearly identical strings.

EIS_comments_deduplicated <- 
     EIS_comments %>%
     # Remove comments with no content
     filter(!is.na(Content)) %>%
     # Flag the web form duplicates
     mutate(form_duplicate = ifelse(grepl("I care about the wildlife at our national parks, including the wolves and moose at Isle Royale. Right now there are only three", Content), "for Alt B", NA)) %>%
     mutate(form_duplicate = ifelse(grepl("Isle Royale's wilderness designation requires that we protect the area's unmanipulated, untrammeled wilderness character. Wild", Content), "for Alt A", form_duplicate)) %>%
     # Form duplicates are not exact matches
     mutate(Content_dup = ifelse(is.na(form_duplicate), Content, form_duplicate)) %>%
     group_by(Content_dup) %>%
     # Retain one of the duplicate sets
     slice_sample(n = 1)

After removing the duplicates and cleaning the data, we are left with 1970 unique comments.

Text analysis with chatGPT

Now, we can start analyzing the content. There are many ways that we could do this, depending on the question we want to answer. For instance, maybe we want to see with questions naturally group together to see if we can find common themes? Traditionally, a common way to do this type of natural language processing would be to use an approach like a Latent-Dirchelt allocation topic analysis that groups comments by tf-idf values of the stems of words contained in the comment. (I cover tf-idf in a previous post). But, one problems with this approach is that the context of words is lost.

If we want to capture the context of the text, we might try using word embeddings from a LLM like GPT. We’ll try this approach later.

In our case, maybe we just want to know how many comments support a given policy.. It would be hard to answer that from the embeddings ourselves, but we could treat GPT as an agent who could read and categorize comments by preferred policy alternative.

We’ll use two packages. httr helps us interact with the chatGPT API. The API speaks in json format. jsonlite helps us parse formatted prompts and responses.

library(httr)
library(jsonlite)

Working with chatGPT is a lot like working with a new intern. Like an new intern, it has no prior contextual understanding of our specific task–we have to be very explicit with our directions. On the bright side, our chatGPT intern has endless patience and never sleeps!

We will be interacting with chatGPT through the API. This differs from the dialectical way that most people interact with chatGPT. We need to engineer our prompt to get a robust response in exactly the same format, every time.  We can do that by passing in quite a bit of context in our prompt and giving specific directions for the output, with examples. Here is the prompt we’ll use:

You are a federal employee tasked with reading the following comment submitted by a member of the public in response to the The Isle Royale National Park Moose-Wolf-Vegetation Management Plan/EIS. The Plan/EIS is a document that evaluates management alternatives for the moose and wolf populations on the island National Park land.
Management alternatives include:

- Alternative A: No Action. Continue the current management of letting nature take its course, without any intervention or manipulation of the moose or wolf populations or their habitats.
- Alternative B: Immediate Wolf Introduction. Introduce 20-30 wolves over a three-year period, starting as soon as possible to reduce the moose population and its impacts on vegetation.
- Alternative C: Wolf Introduction after Thresholds are Met. Introduce wolves if certain thresholds are met, such as the extirpation of wolves, the overabundance of moose, or the degradation of vegetation. The number and timing of wolf introductions would depend on the conditions at the time.
- Alternative D: Moose Reduction and Wolf Assessment. Reduce the moose population by lethal and non-lethal means, such as hunting, contraception, or relocation. The goal would be to lower the moose density to a level that would allow vegetation recovery and assessing introducing wolves to the island in the future.

Here is the text of the public comment: '[INSERT COMMENT TEXT]'.

State which alternative the commenter is most likely to favor (A, B, C, D).
State if the comment is 'For', 'Against', or 'Neutral' on wolf introductions.
State if the strength of the commenter's opinion on a scale from 'Extremely strong', 'Very strong', 'Strong', 'Somewhat strong', or 'Mild'.

Produce the output in json format like this:
{
"favored_alternative": "",
"wolf_opinion": "",
"opinion_strength": ""
}

ChatGPT 3.5 costs 0.002$ per 1000 tokens. We can use the OpenAI tokenizer to estimate the number of tokens constituting our input prompt.

Our input is 420 tokens. The output should be less than 50 tokens. So we can round to assume 500 tokens per query. So, it will cost us about $1 to process 1000 comments. Much cheaper than paying a human!

In the old days, you could pass a list of inputs into chatGPT ‘completions’ model all at once. This is no longer possible. Now, to use the ‘chat/completions’ API requires looping through each of the inputs and making individual requests. Unfortunately, the API often fails or hits the request rate limit. So, we need to be smart about staging and error handling with this larger loop. The structure of this loop is to define the prompt, wait 18 seconds to avoid the rate limit, run a tryCatch block to test if the API call fails, and if so, it skips to the next record and logs the records that the error occurred on, otherwise, parse the response and store the output in a file.

After getting initial responses, I also want to rerun 500 randomly selected comments in order to check chatGPT’s consistency. This is a critical part of using a generative model in quantitative analysis. I’ll talk more about this later.

Here’s the loop. It will take quite a while depending on your rate limit. I’d suggest either running it overnight or putting in on a remote server. Because we write each response out to file, there’s no problem if it fails. Just note the number of the last successful iteration (which will be printed to the screen) and start back up there.

set.seed(7097)

# Randomly select 500 records to resample
IDs_to_resample <- sample(unique(EIS_comments_deduplicated$ID), 500, replace = FALSE)
ID_list <- c(unique(EIS_comments_deduplicated$ID), IDs_to_resample)

# Create a vector to store failed IDs
failed_ids <- c()

ID_list <- Still_need_IDs

for (i in 1:length(ID_list)) {
  ID_number = ID_list[i]
  # Define the prompt
  prompt_content <- paste0( "Here is the text of the public comment: '", EIS_comments_deduplicated %>%
        filter(ID == ID_number) %>%
        .$Content,
      "'.
    State which alternative the commenter is most likely to favor (A, B, C, D).
State if the comment is 'For', 'Against', or 'Neutral' on wolf introductions.
State if the strength of the commenter's opinon on a scale from 'Extremely strong', 'Very strong', 'Strong', 'Somewhat strong', or 'Mild'.
Produce the output in json format like this:\n{\n\"favored_alternative\": \"\",\n\"wolf_opinion\": \"\",\n\"opinion_strength\": \"\"\n}"
    )
  
  # Initialize gpt_response
  gpt_response <- NULL
  
  # With my account, I can make 3 requests per minute. To avoid denied API calls, I add a 18 second pause in each loop.
  Sys.sleep(18)
  
  tryCatch({
    # Call GPT for a response
    gpt_response <- 
      POST(
        url = "https://api.openai.com/v1/chat/completions", 
        add_headers(Authorization = paste0("Bearer ", read_lines("../credentials/openai.key"))),
        content_type_json(),
        encode = "json",
        body = list(
          model = "gpt-3.5-turbo",
          messages = list(
            list(
              "role" = "system",
              "content" = "You are a federal employee tasked with reading the following comment submitted by a member of the public in response to the The Isle Royale National Park Moose-Wolf-Vegetation Management Plan/EIS. The Plan/EIS is a document that evaluates management alternatives for the moose and wolf populations on the island National Park land.
Management alternatives include:
- Alternative A: No Action. Continue the current management of letting nature take its course, without any intervention or manipulation of the moose or wolf populations or their habitats.
- Alternative B: Immediate Wolf Introduction. Introduce 20-30 wolves over a three-year period, starting as soon as possible to reduce the moose population and its impacts on vegetation.
- Alternative C: Wolf Introduction after Thresholds are Met. Introduce wolves if certain thresholds are met, such as the extirpation of wolves, the overabundance of moose, or the degradation of vegetation. The number and timing of wolf introductions would depend on the conditions at the time.
- Alternative D: Moose Reduction and Wolf Assessment. Reduce the moose population by lethal and non-lethal means, such as hunting, contraception, or relocation. The goal would be to lower the moose density to a level that would allow vegetation recovery and assessing introducing wolves to the island in the future."
            ),
            list(
              "role" = "user",
              "content" = prompt_content
            )
          )
        )
      )
    print(paste0("API call successful for ID: ", ID_number, ", index: ", i))
  }, error = function(e) {
    # Handle API call errors
    cat("API call failed for ID: ", ID_number, ", index: ", i, "\n")
    failed_ids <- c(failed_ids, i)
  })
  
  # If the API call was successful, proceed with data wrangling and output
  if (!is.null(gpt_response)) {
    # parse the response object as JSON
    content <- content(gpt_response, as = "parsed")
    
    # Assign the ID to the GPT response
    gpt_response_df <- data.frame(
      response_id = ID_number,
      gpt_response = content$choices[[1]]$message$content
    )
    
    # Convert the JSON to a dataframe and join to the record data
    output <- bind_cols( EIS_comments_deduplicated %>%
        filter(ID == ID_number),
      fromJSON(gpt_response_df$gpt_response) %>% 
        as.data.frame()
    ) %>%
      mutate(response_created_time = Sys.time())
    
    # Append the data to the extant records and write the output to a file. (This is a bit less memory efficient to do this within the loop, but I )
    if (!file.exists("./EIS_GPT_responses.csv")) {
      write.csv(output, "./EIS_GPT_responses.csv", row.names = FALSE)
    } else {
      read.csv("./EIS_GPT_responses.csv") %>%
        mutate(across(everything(), as.character)) %>%
        bind_rows(output %>%
                    mutate(across(everything(), as.character))
        ) %>%
        write.csv("./EIS_GPT_responses.csv", row.names = FALSE)
    }
    
    print(paste0("Completed response ", i))
  }
}

# Log the failed IDs to a file
if (length(failed_ids) > 0) {
  write.csv(data.frame(ID = failed_ids), "./failed_ids.csv", row.names = FALSE)
  cat("Failed IDs logged to 'failed_ids.csv'\n")
}

ChatGPT is nondeterministic, so your responses will differ. You can download the responses I got to follow along.

download.file(
     url = "https://www.azandisresearch.com/wp-content/uploads/2023/09/Final_GPT_Responses.csv",
     destfile = "./data/GPT_output.csv"
)

GPT_output <- read.csv("./data/GPT_output.csv")

