Identifying critical habitat for threatened species: concepts and challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Critical habitat is defined scientifically as the subset of habitat necessary for the long-termpersistence of a given species. Based on this definition, loss of any part of the critical habitat wouldresult in extinction of the species. In the United States, Australia, and Canada, critical habitat ofthreatened species is protected, to various degrees, under endangered species legislation. Effectiveprotection of critical habitat depends on it being identified accurately. Where there is potential forconflict with landowners and industry stakeholders, accurate critical habitat identifications are moredefensible in court, and minimise the opportunity costs of protecting areas that may not be asbeneficial to species persistence. However, obtaining the data required to accurately identify criticalhabitat can take up considerable time and resources that may otherwise be spent on conservationactions. At the same time, delaying protection of critical habitat to improve knowledge can result infurther habitat loss. In this thesis, I review key concepts and challenges surrounding theidentification of critical habitat, and develop decision tools to assist in deciding when and how toidentify it.In Chapter 2, I present a systematic review of critical habitat documentation from the United States,Canada, and Australia to identify the types of data and criteria that have been used to identifycritical habitat in the last decade. Contrary to scientific recommendations that long-term speciespersistence should be used as the criterion for identifying critical habitat, information about thelocation of species occurrences and particular habitat features were used instead to identify criticalhabitats for most of the species reviewed. Insufficient data and the desire to avoid potentialopposition from landowners are likely to be the main reasons for the use of such approaches tocritical habitat identification. Chapter 3 continues with an examination of the merits of the differentcriteria that could be used to inform critical habitat identification, and the types of errors associatedwith each. I also considered the potential consequences of the errors, and recommended that moreexplicit recognition of the potential for errors is important in minimising their negativeconsequences for species persistence.While the accuracy of critical habitat identification may be improved by collecting more data,delaying protection to do so may result in additional habitat loss if habitats are left unprotected inthe meantime. In Chapter 4, I used an optimisation approach to examine this trade-off between thebenefits of delaying protection to improve accuracy and the costs of additional habitat loss in theinterim. I modelled the change in the proportion of habitat correctly identified over time as afunction of both accuracy and habitat loss, and determined the optimal amount of time to spend learning that maximises this value. I found that at low rates of habitat loss, slow learning ratesresulted in a longer optimal learning period. At high rates of habitat loss, however, the improvementin accuracy no longer compensates for the loss of habitat, and the optimal amount of time to spendlearning becomes less than three years, regardless of the learning rate.Given the need for prompt critical habitat protection to avoid additional habitat loss, how should weidentify critical habitat to maximise accuracy while minimising the amount of time and resourcesspent in the process? I addressed this question in Chapter 5 by describing how a structured decisionmakingframework can be applied to help decide which types of data and approaches should beused to identify critical habitat, based on the conservation objectives, movement and habitat usepatterns, and the amount of habitat currently available for the species. Using a structured decisionframework to guide critical habitat identification can also help improve the consistency andtransparency of identification, as well as increase confidence in the validity of critical habitatidentification.This thesis aimed to recommend improvements to the identification of critical habitat by reviewingcurrent practices and potential errors in critical habitat identification, examining the potentialconsequences of these errors for conservation, and developing decision tools to assist in decidingwhen and how critical habitat should be identified. However, it is important to note that adequateenforcement of critical habitat protection also plays a crucial role in ensuring long-term speciespersistence. Further studies to examine how critical habitat protections are implemented andenforced will help to evaluate the overall impact of critical habitat identification on the recovery ofthreatened species, and provide insight into how critical habitats can be protected more effectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it