Optimizing ecological surveys for conservation

Abstract

Conservation decisions must be made with limited funding and incomplete information. Ecological surveys can help reduce uncertainty and, in turn, potentially lead to better management decisions. However, conducting surveys can reduce funds available for implementing management actions and, in turn, can potentially lead to worse conservation outcomes. Here we develop a value of information framework to evaluate and optimize survey plans. Our framework evaluates survey plans based on their ability to improve how likely resulting protected area systems are to secure species of interest, and accounts for survey and land acquisition costs. Using an example of eight imperilled plant species in Middlesex County (Ontario, Canada), we assessed our framework against conventional approaches for designing survey plans that involve selecting places with (i) maximal geographic coverage, (ii) diverse environmental conditions, (iii) highly uncertain information, (iv) high imperilled species richness and (v) low protected area establishment costs. We found that optimized survey plans could improve the protected area system by, on average, 57.52% (0.21 SD) (up to 105.25%) over conventional survey approaches. These optimized plans could also improve the protected area system by, on average, 19.91% (up to 32.37%) over simply prioritizing based on existing information. Survey plans designed using conventional approaches, in many cases, led to a worse protected area system than simply using existing information. Such conventional approaches performed the worst when they allocated a large percentage of the available budget to data collection. Synthesis and applications. Our findings demonstrate that conventional approaches for designing ecological surveys can impede conservation efforts by squandering funds on data that have little chance of improving decision making. Indeed, conventional approaches for designing surveys had the poorest performance under limited budgets, which are typical in real world planning exercises. We recommend that conservation practitioners carefully consider how data collection efforts can potentially improve conservation decisions, and also the costs associated with data collection. By applying the principles of value of information, our framework enables conservation practitioners to cost‐effectively collect data in places that will maximize conservation outcomes.

A super SDM (species distribution model) ‘in the cloud’ for better habitat-association inference with a ‘big data’ application of the Great Gray Owl for Alaska

“…Having such concepts available allows for predictions of high accuracy (see 132 for 1 m prediction resolution), specifically when it comes to impact assessments, e.g. with an optimized survey design 133 , done into the future and with climate change (e.g. 134 - 136 ).…”

A dark side of conservation biology: Protected areas fail in representing subterranean biodiversity

“…In order to identify those basins that better complement the current network of protected areas in representing subterranean species, we computed the minimum set of basins that represent all the gap species at least once in our study areas. For this, we used the R package 'prioritizr' v.7.1.1 with Gurobi solver (Hanson et al, 2023), which uses mixed integer linear programming techniques to solve conservation planning problems. Because there were multiple optimal solutions, we computed conservation importance (irreplaceability) scores for each basin following Ferrier et al (2000) as implemented in 'prioritizr'.…”

The value of targeted biological surveys: An assessment of Australia's Bush Blitz programme

“…In most cases, it is difficult to separate the effect of how complete biological datasets are from other confounding factors (Kujala et al, 2018). Increasingly, methods are being developed to test the effectiveness and improve the utility of additional data collection (Callaghan et al, 2019;Hanson et al, 2023;Mesaglio et al, 2023).…”

“…limited money to spend, challenges in filling data gaps efficiently and adequacy of existing data), ongoing survey efforts yield limited value. Other studies have found either support for additional data collection (Chowdhury et al, 2023;Hanson et al, 2023;Ware et al, 2018) or mixed (Field & Elphick, 2019;Rodewald et al, 2019) or limited support (Maxwell et al, 2015;Mentges et al, 2021).…”

“…In most cases, it is difficult to separate the effect of how complete biological datasets are from other confounding factors (Kujala et al., 2018). Increasingly, methods are being developed to test the effectiveness and improve the utility of additional data collection (Callaghan et al., 2019; Hanson et al., 2023; Mesaglio et al., 2023). The value of additional data to conservation planning will certainly decline at some point simply because the incremental difference made reduces as the dataset becomes more complete (Possingham et al., 2007).…”