GPT_output %>% glimpse()
Rows: 2,470
Columns: 13
$ ID                    <int> 93, 440, 2164, 636, 839, 2335, 36, 487, 1268, 2303, 1781, 60, 1033, 1948, 1826, 1538, 1685, 308, 22…
$ Received              <chr> "7/29/2015 9:09", "8/9/2015 5:14", "8/27/2015 14:36", "8/18/2015", "8/25/2015", "8/28/2015 12:30", …
$ Correspondence        <chr> "Web Form Correspondence", "Web Form Correspondence", "Web Form Correspondence", "Web Form Correspo…
$ Content               <chr> "\"100% o wolves examined since 1994...have spinal anomalies.\"- -Of the six alternatives put forth…
$ form_duplicate        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Content_dup           <chr> "\"100% o wolves examined since 1994...have spinal anomalies.\"- -Of the six alternatives put forth…
$ favored_alternative   <chr> "C", "C", "Alternative D", "C", "C", "B", "C", "C", "D", "C", "Unknown", "C", "B", "A", "B", "A", "…
$ wolf_opinion          <chr> "For", "Against", "Neutral", "For", "Neutral", "For", "For", "For", "Against", "For", "Neutral", "F…
$ opinion_strength      <chr> "Very strong", "Very strong", "Strong", "Strong", "Somewhat strong", "Very strong", "Strong", "Stro…
$ response_created_time <chr> "32:19.2", "33:11.7", "33:16.9", "33:19.5", "34:35.2", "34:54.2", "34:55.4", "36:15.1", "36:16.3", …
$ Favored_alternative   <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Wolf_opinion          <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Opinion_strength      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…

A couple of interesting things to note here. First, I apparently was not specific enough in my instructions for classifying the favored alternative because chatGPT sometimes returns “Alternative B” instead of just “B”. This is one of the struggles with using chatGPT, a generative model, in this way. It strays from instructions just like human survey respondents when inputting free-text results. For example, common responses to the survey question, “How are you feeling on a scale from 1 (bad) to 10 (good)?” might be “I’m good” or “Okay” or “nine” or “0”. None of those answers fit the instructions, so we have to clean them up.

In the case of chatGPT, we might be able to reduce these errors with more specific prompt engineering. For now, we’ll just clean up the responses on the backend.

# Fix erroneous column names
GPT_output <-
     GPT_output %>%
     mutate(
          favored_alternative = ifelse(is.na(favored_alternative), Favored_alternative, favored_alternative),
          wolf_opinion = ifelse(is.na(wolf_opinion), Wolf_opinion, wolf_opinion),
          opinion_strength = ifelse(is.na(opinion_strength), Opinion_strength, opinion_strength)
          ) %>%
     select(
          -Wolf_opinion,
          -Favored_alternative,
          -Opinion_strength
     )

# There are probably more elegant ways to write generalized rules to classify these reponses, but this does the trick
GPT_output <-
     GPT_output %>%
     # Fix 'favored alternative' responses
     mutate(
          favored_alternative_edit = case_when(
               (grepl(" and ", favored_alternative) | grepl(" or ", favored_alternative) | grepl("/", favored_alternative) | grepl("&", favored_alternative) | favored_alternative == "B, D") & !grepl(" and Wolf ", favored_alternative) & !grepl("N/A", favored_alternative) ~ "Multiple",
               grepl("\\bAlternative A\\b", favored_alternative) | favored_alternative %in% c("A", "No Action (A)") ~ "A",
               grepl("\\bAlternative B\\b", favored_alternative) | favored_alternative == "B" ~ "B",
               grepl("\\bAlternative C\\b", favored_alternative) | favored_alternative %in% c("C", "Concept C") ~ "C",
               grepl("\\bAlternative D\\b", favored_alternative) | favored_alternative == "D" ~ "D",
               TRUE ~ "Other"
          )
     ) %>%
     # Fix 'opinion strength' responses
     mutate(opinion_strength = tolower(opinion_strength)) %>%
     mutate(
          opinion_strength_edit = case_when(
               opinion_strength %in% c("strong", "very strong", "mild", "somewhat strong", "extremely strong") ~ opinion_strength,
               TRUE ~ "other"
          )
     ) %>%
     # Fix 'wolf opinion' responses
     mutate(wolf_opinion = tolower(wolf_opinion)) %>%
     mutate(
          wolf_opinion_edit = case_when(
          wolf_opinion %in% c("for", "against", "neutral") ~ wolf_opinion,
          TRUE ~ "other"
          )
     )

Let’s take a look at the results.

We can see that the majority of comments favor Alternative B: immediate wolf introduction. However, if we exclude the duplicated comments, our conclusion shifts to a majority in favor of the more moderate Alternative C: introduce wolves only after certain thresholds are met. Almost no one supports Alternative D: moose reduction and wolf assessment.

Comments that favored Alternative A were stronger proportionally. Alternative B supporters had mostly strong opinions but very few extremely strong or mild opinions. Supporters of Alternatives C and D were the least opinionated.

Validating chatGPT responses

It is worth asking ourselves how reliable chatGPT is at classifying these responses. One way to test this is to rerun a subset of comments, like we did above and check for agreement. This is called inter-rater reliability* (IRR).

* Although, maybe it should be called intra-rater reliability in this case. I guess it depends on out definition of ‘individual’ with LLM queries, but that’s a very philosophical bag of worms!

First, we need to subset our dataset to the responses that we scored twice.

IRR_comparisons <- 
     GPT_output %>%
     group_by(ID) %>%
     arrange(response_created_time) %>%
     mutate(ID_row_count = row_number()) %>%
     filter(ID_row_count <= 2) %>%
     mutate(n = n()) %>%
     filter(n > 1) %>%
     ungroup()

Then we can see how reliably the favored alternative was scored,

IRR_comparisons %>%
     select(ID, favored_alternative_edit, ID_row_count) %>%
     pivot_wider(
          id_cols = "ID",
          names_from = "ID_row_count",
          values_from = "favored_alternative_edit",
          names_prefix = "val"
     ) %>%
     group_by(val1 == val2) %>%
     tally() %>%
     mutate(
          total = sum(n),
          prop = n/total
     )
# A tibble: 2 × 4
  `val1 == val2`     n total  prop
  <lgl>          <int> <int> <dbl>
1 FALSE              2   500 0.004
2 TRUE             498   500 0.996

ChatGPT gave consistent responses in 498 out of 500 cases. That’s pretty good! Let’s look at the comments where it disagreed with itself.

IRR_comparisons %>%
     select(ID, favored_alternative_edit, ID_row_count) %>%
     pivot_wider(id_cols = "ID", names_from = "ID_row_count", values_from = "favored_alternative_edit", names_prefix = "val") %>%
     filter(val1 != val2)
# A tibble: 2 × 3
     ID val1  val2 
1   288 C     B    
2  1160 B     C    
 
EIS_comments_deduplicated %>%
     filter(ID == 288) %>%
     .$Content %>%
     cat()
There should be a balance between the wolf population and moose. When it is not balanced there is more harm than good done to the environment. Please introduce more wolves on this island instead of decreasing their population and this will keep the moose in check. Please add more wolves to contain the moose population. So many wolves are under attack in other states and decreasing their population is NOT the answer. It only creates more problems to the environment. There should be intense management of the wolf population to help it thrive and return the land back to it's natural state where there are enough moose and wolves. I think the public should be consulted as far as future plans for any culling. There should be intense management to monitor the effects of climate change as this will affect all aspects of wildlife and plant life on the island. I do not like the idea of a moose cull. I like the idea of introducing more wolves to the island so long as there is harmony with the existing wolves on the island. Maybe possibly try to introduce another type of animal that would be a good balance with the wolves and moose but only if it does not disrupt the balance and create new problems. Other states have adopted disastrous wolf culling plans that are only in the interests of farmers and ranchers. As the wolf population is dwindling, other problems will begin to develop as there is not a proper balance. Please keep wolves in mind and do your best to increase their population before it is too late and more animals will be needlessly killed without the proper balance of mother nature.> 
 
EIS_comments_deduplicated %>%
     filter(ID == 1160) %>%
     .$Content %>%
     cat()
I have heard both sides of this situation and I believe that new wolves should be introduced on Isle Royale. Climate change has made a large impact on the amount of ice that freezes in the Isle Royale region. Previously wolves from the mainland could cross the ice that formed and take up residence on the Isle. The ice hasn't been stable enough for these crossings in the last few years and the wolves are becoming inbred and dying off. If you will check a video that I have watched about the wolves being reintroduced to Yellowstone, you will see that the ecology of the region is benefited by the wolves being there. If enough wolves are transported to Isle Royale, the wolves will keep the moose in check and the ecology will improve. Allowing the pack to die off is really not a positive move. Introducing a new bloodline to the pack will help. I believe the wilderness designation of Isle Royale is a positive thing and that the wolves help to keep the ecosystem there in good order. Thank you for taking comments from the public.

In both cases, chatGPT vacillated between classifying the comment as favoring alternative B or C. Difference between those alternatives is admittedly nuanced. Both alternatives propose replacing wolves, the only difference is in the timing. In Alternative B, wolves would be introduced immediately and in Alternative C wolve would be introduced, “if certain thresholds are met, such as the extirpation of wolves, the overabundance of moose, or the degradation of vegetation. The number and timing of wolf introductions would depend on the conditions at the time.”

Both of the comments that made chatGPT disagree with itself focus on the environmental conditions that wolf introductions might remedy. However, these comments seems to presuppose that those conditions have been met and seem to suggest immediate introduction is necessary. So, I can see where chatGPT might have a hard time solidly classifying these comments.

Let’s also check the IRR for chatGPT’s classification of ‘opinion strength.’ Unlike the favored alternative, where most folks explicitly stated their preference, classifying the strength of an opinion is a far more subjective task.

IRR_comparisons %>%
     select(ID, opinion_strength_edit, ID_row_count) %>%
     pivot_wider(
          id_cols = "ID",
          names_from = "ID_row_count",
          values_from = "opinion_strength_edit",
          names_prefix = "val") %>%
     group_by(val1 == val2) %>%
     tally() %>%
     mutate(
          total = sum(n),
          prop = n/total
     )
# A tibble: 2 × 4
  `val1 == val2`     n total  prop
  <lgl>          <int> <int> <dbl>
1 FALSE              5   500  0.01
2 TRUE             495   500  0.99

ChatGPT disagreed with itself in 5 cases, but gave reliable classifications 99% of the time. That’s pretty good! However, just assessing binary disagreement or agreement isn’t a strong metric for this variable. A switch from “extremely strong” to “very strong” is less of an issue than a vacillating from “extremely strong” to “mild”.

