Quantifying uncertainty in the identification of endangered ecological communities

Abstract

Ecological community and ecosystem "red lists" have been developed in several jurisdictions to improve ecosystem-level biodiversity protection. However, a challenge for the conservation and management of listed ecosystems is consistent identification in the field or from plot records. Ecosystem descriptions must have enough detail for positive identification but be broad enough that most instances are included. In many jurisdictions, descriptions are not supported by dichotomous keys or thresholds of ecosystem collapse and identification relies on the interpretation of trained individuals, with potential for opposing opinions. Using a structured process, we assessed the ability of experts to identify a critically endangered ecological community from vegetation plot samples. We compared the allocations made by experts with a numeric classification that underpinned the legal definition of the community. Overall, experts correctly identified the presence or absence of the community in 81% of samples although individual classification rates ranged from 63% to 94%. False positive rates varied among experts (7-50%) and experienced botanists did not necessarily perform better. Disturbance increased uncertainty and experts differed in their opinion about when the community had collapsed and was no longer recoverable. Inconsistent interpretation, in the absence of diagnostic keys and consensus models of collapse, will have implications for recovery and conservation of listed communities and ecosystems, and could impact the effectiveness of laws and policies designed to protect them.

The importance of expert selection when identifying threatened ecosystems

“…Presumably experts would place differing weight on such features, increasing variability. There is, however, scant information to show how consistently ecosystems are diagnosed by experts in the field (Dorrough et al., 2021).…”

“…Where expert judgement is required to identify ecosystems, model‐based, expert‐informed decision support tools have the ability to improve identification consistency and increase confidence that decisions reflect a broad range of knowledge (Saunders et al., 2020, Dorrough et al., 2021). We found consistent, minimal error rates for models constructed with judgements from as few as 20 experts, on par with previous studies that use similar modeling approaches (Sinclair et al., 2015, 2018).…”

“…Labeling ecological communities by their dominant species may prime experts toward a confirmation bias. It is also possible that heuristics of the elicitation process, such as mental short cuts with question substitution, contributed to this (Tversky & Kahneman, 1974, O'Hagan, 2019, Dorrough et. al., 2021.…”

“…Limitations in ecosystem definition often results in identification in situ via expert opinion (Adam, 2009, Dorrough et al., 2021). As such, the decision‐making process is often unstructured, unquantified and lacks transparency.…”

Assessing Integrity Using Vegetation Structure and Composition

“…However, people, including experts in vegetation assessment, use different cues to perceive integrity. When assessing vegetation communities experts may tend to be biased towards dominant woody species (Dorrough et al 2021), rather than overall structure or composition (such as foliage cover and native species richness among functional groups).…”