Assigning trend‐based conservation status despite high uncertainty

Author:

White Richard S. A.1ORCID,Stoffels Rick J.1,Whitehead Amy L.2

Affiliation:

1. National Institute of Water and Atmospheric Research Christchurch New Zealand

2. Wildlife Ecology & Management Manaaki Whenua ‐ Landcare Research Lincoln Canterbury New Zealand

Abstract

AbstractEstimates of temporal trends in species’ occupancy are essential for conservation policy and planning, but limitations to the data and models often result in very high trend uncertainty. A critical source of uncertainty that degrades scientific credibility is that caused by disagreement among studies or models. Modelers are aware of this uncertainty but usually only partially estimate it and communicate it to decision makers. At the same time, there is growing awareness that full disclosure of uncertainty is critical for effective translation of science into policies and plans. But what are the most effective approaches to estimating uncertainty and communicating uncertainty to decision makers? We explored how alternative approaches to estimating and communicating uncertainty of species trends could affect decisions concerning conservation status of freshwater fishes. We used ensemble models to propagate trend uncertainty within and among models and communicated this uncertainty with categorical distributions of trend direction and magnitude. All approaches were designed to fit an established decision‐making system used to assign species conservation status by the New Zealand government. Our results showed how approaches that failed to fully disclose uncertainty, while simplifying the information presented, could hamper species conservation or lead to ineffective decisions. We recommend an approach that was recently used effectively to communicate trend uncertainty to a panel responsible for setting the conservation status of New Zealand's freshwater fishes.

Funder

Ministry of Business, Innovation and Employment

Publisher

Wiley

Subject

Nature and Landscape Conservation,Ecology,Ecology, Evolution, Behavior and Systematics

Reference54 articles.

1. Ensemble forecasting of species distributions

2. Incorporating uncertainty in predictive species distribution modelling

3. Booker D. &Hicks M.(2013).Estimating wetted width and fish habitat areas across New Zealand's rivers : Wetted widths estimation. NIWA Client Report CHC2013‐075 33.

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