Instead, we can use the Krippendorff’s Alpha. This metric provides a formal way to assess the the amount of inter-rater disagreement. There are multiple metrics that we could use, but Krippendorff’s Alpha is nice because it can generalize to any number of reviewers and can handle many types of disagreement (i.e. binary, ordinal, interval, categorical, etc.). Here’s a great post for understanding Krippendorff’s Alpha. We’ll use the irr package to estimate it.

library(irr)

The irr package needs the dataset in wide format matrix with one row per reviewer and each record (the package calls records ‘subjects’ because this metric is traditionally used in social science research) as a column. For this analysis, we’ll consider the first and second responses from chatGPT as individual reviewers. We also need to enforce the order of our opinion strength levels; otherwise, R will naturally order them alphabetically.

IRR_comparisons %>%
     mutate(opinion_strength_edit = fct_relevel(
          opinion_strength_edit,
          c(
               "other",
               "mild",
               "somewhat strong",
               "strong",
               "very strong",
               "extremely strong"
           )
     )) %>%
     select(
          ID,
          opinion_strength_edit,
          ID_row_count
     ) %>%
     pivot_wider(
          id_cols = "ID_row_count",
          names_from = "ID",
          values_from = "opinion_strength_edit",
          names_prefix = "ID_"
     ) %>%
     select(-ID_row_count) %>%
     as.matrix() %>%
     kripp.alpha(method = "ordinal")
  
Krippendorff's alpha

 Subjects = 500 
   Raters = 2 
    alpha = 0.996 

Krippendorff’s Alpha ranges from -1 to 1, where 1 means perfect concordance, 0 means random guesses among reviewers, and -1 is perfect negative concordance. At .996, we are pretty near perfect reliability.

For many datasets, there will be a lower degree of IRR. But, it is important to remember to interpret the alpha value in context. Perfect concordance may not be realistic, especially in highly subjective classifications. In most cases our goals is not perfect concordance, but simply greater reliability than we’d get if we hired a bunch of humans to do the annotating. Preliminary evidence seems to indicate that even version 3.5 of chatGPT is more reliable than humans (even domain experts!) in subjective classification tasks.

In most cases, you won’t have the resources to get human annotations for an entire dataset for comparison. Instead, you could 1.) get human annotations for a small subset, 2.) use a similar benchmark dataset, or 3.) spot-check responses yourself. If you choose to spot check, I’d suggest rerunning chatGPT multiple times (> 3) in order to estimate the variance in responses. High variance responses indicate especially difficult classifications that you should target for spot-checks. Another tip is to ask chatGPT to return it’s justification with each response. Ultimately, this process will help you diagnose problematic types of responses and enable you to engineer better prompts to deal with those edge cases.

The bottom line is that working with chatGPT is less like working with a model and more like working with human raters–and all of the validation tasks that entails.

Analysis with token embeddings

Up to this point, we’ve presupposed the classifications we wanted ChatGPT to identify in our data. But, what if we wanted to uncover hidden categories in the responses? Folks could advocate for the same Alternative but for different reasons. For example, among those who favor Alternative C, some might argue from the perspective of climate change and some from the perspective of moose populations.

We can use token embeddings to uncover hidden clusters of topics in our responses. Embeddings are the way that LLMs encode free text into numeric form.  Each token or ‘unit of language’ is numerically described as a position in multidimensional language space. This is a huge advantage over more traditional language clustering methods that simply count the occurrence of certain words. Embeddings retain the context of each token as it exists in the document.

As a toy example, the word “train” in these sentences: “I train a model”, “I train for a marathon”, “I rode the train”, “I’m on the Soul Train” could be described in two dimensions of more or less metaphorical and noun/verb. If we do this for all of the words in a document or chunk of text, we can then think of all the embeddings as a point cloud. Documents with highly overlapping point clouds are more similar that those that don’t overlap at all.

We call a different OpenAI model, text-embedding-ada-002, to return the embeddings. Unlike the chat model, we can pass all of the responses as a list in a single call, instead of looping through each response. This makes embeddings much faster and cheaper than using the chatGPT API.

Prior to embedding, I like to remove non-alpha numeric characters from the text.

# Clean up the text to remove non-alpha numeric characters
input_to_embed <- 
     EIS_comments_deduplicated %>%
     mutate(Content_cleaned = str_replace_all(Content, "[^[:alnum:]]", " "))

# Call OpenAI for the embeddings
embeddings_return <- 
     POST(
          "https://api.openai.com/v1/embeddings",
          add_headers(Authorization = paste0(
               "Bearer ", read_lines("../credentials/openai.key"))
          ),
          body = list(
               model = "text-embedding-ada-002",
               input = input_to_embed$Content_cleaned
               ),
          encode = "json"
     )

The returned object is a bit convoluted. We can use a bit of purrr and jsonlite to extract the embeddings.

# Extract the embeddings from the API return
embeddings_list <-
     embeddings_return %>%
     content(as = "text", encoding = "UTF-8") %>%
     fromJSON(flatten = TRUE) %>%
     pluck("data", "embedding")

Then add the embeddings back into the dataframe.

# Combine the embeddings with the original data
EIS_GPT_embeddings <- 
     EIS_comments_deduplicated %>%
     as_tibble() %>%
     mutate(
          embeddings = embeddings_list,
          ID = as.character(ID)
     ) %>%
     left_join(
# We need to get only the first instance of the GPT response data, which also included the repeated reliability test responses, to know which alternative the comment favors
          GPT_output %>%
               group_by(ID) %>%
               arrange(response_created_time) %>%
               mutate(ID_row_count = row_number()) %>%
               filter(ID_row_count == 1) %>%
               ungroup() %>%
     select(
          ID,
          favored_alternative_edit,
          opinion_strength_edit
          )
     )

Topical clustering from text embeddings

The problem is that those point clouds exist in extremely high dimensions. OpenAI’s text-embedding-ada-002 model returns 1536 dimensions. We need a method to reduce that complexity into something useful.

As mentioned, the embeddings allow us to see how comments relate in high-dimensional language space. We want to figure out where there are denser clusters of point clouds in that space which indicate common themes in the comments.

A couple of common ways to do this is to use a clustering algorithm (e.g. K-means) or dimension reduction (e.g. PCA). For this tutorial I want to use a bit of a hybrid approach called t-SNE (t-distributed Stochastic Neighbor Embedding) that will allow us to easily visualize the clusters of common comments which we can then explore.

We’ll use Rtsne package which requires that the data be in matrix form.

library(Rtsne)

# Rtsne requires the embeddings to be in matrix form, so we extract the lists of emdeddings from the dataframe and convert them to matrix form.
openai_embeddings_mat <-
     matrix(
          unlist(
               EIS_GPT_embeddings %>%
               .$embeddings
               ),
          ncol = 1536,
          byrow = TRUE
     )

# Estimate tSNE coordinates
set.seed(7267158)
tsne_embeddings <-
     Rtsne(
          openai_embeddings_mat,
          pca = TRUE,
          theta = 0.5,
          perplexity = 50,
          dims = 2,
          max_iter = 10000
     )

Determining the proper theta (i.e. learning rate) and perplexity (basically an estimate of how close points are in relation to the expected groupings) is more of an art than a science. This post does a great job of exploring choices for these parameters. By setting pca = TRUE in this case, we are first reducing the dimensionality to 50 principal components and then using tSNE to do the final reduction to two visual dimensions.

# Extract the tSNE coordinates and add them to the main dataset
EIS_GPT_embeddings <- 
     EIS_GPT_embeddings %>%
     mutate(
          tsne_dim1 = tsne_embeddings$Y[,1],
          tsne_dim2 = tsne_embeddings$Y[,2]
     )

# Visualize the tSNE plot
EIS_GPT_embeddings %>%
     ggplot(aes(x = tsne_dim1, y = tsne_dim2)) +
     geom_point(alpha = 0.5, pch = 16)

The first thing to note is that we are not seeing much discrete grouping of the points. This tells us that that the comments share a lot more in common across all comments than across local groups of comments. The second thing to notice is that despite the spread, we do see a handful of groups budding off along the periphery. In fact, one group in the bottom right is very distinct. It is important to remember that, unlike PCA, the axis dimensions in tSNE are meaningless. In fact, I’ll remove them from plot for the rest of the post. Position doesn’t matter in tSNE–only relative closeness.

At this point, we might want to manually delimit groups that we want to analyze further, like pulling out all of the comments from that cluster in the top left. To make this a bit easier, I’ve opted to cluster the two dimensional tSNE with hierarchical clustering. It is important to realize that this is purely a convenience for visualization. If we really wanted to use clustering to directly define groups (like hierarchical, KNN, etc.), it would make much more sense to cluster directly on the first 50 principle components.

tsne_embedding_clusters <- 
     hclust(
          dist(tsne_embeddings$Y), 
          method = "average"
     )

EIS_embeddings_clustered <-
     EIS_GPT_embeddings %>%
     mutate(
          cluster = cutree(tsne_embedding_clusters, 7)
)

Since we are clustering on tSNE dimensions where distance doesn’t really matter, deciding where to set our breakpoint is a personal choice. I’ve decided to use 7 clusters because it seemed a natural breakpoint and recovered the obvious clusters.

Text analysis of topical clusters

Now that we have putative clusters of topics, we can perform some classic natural language processing (NLP) to illuminate the themes of those topics. We’ll use tidytext for this task.

library(tidytext)

First, we need to get the data into a long, tidy format where each word in every comments is its own row. We’ll also remove common stop words that are predefined in the tidytext library. Then, we can calculate the term frequency-inverse document frequency (TF-IDF) for the clusters. TF-IDF is basically a measure of how common a word is within a cluster, after accounting for how common a given words is overall.

For example, if we take a look at the most common words in each cluster, it is unsurprising that “wolves”, “moose”, “isle” and “royale” dominate. (Although it is interesting that the top words for clusters 4 and 7 are “wilderness” and “management”… more on that later).

However, TF-IDF tells us about the relatively unique words that define a cluster of comments. Some clusters, like 1 and 2 have very even tf-idf distribution and the important words are mostly filler or nonsense words. This happens when clusters are saturated with common words and there is no strong theme producing uniquely important words. We could have guessed from the tSNE plot of the embeddings that the bulk of comments in the center of the plot would fall in this lexical no-man’s-land. But! Clusters 3, 4, 5, and 7 show promisingly skewed distributions.

Cluster 3 seems to orient towards a topic of animal welfare, with words like, “contraception”, “sterilization”, “lethal”, and “culls”. I suspect that these comments speak to folks’ concerned less about the wolf population or wilderness management, and more about the ethics of any proposed action involving animals. In a similar way, it looks like Cluster 7 is more concerned with the science and measurement behind the management decision and less about the decision itself with words like, “evaluating”, “approximately”, and “tools” with high uniqueness and “management” as the most common word overall. These topics would have been completely lost if we had stopped at categorizing favored alternatives.

Meanwhile cluster 4 appears to be squarely concerned with Wilderness issues. “Wilderness” and “nature” are the most common words in this cluster and “untrammeled” and “unmanipulated” are the most uniquely important words. We might expect that most of the comments that chatGPT categorizes as favoring alternative A will fall into cluster 4.

We can also take a look at how the clusters map onto the chatGPT categorizations.

Mappin the chatGPT categorized ‘favored alternative’ onto the tSNE coordinates, we can see that comments roughly sort by favored alternative. Cluster 6 is almost entirely defined by support for Alternative B – immediate wolf introduction. Cluster 4, which seemed to orient towards Wilderness values is mostly comprised of comments in support of Alternative A – no action.

Cluster 7 and Cluster 3, are mostly skewed to Alternative C – more monitoring, but exhibit very similar distributions. This might be a great example where even folks who tend to agree on the same Alternative, do so for different reasons–a pattern we would have totally missed without text analysis.

The remaining clusters which compose the bulk of the midland in the tSNE plot favor a mix of Alternatives.

Chain-of-density summarization

We can learn a lot from looking at common and important words and using our human judgement to piece together the topical theme of each cluster. Ideally, we would read all of the comments in a cluster to develop a topical summary. But that would take a long time. As an alternative, we can pass all of the comments in a given cluster to an LLM and have it summarize the theme.

Currently, only a handful of models support context windows large enough to digest the entirety of the comments in our clusters. Anthropic’s Claude2 has a context widow of up to 100k tokens (rough 75,00 words). Although, it isn’t quite as good at chatGPT 4. To get the most out of Claude2, we can use a special type of prompting developed for summarization called “chain-of-density”. Chain-of-density prompting forces the model to recurrently check it’s own output to maximize the density and quality of its summarization. Research shows that people tend to like the chain-of-density summaries even better than human-written summaries of new articles.

For demonstration, we’ll use chain-of-density prompting to summarize the theme of cluster 3. Here is the prompt that we will pass to Claude2:

"You will generate increasingly concise entity-dense summaries of the semicolon separated comments included below.

The comments were submitted by a member of the public in response to the The Isle Royale National Park Moose-Wolf-Vegetation Management Plan/EIS. The Plan/EIS is a document that evaluates management alternatives for the moose and wolf populations on the island National Park land.

Now that you know the context, here are the semicolon separated survey response:

[INSERT SEMICOLON SEPARATED COMMENTS]

Instructions: You will generate increasingly concise entity-dense summaries of the above semicolon separated comments. Repeat the following 2 steps 5 times.

Step 1: Identify 1-3 informative entities (delimited) from the comments which are missing from the previously generated summary.
Step 2: Write a new denser summary of identical length which covers every entity and detail from the previous summary plus the missing entities.

A missing entity is
- Relevant: to the main themes of the comments.
- Specific: descriptive yet concise (5 words or fewer).
- Novel: not in the previous summary.
- Faithful: present in the comments.
- Anywhere: located in the comments.

Guidelines:
- The first summary should be long (7 - 5 sentences, ~100 words), yet highly non-specific, containing little information beyond the entities marked as missing. Use overly verbose language and fillers (e.g., 'these comments discuss') to reach ~100 words.
- Make every word count. Rewrite the previous summary to improve flow and make space for additional entities.
- Make space with fusion, compression, and removal of uninformative phrases like 'these comments discuss'.
- The summaries should become highly dense and concise, yet self-contained, e.g., easily understood without having read the comments.
- Missing entities can appear anywhere in the new summary.
- Never drop entities from the previous summary. If space cannot be made, add fewer new entities.

Remember: Use the exact same number of words for each summary.

Answer in JSON. The JSON should be a list (length 5) of dictionaries whose keys are 'missing_entities' and 'denser_summary'."

I don’t have API access to Claude2, but unlike chatGPT, Claude2 accepts files as input. So, it is easy to output our prompt with the contained comments as a .txt file and submit it to Claude2 manually.

Claude2 gives us five increasingly dense summaries:

Summary 1: These comments provide a range of perspectives on how the National Park Service should manage the declining wolf population and increasing moose population on Isle Royale. There is discussion of various preliminary alternative concepts, including taking no action, reintroducing wolves once, maintaining both moose and wolves, reducing the moose population, and intensively managing the moose population. Many commenters express support for alternative C, which involves reintroducing and augmenting the wolf population on Isle Royale more than once if necessary. There is also frequent mention of the possibility of relocating problem wolves from elsewhere to the island. Overall, most commenters emphasize the importance of restoring the natural wolf/moose balance on Isle Royale through wolf reintroduction rather than intensively managing the moose population.

Summary 2: These comments provide perspectives on managing the declining Isle Royale wolf and increasing moose populations. Many support alternative C to reintroduce and augment wolves more than once if needed. Wolf relocation from the mainland is also mentioned. Most emphasize restoring the wolf/moose balance through wolf reintroduction rather than intensively managing moose. Climate change reducing ice bridges for wolf migration and the wilderness designation of Isle Royale are frequently discussed. Many oppose lethal moose culling, instead supporting moose contraception/sterilization. Overall there is a focus on restoring the natural predator-prey dynamic through wolf reintroduction while minimizing intensive human management due to the wilderness designation.

Summary 3: These comments discuss managing the declining Isle Royale wolf and increasing moose populations. Many support alternative C to reintroduce and augment wolves more than once if needed to restore the wolf/moose balance. Wolf relocation and genetics are often mentioned given reduced migration from climate change. The wilderness designation and opposition to lethal moose culling are frequently discussed, with support for contraception/sterilization. There is concern about moose overbrowsing without sufficient wolves. Some mention wolf tourism benefits. Overall there is emphasis on restoring the natural predator-prey dynamic and wolf genetics through reintroduction while minimizing intensive human management due to the wilderness designation.

Summary 4: These comments discuss managing the declining Isle Royale wolf and increasing moose populations, often supporting alternative C to reintroduce and augment wolves more than once to restore balance. Wolf relocation and genetics are frequently mentioned to compensate for reduced migration from climate change. Many oppose lethal moose culling, instead supporting contraception/sterilization, expressing concerns about moose overbrowsing and ticks without wolves. Some mention wolf tourism benefits and preserving the wilderness designation. There is emphasis on restoring balance through wolf reintroduction to limit moose impacts on vegetation while minimizing intensive human management.

Summary 5: These comments frequently support alternative C to reintroduce and augment Isle Royale wolves to restore wolf/moose balance and predator-prey dynamics, concerned about moose overbrowsing, ticks, and vegetation without sufficient wolves. Wolf relocation and genetics are often proposed to compensate for reduced migration from climate change. Many oppose lethal moose culling, noting wilderness values. Some mention continuing the iconic predator-prey research, wolf tourism benefits, wolf-livestock issues, and the value of public engagement. Overall there is emphasis on reestablishing a healthy Isle Royale wolf population through reintroduction to minimize intensive human management and moose impacts.

The final two summaries start to get at the nuance particular to this cluster. Summary 4 mentions, “Many oppose lethal moose culling, instead supporting contraception/sterilization”. Summary 5 mentions: “Many oppose lethal moose culling, noting wilderness values.” As expected, much of the summary is likely common across all comments. So, a human interpretation of the summaries is still necessary.

Conclusion

In this post, we explored using chatGPT, text embeddings, and Claude2 to analyze public comments on a complex environmental management decision. We learned methods to responsibly validate chatGPT output. While not perfect, chatGPT showed promising reliability at categorizing free-form opinions. The text embeddings allowed us to uncover hidden topical clusters among comments that traditional methods would have missed. Claude2’s long context window allowed us to further interpret the topical clusters. Together, these tools enabled a nuanced quantitative analysis of subjective text data that would be infeasible for a single human analyst to perform manually.

During the pandemic, my partner, Bayla, and I began taking daily walks down to Yale’s campus. We often noticed dead birds at the base of the glass walls that wrap the Yale School on Management building when we passed by.

Overlooked casualties of building all-glass buildings. Female and male Ruby-throated hummingbirds with broken beaks, killed by flying into @YaleSOM building. pic.twitter.com/Idl8q0mqWu

— A. Z. Andis Arietta, PhD (@azandisarietta) May 9, 2021

Because we both have working relationships with the Peabody Museum of Natural History, we began saving the bird specimens for the museum’s collection. Through that partnership, we learned that the School of Management building is one of the most lethal pieces of architecture on Yale Campus. We also met Viveca Morris at the Yale Law Ethics and Animals Program who had been helping to organize city-wide bird-strike data collections and spearheading a push to adopt bird-friendly building ordinances in New Haven.

Another @YaleSOM window strike casualty. Yellow warbler.#illustration #watercolour #watercolor #watercolorartist #birdsketch #birdart #deadbird #birdpainting #yellowwarbler #migratorybirds #warblersofinstagram #darkart #baylaart pic.twitter.com/QiX58rc9VX

— Bayla Arietta (@BaylaArt) May 26, 2020

One of the main barriers enacting mitigatory measures at the SOM building was that the lack of hard accounting of the total number of birds killed allowed the administrators of the building to downplay the problem. So, along with Viveca, we began a systematic survey of bird strikes at SOM. I’ll write more about that in a future post.

We also began thinking about the larger picture. How could we get more folks to recognize the magnitude of deaths due to thoughtless architecture? And how could we inspire folks to demand businesses, architects, and municipalities to adopt bird-friend design?

Bayla began painting some of the specimens we found. She posted a painting of five warblers we collected on a single day at SOM. The response was huge. That image seemed to have struck a chord. We realized that art could be a way to simultaneously introduce the topic and inspire emotions toward enacting change.

Bayla contacted Talon and Antler galleries in Portland, Oregon which feature some of our favorite contemporary artists and tend toward natural themes.

They agreed to let us curate a show with us. Over the next few months, Bayla contacted artists whose work fit the theme. In total, 62 artists contributed original pieces to the show titled, “Fractured Aviary”, which hung for the month of June 2022.

If you missed the show, you can see some of my favorites below:

HealingVeronica Steiner watercolor

CrashDarla Jackson plaster, clay, resin, acrylic

Window Strike IBayla Arietta watercolor, gouache, gold leaf

Window Strike IIBayla Arietta watercolor, gouache, gold leaf

Window Strike IIIBayla Arietta watercolor, gouache, gold leaf

The Last Song IIRebecca Reeves hand-beaded ceramic

Yallow WarblersVasilisa Romanenko

Regressive ReincarnationsTeagan White watercolor, gouache, charcoal

The WinoSteve Martinez acrylic, spray paint

In MemoriamMadison Erin Mayfieldgouache

Hollow BonesKatrina Haffner gouache, ink

Caecus VitrumErica Williams ink, gold foil on Bristol

Highway 21 MemorialJay Rasgorshek gouache

Silent DownVeronica Park screen print

Hothouse 3Josie Morway oil, enamel

FracturedBrin Levinson graphite

III of SwordsLauren Marx watercolor, ink, pencil

ImpactMiranda Brandon photo

Fractured Aviary: Ruby-throated Hummingbird-ThroatedHummingbird_04-webJane Kim Kim fabricated this amazing kaleidoscope of glass shards around her hummingbird painting. The dots on the outside represent window glazing patterns to prevent bird strikes.

Fractured Aviary: Ruby-throated HummingbirdKim fabricated this amazing kaleidoscope of glass shards around her hummingbird painting. The dots on the outside represent window glazing patterns to prevent bird strikes.

Broken SpellsJake Messing acrylic

I Am LostKate MccGwire feather

— I originally wrote this post for the Sitka Conservation Society‘s website in 2014. This trip was part of the Stika Community Wilderness Stewardship Project.

The day we headed out from Hoonah was like most days in Southeast Alaska. Grey clouds diffused the light and an almost imperceptible rain left everything damp. We were headed to the Inian Islands, a cluster of knobby isles on the western end of Icy Strait, just inside the entrance to Cross Sound where the Inside Passage meets the angry Pacific. Our trip held a dual mission: to conduct volunteer wilderness monitoring for the Forest Service and to gather traditional subsistence foods for the Hoonah locals on the trip: Owen James and Gordon Greenwald, our boat captains and wizened culture-bearers, two young men named Randy and Sam, and another adult volunteer, Kathy McCrobie.

The Inians along with two other large islands make up the Pleasant/Lemesurier/Inian Island Wilderness. The PLI Wilderness is one of 19 areas within the Tongass National Forest designated as Wilderness, the highest form of protection public lands can receive. The islands are also historic gathering and hunting grounds of the Huna Tlingit, the native tribe who call this section of northern Southeast Alaska home. Because the Inians are close to the open sea, they are rich with unique flora and fauna. A trip to these distant islands is an opportunity to collect delicacies not common in interior waters near the town of Hoonah. For instance, one of our subsistence targets was black seaweed, a species that thrives in the cold, wave-washed intertidal zone of the outer coast, but is rarely found more than a few miles into the Southeast archipelago.

The outside waters can be a harsh place in the summer and downright inhospitable in the winter. Although the Huna Tlingit are seasoned open ocean travelers and motorized skiffs make the 40-mile journey from the village of Hoonah to the islands much more manageable than a Tlingit canoe, it is still a sizable trip for locals. The same factors—difficult access and a short season—also make it difficult for the Forest Service Wilderness Rangers who are headquartered in Hoonah to access these areas that they are tasked with managing and protecting.

On the first day of our trip we arrived at the Inian Islands after a few hours of skiffing over unusually calm waters. Our first stop was at low tide on a rocky beach, the perfect habitat for Black Katy chitons, one of the traditional foods commonly called Gumboots which we hoped to return with.

The beach also looked like it could be a prime camping area, so while the rest of the crew flipped rocks and pried unsuspecting chitons from their hiding spots, I headed up the beach to look for recreational impacts. Monitoring impacts from visitors is one of the tasks the Forest Service has asked us to assist with. Wilderness areas are intended to preserve nature in its wildest state, but trash, campfire rings, and other signs of previous visitors detract from the wild character of these places. Also, once a site has been impacted, the trend is a downward slope to a trashed site. To prevent cumulative impact, we check known campsites and cleanup and naturalize any human traces we find. Fortunately, this site was in the same condition it’s probably been in since it was uncovered by the glacier, so I spent some time flipping rocks and adding to the gumboots collection.

As the tide neared its apex, Gordon pointed out a small rock island set apart from the larger Inian Islands. For generations, this rock had been the prize destination for Huna families. Set far from land and too small to support trees, the rock is the perfect nesting grounds for seabirds like gulls and cormorants. We had timed our trip perfectly to harvest the new eggs.

As we approached the rock in skiffs, Gordon and Owen explained the protocol: as the swell surges, he runs the skiff up to the rock, one person jumps off, and he pulls the bow away before the swell drops the boat onto the shore, then he resets and we try again for the next person to leap from the bow onto the island. Before they maneuvered the skiffs toward the rock, they carefully taught the boys the traditional method to appropriately harvest the eggs. If done in an ecologically responsible way, these practices will be able to continue forever. (Learn more about the regulations regarding egg collection by Alaskan Natives and locals for subsistence).

Sam was the first to make the jump. The birds immediately erupted in a cacophony of squawks and feathers. Randy and I traded apprehensive glances. I made an excuse that I needed to pack my camera gear in drybags before I could jump…really I just wanted one more chance to see how it was done. Randy landed an impressive leap, despite receiving a bootfull of water. I followed him up the rock.

Blankets of birds flapped above us. The few green tufts of grass made a stark contrast to the guano-bleached stone and the blue-grey sky and water. It took no time for Sam and Randy to collect plenty of eggs to share with family and elders back in Hoonah. With concentration, steady boat handling, and good timing, we all made it safely back aboard the skiffs.

As the day went on, I was impressed with the way Owen and Gordon pointed out new landmarks to the two young men. Every remark about a headland or bay included not only geographical references, but also historical, cultural, and subsistence context. That night, while we ate chowder made with local salmon, smoked octopus, and cockles, I reflected on the education Randy and Sam had inherited on this trip. I have no doubt that they were more interested in learning about hunting spots, edible shellfish, and traditional stories than they were about the Wilderness land designation of their home. But, I would like to think that by relating the cultural values and subsistence practices of the Inian Islands along with the Wilderness values that will continue to protect this place for those practices, they have a better chance of retaining a favorable perspective of public lands, too. In the end, the idea and values of Wilderness are stories, stories that must be repeated and retold to maintain their relevance. Gordon and Owen have endeavored to pass those stories to Hoonah youth. My esteem and thanks goes out to them for including the value of respect for public lands in the stories they tell.

Sunset over Icy StraitThe Inians along with two other large islands make up the Pleasant/Lemesurier/Inian Island Wilderness in Icy Strait, Alaska.

Collecting black seaweedThe Inian Islands are the perfect spot to collect black seaweed, a species that thrives in the cold, wave washed intertidal zone of the outercoast, but is rarely found more than a few miles into the Southeast archipelago.

The nesting islandFor generations, this small rock island within the Inian Islands has been a prize destination for Huna families seeking sea gull eggs.

Preparing for the leapAs we approached the rock in skis, Gordon and Owen explained the protocol: as the swell surges, we run the ski up to the rock, one person jumps

Leaping onto the islandAs we approached the rock in skis, Gordon and Owen explained the protocol: as the swell surges, we run the ski up to the rock, one person jumps

Searching for gull eggsI was impressed with the way Owen and Gordon pointed out new landmarks to the two young men. Every remark about a headland or bay included not only geographical references, but also historical, cultural, and subsistence context

Gull nest with eggThe traditional method to harvest gulls eggs, done in an ecologically responsible way, these practices will be able to continue forever

Some nests are difficult to reachFor generations, this rock had been the prize destination for Huna families. Set far from land and too small to support trees, the rock is the perfect nesting ground for seabirds like gulls

Harvesting gull eggsFor generations, this rock had been the prize destination for Huna families. Set far from land and too small to support trees, the rock is the perfect nesting ground for seabirds like gulls

Collecting gull eggsBlankets of birds flapped above us. The few green tufts of grass made a stark contrast to the guano-bleached stone and the blue-grey sky and water.

Bounty of the harvestIt took no time for Sam and Randy to collect plenty of eggs to share with family and elders back in Hoonah.

Harvesting gumbootsAfter the Gumboots have been collected, they are typically canned for preservation and storage.

Gumboot (Black Katy chiton)Black Katy chiton (Katharina tunicata) is a traditional food, commonly called Gumboots.

Black Katy chitonGumboots live in the intertidal zone, and are particularly susceptible to contamination from development and timber harvest. Wilderness designation ensures that these fragile ecosystems and the subsistence foods within will be protected in perpetuity.

Flipping rocks for gumbootsOwen instructs the students on the art of Gumboot hunting.

Our chapter tells the story of green frogs and the feral condition of their life with suburban human neighbors. We especially highlight the way that the human built-environment of lawns, pavement, sewers, and septic systems is infused into the biology of green frogs (a topic that Max and Dave have studied in depth). As a counter example, I told the story of the wood frog, a species that has escaped a feral fate by clinging to the remnants of wild space away from humans (a topic I study in depth).

I rarely get to write about wood frogs outside of academic articles, so it was a pleasure to contribute to this piece. I think it is some of my best natural history writing. I’ve excerpted my section below (or, read the chapter):

“To better understand why our housing patterns influence frogs, it is worth taking a frog’s-eye-view of suburbanization. Most frogs exhibit distinct life-stages. Like humans, frogs begin development as shell-less and fragile eggs, but while human embryos float within the protection of a womb, frog embryos are buoyed among the vegetation and flotsam of ponds. The embryos have an umbilical relationship to the water that surrounds them. Nutrients and oxygen easily pass through the transparent jelly and are consumed through delicately branching gills. Any contaminants in the water also suffuse the embryos.
Even before their eyes or mouths have formed, the developed embryos hatch as free-swimming larvae not much larger than a grain of rice. Hatchlings are vulnerable. Thus, frogs hedge their bets by producing hundreds of eggs per clutch, hoping that at least a few will win the lottery of life. Some species, like wood frogs, additionally safeguard their offspring by choosing impermanent pools that are devoid of fish as relatively safe nurseries.

Those hatchlings that survive develop into recognizable tadpoles with bulbous bodies and slender tails. A pond’s version of cows, tadpoles graze along the bottom with scraper-like teeth. They consume algae and detritus along with any solid matter that washes into the pond basin. A long digestive tract allows the tadpoles to incorporate nutrients into a growing body. Where ponds neighbor septic systems, this means that human waste makes up a prodigious portion of a tadpole’s body.

The transition from a tadpole to a frog is a remarkable change. It makes the squeaking voice and acne of human puberty seem like a blessing. Every system in the tadpole’s body transforms. The tail gives way to bony limbs. The narrow, disc-shaped mouth morphs into a wide, insect-capturing, gape. The goggle eyes, so fine-tuned to underwater vision, mutate into something much like our own. Even the long and coiled digestive tract shortens and distends. At the end of this metamorphosis, the aquatic vegetarian leaves the water’s edge and becomes a terrestrial carnivore.

Green frogs are parochial and prefer a pond-side life. For a short time as juveniles they might range far and occupy any standing water from lakes and ponds to swimming pools and tire ruts. Upon adulthood though, they settle along freshwater shores where they patiently wait for dragonflies and other insects to approach within range of a lunging gulp. Since green frogs inhabit permanent ponds, they can breed throughout the summer, and without the threat of the pond drying out from beneath them, their tadpoles can be leisurely in development. When snow falls and the pond freezes, both adults and overwintering tadpoles take refuge deep in the insulating layer of pond muck. Because a green frog’s life is so reliant on a pond, they can survive in just about any permanent water with at least a narrow perimeter of vegetation. As long as a homeowner neglects the tufts of grass along the bank, green frogs are more than happy to remain neighbors.

Unlike green frogs, wood frogs become sylvan nomads after metamorphosis. As their home ponds dry up in the summer and fall, they wander the forest floor hunting among the leaves, only briefly returning to recently filled pools in early spring to breed. During the winter months, wood frogs burrow just under the blanket of leaves dropped in fall. This enables them to be the first out of hibernation as the forest thaws in spring. For these reasons, wood frogs rely on leaf-littered landscapes. Manicured lawns where leaves are regularly raked and bagged make inhospitable places for them. Where the balance of forest gives way to lawns, wood frogs disappear…”

Overall, this was a really fun project to work on that gave me a chance to switch up my writing style. It was also a lot of fun to be able to collaborate with my partner, Bayla, who painted the featured image for the chapter.

The online version of the book is a little counter-intuitive to navigate (I think this was intentionally designed as a rhetorical device), but if you can figure it out, it is worth checking out some of the other cool stories of our feral world!

Rats have traveled around the globe alongside humans. Where we make our homes, they are happy to make their own, too. Unfortunately, they can make terrible neighbors, especially when they become vectors for diseases.

The rats that live in the slums of Salvador, are to blame for the widespread of emergence of leptospirosis infections that poses a serious health risk. In response, the municipality planned a multi-year rat-eradication project.

The bad news is that it is nearly impossible to fully eradicate rats from large territories. After just a short time, the populations tend to rebound, either because some rats were missed and then repopulate from within or because new rats migrate from outside and colonize the area. Understanding how rats repopulate after eradication efforts is important for deciding how best to proceed with future management strategies.

To test which of these scenarios was at play in Salvador, my collaborators trapped rats before, during, and after the eradication campaign in three contiguous geographic regions within the slums. We looked at the genetic relatedness of the populations across the three time-points. If the rebound populations were more similar to each other post-eradication, it would suggest that the populations were recolonized from source populations outside. However, if the regions became more genetically distinct post-eradication, it would suggest that the rebound populations were seeded from the few local genetic lineages that persisted.

We found that the populations showed distinct genetic differences immediately after the eradication effort, suggesting that remnant rats from the original populations had repopulated from within. Those difference persisted after 7 months.

In addition, we looked at the genetic signatures of population expansion and contraction. We found that post-eradication populations had pronounced reductions in genetic diversity. As animals were removed from the population during eradication efforts, it created a bottleneck. Since the new populations arose from the few remaining rats, only those few genetic variants left over persisted.

If enough genetic diversity is lost, and is not replaced by migrant mice from outside the original population, it might eventually lead to inbreeding and reduced fitness. If that is the case, it means that creating serial bottlenecks in the population through eradication efforts could be an effective way to weaken the rat populations to manageable levels, even if the overall population number rebounds in the short term.

I think my collaborator Dr. Jonathan Richardson (@JRichardson_44) did a nice job of succinctly outlining our findings in this Twitter thread:

I'm very excited about our new paper out today on the significant genetic impacts that accompany an urban eradication campaign of rats in 3 areas of Salvador, Brazil, w/ @georgisil_ & many Twitterless colleagues. The TL,DR version is in short thread below:https://t.co/WLqmQ1UJkD

— Jonathan Richardson (@JRichardson_44) June 6, 2019

I was only able to stay for the day of my talk and missed the award ceremony on the second day. So it was quite the surprise when I visited the Conference’s website to register for this year and saw that I had won an award for the best speed talk!

Check out the video of my talk below:

Or follow this link to the video.

As an ecologist (with a special interest in freshwater ecology), I felt incredibly fortunate to experience the new face of this watercourse which had been radically changed by a “High Flow Experiment” just a moth earlier. Also, I was out-of-my-mind excited to see some of the first wild-fledged California condors in Arizona while on the river. So, in addition to lots of photos from the trip, the end of this post contains a fair bit of nerd-splaining about river ecology and the condor recovery program.

This trip was especially important to me because it brought my career as an environmentalist full circle.

At the beginning of May 2007, I was in a 13-passenger van towing a trailer full of gear headed from northern Wisconsin to Arizona. At the helm was an English professor named Alan Brew. He was carting us out to the desert to spend a month reading the works of Edward Abbey while visiting all of the places where “Cactus” Ed had put those words to paper.

The discussions we had around the fire, during breathless hikes, and among sandy van seats helped form the bedrock of my wilderness ethics. And in no small way, that trip helped impel my career as a conservationist. To this day, when someone asks what I do for a living, my first impulse is to respond with a quote from one of Abbey’s books:

“My job is to save the fucking wilderness. I don’t know anything else worth saving.”

The most influential of Abbey’s books, for me, was his novel The Monkey Wrench Gang. It tells the story of a small posse of unlikely activists who refine the art of environmental sabotage (“monkey wrenching”) in escapades across the American West. The book culminates in the posse’s attempt to blow up the pinnacle of their ire–the Glen Canyon Dam, the dam that plugged the Colorado River just upstream of the Grand Canyon.

As it did for many generations before me, The Monkey Wrench Gang served as a distillation of all my frustration with the environmental catastrophe of modern society (the books was an inspiration for the first-wave of environmental activist organizations like Earth First! and later, for eco-terrorist groups like Earth Liberation Front). Above all else, the lesson I took from TMWG was that the Glen Canyon Dam represents the domestication of the willy Colorado River. It is a metaphor for everything wrong with our approach to natural systems. And I wanted nothing more than to see the fucker demolished.

Now, over a decade later, I got the chance to float the neutered stretch of the river between Glen Canyon Dam and the bolus of water backed up by the Hoover Dam downstream.

We put in just under Glen Canyon Dam on December 14 under a waxing moon and pulled out at Lee’s Ferry on Lake Mead 17 days later feeling (and smelling) like whole new people.

The High-flow Experiment:

So, why are dams so bad for river ecosystems?

River ecosystems, and especially desert riverine systems, are tough places for organisms to exist. Only specialized species can cut it here. River banks are constantly flooded and then parched. Entire sections of banks are washed away in high-flows and redeposited elsewhere downstream. During storms, the river turns turbulent and chocolate brown with suspended sediment, yet during the summer, the water can be still as glass, warm, and oxygen-starved.

Despite the hardships, or rather because of them, organisms have evolved to synchronize with the seasonal fluctuations of the river. Trees rely on bankful rivers to deposit seeds. Insects rely on high-flows to blast the silt out of the stony crevices they call home. Even frogs rely on the high pools left by receding waters as nurseries for tadpoles.

Dams stifle the natural flows of rivers and dampen natural flood events. Instead of seasonal peaks of silt-rich waters, dams reduce the river to a steady, nutrient-saturated but silt-poor trickle. In general, the entire system is engineered for the annual catastrophism of raging floods. Without that annual reset, a few species can come to dominate at the expense of overall biodiversity.

In order to restore down-stream ecosystems, researchers have begun to experiment with creating artificial floods by releasing high-volume surges from the bottom of dams. Releasing from the bottom of the dam purges the sediment build up behind the wall and flushes it downstream.

At Glen Canyon, the first High-Flow Experiment (HFE) was conducted in 1996. Follow-up trials were conducted in 2004 and 2008, and then annually since 2012. During an HFE, flows peak at between 38,000 and 45,000 cfs–about double the average base-flow.

While HFEs help simulate minor floods, they do not replicate natural flow patterns. Take a look at the flow plot from 1920-2010. Before Glen Canyon Dam, annual floods peaked over 100,000 cfs every five years or so, at times exceeding 150,000 cfs, which is double or triple the peaks of HFEs.

The HFE program is certainly better for the river than continual flows, but probably will not restore long-term ecological processes. For instance, during our trip, those folks who had been on the river in years past were surprised to see how some of the camping beaches had changed. Ecologically, that’s a good thing, because it establishes a natural successional regime. However, the changes were nominal and limited to the immediate banks while the upper beaches were unaffected. One reason for this is that a history of consistent flows allowed woody vegetation to develop deep roots that anchor the upper beaches in place. Historically, annual floods would have prevented most plants from this type of entrenched colonization, and the irregular massive floods would have removed those plants that had begun to establish themselves. The result is that a post-succession regime currently dominates most beaches, choking out important ephemeral habitats, and every year they grow more recalcitrant to floods.

California condors:

Up close, condors are ugly birds. They kind of look like Mitch McConnell wearing an Ewok suit. But when you see them gliding along the elevated skyline of the Grand Canyon’s rim, they seem both majestic and imposing. With a wingspan of 9.5 feet, they are the largest bird in North America.

The story of the condors is one of the greatest successes of the environmental movement. Due mainly to lead poisoning, the California condor population crashed to just 22 individuals in the entire world in 1982. Condors are carrion specialists and especially like the large carcasses left by hunters. Unfortunately, the lead shot in bullets remains in the carrion and ends up in the birds. Over the years, the populations dwindled as more and more birds died of lead toxicity before reproducing. Without intervention, the species would have been extinct by the end of the century.

Over the next 5 years, the Fish and Wildlife Service managed to capture all of the remaining birds and placed them in a captive breeding program. By 1992, the captive population had tripled and the first new birds were released back into the wild. Over the next couple of decade, the captive population grew and new captive breeding programs were established. At the same time, new individuals were released and new reintroduction sites were established, including the second release site in Arizona.

We saw condors on the first day of our trip, just a couple of miles downstream of the dam. Judging from the broken white bands under the wings, these were probably juveniles. That’s super exciting because it means that these were wild-fledged birds from released parents. As of 2017, there were over 60 wild-fledged condors and that number keeps rising.

The future of the condors looks hopeful. In 2011, for the first time since the recovery effort began, the number of condors in the wild exceeded the number in captivity–290 to 170 as of 2017.

But there remains cause for concern. Lead poisoning continues to impact the wild populations. In fact, USFWS expects that every wild bird will need to be treated for lead toxicity at some point in its lifetime and lead toxicity can be blamed for the majority of deaths. Finkelstein et al. (2012) used isotopic analysis to trace the origin of the lead found in birds—no surprise—the predominant source of lead was from ammunition.

Despite the fact that we’ve known for decades that lead causes major harm, no legislation has been passed to ban lead in bullets. We can continue to supplement wild populations with captive breeding programs, but until we manage to fix the problem of lead in the environment, condors will never be self-sustaining.

Fortunately, a lead ammunition ban is supposed to go into effect in California this year. However, populations in Arizona, Utah, and Mexico will continue to suffer from poor hunting practices.

Resources and references:

U.S. Fish and Wildlife – California Condor Recovery Program
National Park Service – Condor Re-introduction & Recovery Program

Glen Canyon Dam Adaptive Management Wiki

Finkelstein, M. E., Doak, D. F., George, D., Burnett, J., Brandt, J., Church, M., et al. (2012). Lead poisoning and the deceptive recovery of the critically endangered California condor. Proc. Natl. Acad. Sci. U. S. A. 109, 11449–11454.

Poff, N. L., and Schmidt, J. C. (2016). How dams can go with the flow. Science 353, 1099–1100.

West, C. J., Wolfe, J. D., Wiegardt, A., and Williams-Claussen, T. (2017). Feasibility of California Condor recovery in northern California, USA: Contaminants in surrogate Turkey Vultures and Common Ravens. doi:10.1650/CONDOR-17-48.1.

I’m currently on my way up to Alaska for another supremely short season of guiding (just two trips this years). I was going through some old photos and came across this image of the Milky Way from a trip back in 2014. It evoked a memory of the last time I paddled with Ken Leghorn in Windfall Harbor.

Ken Leghorn was a hero of the Alaskan conservation movement and a friend and mentor of mine who passed away a little over a year ago. I wrote down this recollection on the airline napkin:

The night was dead still under the stars as we scraped the final bites from our dishes and made the slippery pilgrimage over the popweed to wash plates at the waterline. As we cast our rinse water out, the splash excited thousands of tiny green sparks in the wake. Bioluminescent algae had flooded into Windfall Harbor with the rising tide and now the bay was dense with the tiny flashing organisms. Ken and I decided it was definitely worth the effort of pulling a tandem down from the woods. We slid off into the black indefinite water. Every paddle stroke lit up like an aquatic Christmas tree. We stopped paddling not far from shore and floated. As the hull lost momentum it ceased to perturb the algae. Now the water was a black mirror of the star-full sky. Between the silence, Ken and I traded similes: Our kayak was like a space ship floating in space. Our wake was like a ripple in space-time. The Alexander Archipelago was like a solar system hurtling through the universe and we were a satellite in orbit around a tiny island planet.

We paddle back and pulled the kayak back up into the treeline. Knotting the bowline, we agreed it was the best bioluminescence we’d ever seen in Southeast.

Since I shuttered my photoblog a few months ago, I realized that my original post from that trip to Windfall Harbor had been lost to the ether. So, I resurrected the photos and lightly edited that post below.

From August 2014:

There are only a few Wilderness areas in Southeast Alaska that I have not been to. Surprisingly, Admiralty Island/Kootznoowoo Wilderness, one of the larger Wildernesses in the Tongass is one that I had never visited. Along with Baranof Island and Chichagof Island, Admiralty Island has one of the highest concentrations of brown bears in the world. The average is one bear per square mile. In total, that means that the bears outnumber the people on these large islands. In fact, Admiralty itself has more bears on it than all of the lower 48 states combined.

Pack Creek is a special place for bears. It is a wildlife sanctuary in addition to its Wilderness designation. That means that there is no hunting of bears at Pack and the viewing at the Creek is strictly regulated. This is a great set up for bear viewing, as bears get much closer than would be normally comfortable. We arrived late in the season, well after the tourists, so we basically had the place to ourselves.

Many thanks to friend Ken Leghorn and Pack Creek Bear Tours for loaning us a kayak, sharing salmon dinner, and providing super helpfully detailed info about Pack. If you ever want to make the trip yourself. Pack Creek Bear Tours are the folks to call.

The inspiration for this trip was a visit from one of my best friends from middle school, Jordan, who came up to visit Alaska for the first time. After years hearing about the incredible bear viewing at Pack Creek, this seemed like the best excuse to spend a few days there. We boarded the float plane in Juneau and made the short flight to Windfall Harbor where the Forest Service maintains a small seasonal camp for their rangers on an island just a stone’s throw from the Creek. This is also where Pack Creek Outfitters store their kayaks. It was the end of the season, so Ken offered to let us use a kayak for a few days if we would help him move his fleet to the winter storage area.

The operation at Pack Creek is nothing like any other bear viewing site. There is no platform, no fences, no barriers. The viewing area is a 5 by 10 meter area of mown grass with a driftlog to sit on. The Forest Service and Fish and Game rangers are on-site at all times that people are present. They are trained to let the bears move about freely up to the edge of the mown grass line.

The unique situation at Pack Creek is a stamp of its history. In the 1930s a major conservation campaign sprouted with the intent of designating all of Admiralty Island a bear refuge, but succeeded only in protecting the Pack Creek drainage from hunting. In 1935, the Forest Service designated it an official bear viewing. Despite the restrictions, poaching was regular in the remote watershed. In 1956, a local miner and logger, Stan Price, rowed his floating cabin on shore at the mouth of Pack Creek and established a homestead with his wife, Edna. Rather than fear, they treated the local bruins as neighbors. Their presence helped to curtail poaching and also attracted new visitors. For almost 4 decades, the Prices lived with the bears. Over that time, new generations of cubs were born and reared with the Prices as a normal fixture of life. By the time Price died in 1989, just about every local bear was habituated to constant human presence.

In 1984, the tiny sliver of bear sactuary was expanded to a no-hunting zone encompassing Pack Creek and the adjacent watersheds, as well as the islands in Windfall Harbor. As the 80s progressed visitation increased to the point that the agencies decided to actively manage the area. Viewing times were limited, rangers were installed on-site, and visitation was limited to just 24 people per day.

As a result, generations of bears have come to associate Pack Creek as a safe haven from hunting and to ignore the small groups of human onlookers.

Sitting on a log, surrounded by Alaskan brown bears playing, snoozing, bathing, and snapping at salmon is a mesmerizing experience. We spent most of our time sitting on the log at the Creek mouth or walking up the trail to the viewing platform. But we managed a couple paddles around the Harbor, including a visit to the most impressive Sitka Spruce tree I’ve ever met.

On our second night, we sat under the clear night sky and discovered bioluminescent algae in the water. It is rare to see stars in Souheast Alaska. And it is a pleasure to see them reflected in the still waters. It is utterly, chest-caving, breathtaking to paddle the myth-like firmament of water sandwiched between a sky of stars above and swirls of bioluminesces below. Ken and I paddled out in a tandem just to sit and float. I can’t describe it. It was one of those utterly unique experiences that will forever bound my conception of hyperbole.

On the final evening of our visit, Jordan and I sat on the log with my friend Daven who happened to be the Forest Service Ranger on staff for the day.

The three of us sat in silence for most of the evening, occasionally swatting mosquitos, surveying the moldering ruins of Stan Price’s cabin, and potting bears across the river. With the sun dropping behind the mountains, we were contemplating packing up for the evening when a medium-sized rich-chocolate colored bear sauntered out of the trees. Daven recognized her immediately as Chino (her mother, a creamy brown bear, was named Mocha… get it?). Chino ambled across the streamlets and with no attention to us, came to rest in the tall grass at the edge of the viewing area. We were stunned into silence. I frantically switched lenses since she was closer than the focusing distance of my long lens and filled cards with her portrait.

I’ve seen many, many bears at very close range. But the general protocol for bear encounters is to make your presence known with the goal making it clear to the bear that you want your space. At Pack Creek, the tone is completely different, the intent is to discharge any discomfort, to let Chino forget we were even there. I learned that nonchalance is a powerful emotion when seen in the eyes of a bear.

We flew out on a clear day with Ken. Upon take off, we circled over the Swan River estuary which was expansive at low tide. The afternoon sun fluoresced the rivulets like veins under an X-ray. Out on the flats, we passed over a sow and two cubs. It takes a big landscape to make a 900lbs animal look like a speck, and it takes an even larger Wilderness area to ensure that such a landscape remains truly wild.

Overview:

Isle Royale is an island in Lake Superior that is designated as a Wilderness Area and managed by the National Park Service. In the 20th century, wolves and moose migrated to the island and their dynamic spurred one of the longest predator-prey studies in history. Now, the wolf population has dropped to 2 and the Park Service is planning a major intervention that will install an entirely new, synthetic population of wolves on the island. This plan is the result of myopic research perspective and disregard for eco-evolutionary dynamics. It is bad policy and even worse science. Here’s why:

Background:

Isle Royal is a smallish-island (just over 200 square miles) that sits about 8 miles from the north shore of Lake Superior. (Although it is small, it is large enough to host its own internal lake with an island, making it, as upper-midwesterners are fond of point out, the largest island on the largest lake on the largest island on the largest lake in the world.) The Isle and its many tiny satellite islands became a National Park in 1940 and were designated as a federal Wilderness Area in 1976.

Because the island is small and a long swim from the mainland, large fauna populations have been inconsistent denizens, historically. Moose first arrived on the island in the early 1900s. Wolves followed the moose in the 1940s, adding two major trophic levels to the island ecosystem. The complex predator-prey interactions became one of the classic test cases of ecological theory (see Peterson et al. 1984; McLaren & Peterson 1994).

Over the decades, the wolf and moose populations have demonstrated a standard predator-prey oscillation, with the wolves generally bouncing around about 20 individuals, but reaching a population maximum of 50 individuals in the 1980.

However, Isle Royale is a small place. Small islands are more susceptible to tipping points on the roller-coaster of demographic stochasticity. It’s kind of like drunkenly walking along the centerline of a bridge versus drunkenly walking a tightrope. If you stumble off course too far on the bridge, you have the latitude to recover and get back on course. Too big a waiver on a tightrope and you’re done for. The small size and isolation of Isle Royale makes it a tight rope for large predators. Like all oscillatory ecological patterns, what goes up eventually comes down, and in the last decade or so, the wolf population has declined in a mirror-like inversion of the population boom in the 1980s. As of this year, there are only two wolves left. As per the dynamics of island-biogeography, the natural course looks like the rein of the wolf will eclipse on the island, probably followed by a boom and eventually extirpation of moose, and the island will continue along as it did for the many decades prior to the most recent immigration events. That is, until the next colonists arrive, as has happened multiple times in the past. Coyotes immigrated and blinked out in 50 years in the early to mid-1900s. At times, lynx and caribou both made the pilgrimage to the island and subsequently slipped off the tightrope.

The issue:

Now, the National Park Service has released an Environmental Impact Statement (EIS) for a plan to install a new populations of wolves on the island (available here). If you are unfamiliar with the NEPA process, here’s how it works: When a land management agency like the National Park Service wants to embark on a project that might run counter to its mandate and/or result in large impacts, they are required to vet all potential options, usually as an EIS, and ask for the public’s comments on the plan. After the revision process, they make a final decision to enact one of those potential options, the “preferred alternative.”

Since 99% of  Isle Royale Park is a designated Wilderness, “where the earth and its community of life are untrammeled by man” and “generally appears to have been affected primarily by the forces of nature, with the imprint of man’s work substantially unnoticeable” (Wilderness Act, 1964), shipping in a boatload of wolves to manipulate the ecosystem required an EIS.

As a scientist and especially as an ecologist, I tend to view Wilderness Areas as our most critical ‘controls’ or ‘baselines’ for science to contrast other areas where human impact alters systems. Though every system is touched by human impact to some extent, there is huge value in preserving the least impacted places in an unmanipulated state. As an analogy, a blemished diamond might be worth a little less than a perfect diamond, but that doesn’t reduce it to equal value with a lump of coal.

But, not all scientists think that way.

To introduce or not to introduce:

The push to introduce wolves to Isle Royale has been championed primarily by two researchers at Michigan Tech, John Vucetich and Rolf Peterson, whose careers are rooted in the Isle’s wolf-moose study.

I first heard about this proposal when Vucetich gave a presentation at the Sigurd Olsen Environmental Institute. At the time, it wasn’t the science that bothered me about the presentation–it was the patent misrepresentation and obvious straw-man Vucetich employed to characterize the intention of the Wilderness Act. Since then, these researchers have made major pushes in film (and this one) and popular press to portray wolves as an ever-present and integral part of the Isle Royale ecosystem, and pit the “health” of the ecosystem against what they believe is an outdated philosophy of conservation.

Essentially, their argument is that since climate change impacts the whole globe, no wilderness is really free of human manipulation, so we should be free to further manipulate it to our own design. They explicitly argue for “new visions for the meaning of wilderness,” with their preferred vision being “a place where concern for ecosystem health is paramount, even if human action is required to maintain it” (from here).

Intentionally or not, their use of relativistic “ecosystem health” rhetoric and attempts to stretch the ‘wilderness myth’ concept into their own application has thoroughly muddied the debate.

And it’s resulted in a lot of public confusion on the topic. For instance, here’s one comment I pulled from the public response in the EIS:

I have visited Isle Royale twice and it remains one of my favorite places in the world. The wolves and the moose have become a part of the island and that is a good thing. Wolves and moose aren’t faring well on the mainland due to politics ignorance and climate change. Isle Royale remains a unique microcosm where we can still observe and study this ancient predator-prey relationship. In a world where species are becoming extinct on a daily basis, this rare relationship has endured and that should be given a lot of weight when making the decision of what to do about the wolf-moose problem on Isle Royale. Please use common sense and act sooner rather than when it is too late.

First off, it’s not an ancient relationship (it’s only been going on for 60 years on the island), and it’s not a rare relationship (wolves eat moose all over the continent all the time). What makes it “rare” is the fact that it happens without human intervention (at least until NPS takes control of the population) on an isolated island with researchers tracking every move.

This person’s comment shows that opinions on the wolf issue are completely colored by human perception: i.e. anything that happened before your lifetime is “ancient,” anything that looks the way it is when you first saw it is “natural,” if you’ve only heard about something in one place, it must be “rare,” etc. The most pernicious perception is that the only species that are worth concerning ourselves with are the big ones with faces that you can relate to (after all, amphibian populations fluctuate on and off in ponds all over the upper midwest following the exact biogeographic pattern as the wolves of Isle Royle, but I’ve yet to see an outrage).

Even the main proponents of wolf introduction, Vucetich et al. and the National Parks Conservation Association invoke the myth that a “sustainable” wolf population is critical to the island’s “health.” Considering that wolves only appeared on the island within a human lifetime and probably blinked on and off the island historically, wolves are only an ephemeral component of this dynamic ecosystem. They never have been “sustainable,” and if ecosystem “health” hinges on the presence of wolves, the island has always been naturally unhealthy.

The rhetoric of “healthy” ecosystems is useless in science, because its meaning is entirely relative. Rolf Peterson, the researcher who initiated the moose-wolf study in the 70s, states that, “There’s a mythical belief that Isle Royale has been working well because we kept our hands off it; my opinion is, it worked well because there were wolves there” (from here). You can only consider a wolf-inhabited Isle Royale as “healthy” if you define a “healthy” ecosystem as one that looks the same way it did when you started your research plan. The real myth is conflating wolf presence with Isle Royale’s natural state, and in this case, it seems a personal mythology crafted to shore the legacy of Peterson’s research project.

The Park’s plan:

The preferred action of NPS is to install 20-30 wolves on the island over the next 3 years, and if those don’t take, to continue introducing for 2 more years.

Originally, the proponents of airdropping new wolves onto the Isle proposed it as genetic “rescue.” But with only two post-breeding age, inbred stock left, there is little chance that new wolves will breed with the two relics. Thus, in reality, this is not a genetic rescue project, it is a genetic replacement project.

So, where do the replacement wolves come from? The EIS suggests that wolves should be sourced from the mainland near the Park, but that many different populations around Lake Superior should be mixed on the island. They also suggest sourcing wolves with experience hunting moose (which are rare in mid-western populations).

Will a new population fair better? The reason wolves lost the plot in the first place was due to the ubiquitous force of natural selection. When faced with strong selection pressure, organisms are faced with three choices: move (not possible on an island), adapt, or disappear. The current wolves were not able to adapt to the ecological scenario they found on the island, so they are disappearing. The NPS knows that new wolves will be even more likely to succumb to selection pressure because they will not be locally adapted. This is why they are planning recurring introductions for a total of up to 5 years. The new population, with lots of diverse genetic material to work with, might be more prone to local adaptation, or it might be more prone to crash because the animals are too locally adapted to their naive system to cope in the new setting.

It might be tempting to think that evolution won’t be a factor considering the short tenure of wolves on the island, but wolf generation times are under 5 years (Mech et al. 2016) which means that they’ve had about 20 generations on the island. We know from the deluge of rapid evolution studies in the past few years that 20 generations is well within the timespan for marked evolution. Similarly, one can expect that moose have been evolving in that time too (Hoy et al. 2018), as have the plants that are browsed by moose, and the small mammals, and the microorganisms that exists in concert… In other words, the entire trophic system has been subject to dynamic eco-evolutionary change that has refined its assemblage and genetic composition. Replacing local wolves with wolves from elsewhere will short-circuit that dynamic process and set a new eco-evolutionary trajectory. Any study that occurs post-introduction will be studying a different eco-evolutionary system, altogether.

Proponents have made the case that occasional genetic influx from the mainland population (when a wolf might cross the ice to the island in cold winters) is part of the natural dynamic, but that climate change has disrupted this process. Leaving aside the fact that much of Isle Royales history was wolf-less long before climate change, reintroducing wolves does not simulate this natural process. In natural migration events, wolves are not randomly selected from a larger pool. The process of migration is a selective sieve that winnows out some potential migrants and selects for others. By high-grading the genetic stock from the mainland based on their own criteria, the Park Service will not be replicating nature, they will be conducting a large-scale, manipulative selection experiment.

The value of non-intervention:

As I mentioned, one of the most critical values of Wildernesses are their role as baselines. This is a point  repeatedly highlighted in the “Strategic Plan for Scientific Research in Isle Royal National Park” (Schlesinger et al. 2009).  The Plan lists as “Unique Attributes of Isle Royale National Park” that it is “An Isolated Location for Baseline Studies”, and “an Ideal Place to Study Fundamental Ecological Concepts” like island-biogeography and predator-prey dynamics.

Isle Royale attracts biogeographers, whose focus is the distribution of life forms as determined by the balance of regional dispersal and local extinction processes (MacArthur and Wilson 1967). Determination of the relative importance of both dispersal and extinction is of central interest to ecologists wishing to explain variability in the species diversity of a given environment and the potential changes brought about by environmental change. As the Strategic Plan states, “the pristine nature of Isle Royale offers an opportunity to examine the potential influence of regime shifts due to natural causes or indirect anthropogenic causes such as climate change.” If we choose a policy of artificially imposing stasis on a naturally dynamic ecosystem we lose that value almost entirely.

On the other hand, if we practice humility and allow natural systems to be dynamic, we can ask a list of interesting questions: What happens if we remove those top trophic levels of moose and wolf? How will that impact the nutrient cycle? How will it impact community dynamics? In what ways will the change in selection pressures drive evolution? Will the eco-evo dynamic play out in predicatable ways based on theory and inference from other archipelagos? What will the post-wolf community composition look like and will it be the same as the pre-wolf community? Etc. etc…

There are endless scientific questions that a wolf-less IR can answer. On the other hand, a replacement wolf population cannot even answer the original question that it is intended to address because such a manipulation cannot be considered a continuation of that community; at best, we can only consider this a manipulative experiment at the price of sacrificing an entire natural ecosystem and ruining an exemplary opportunity to study eco-evo dynamics.

References:

Hoy, S.R., Peterson, R.O., and Vuctich, J.A. 2018. Climate warming is associated with smaller body size and shorter lifespans in moose near their southern range limit. Global Change Biology. DOI: 10.1111/gcb.14015