Combining species distribution models and value of information analysis for spatial allocation of conservation resources

Abstract

Managers often have incomplete information to make decisions about threatened species management, and lack the time or funding needed to obtain complete information. Value of information (VOI) analysis can assist managers in deciding whether to manage using current information or monitor to reduce uncertainty before managing. However, VOI analysis has not yet been applied to spatial allocation of monitoring resources across a landscape. Here, we demonstrate how to make the best use of data from species distribution models (SDMs) and VOI analysis to assess the value of land protection decisions for single and multiple‐species objectives across a heterogeneous landscape. Our method determines the situations where one should monitor before protecting the land, and those where one should act based on current incomplete information. Further, we prioritize land planning units based on cost‐effectiveness (expected number of occurrences protected per dollar spent) and identify properties to target for monitoring or immediate conservation. In a single species case study, we found that the optimal decision was to act based on current information when the prior probability of detecting an occurrence in a survey was low. When probability of detection was high, it was most effective to monitor the majority of units. In a multi‐species case study, monitoring was only optimal in 50% of cases, due to high inferred probability of at least one occurrence of a threatened species in many units. When compared to a simulation where units were monitored by default, using VOI to determine which units were monitored or prioritized for immediate conservation led to an increase in the expected number of occurrences protected. Synthesis and applications. Using a combination of species distribution models and value of information analysis can assist managers in efficiently distributing limited resources for protected area allocation. Our results suggest that if managers can use value of information to monitor more efficiently, it can lead to protecting a greater number of threatened species occurrences.

Using social values in the prioritization of research: Quantitative examples and generalizations

“…EVPI can be used to prioritize research and monitoring around the uncertainties that “matter most,” where “mattering” is defined in terms of the utility of actions. In applied ecological contexts, EVPI has been used to (1) design monitoring programs that address stakeholder conservation concerns (Runge et al, 2011 ), (2) identify the switch‐point between monitoring and acting (Bennett et al, 2018 ), (3) spatially prioritize conservation efforts (Raymond et al, 2020 ), and (4) quantify the species‐persistence benefits of reducing the most important uncertainty‐species responses to threat alleviation (Nicol et al, 2019 ). EVPI has also been focus of reviews (Bolam et al, 2019 ; Canessa et al, 2015 ), and analytical methods also accommodate imperfect information (Nicol et al, 2019 ; Raiffa & Schlaifer, 1961 ; Williams & Johnson, 2015 ).…”

Predicting the current habitat refugia of Himalayan Musk deer (Moschus chrysogaster) across Nepal

“…Due to the presence of large data and multifaceted associations between species and ecological variables, the scope of computer algorithms for ecological niche modeling, habitat modeling, predictive habitat distribution modeling, and range mapping such as SDM has increased to solve the problem of ecologists and statisticians (Beery et al., 2021). Besides, SDM helps to envisage the effects of climate change on species, which is very essential to achieve the conservation goals of being aware of the species distribution (Forester et al., 2013; Raymond et al., 2020). Discrepancies among different SDMs create challenges in determining the optimal model choice (Elith et al., 2011; Elith & Leathwick, 2009; Renner & Warton, 2013).…”

Defining the extent of suitable habitat for the endangered Maple-Leaf oak (Quercus acerifolia)

“…Many recent SDM studies have used the "ensemble" SDM methodology, which incorporates predictions from multiple modeling techniques to make better and more accurate predictions (Meller et al 2014, Hao et al 2019, Ahmad et al 2020. Furthermore, SDMs have the potential to contribute to conservation planning goals by augmenting knowledge of species distributions (Raymond et al 2020) and predicting the impacts of climate change on species (Schwartz 2012, Foden andYoung 2016). SDMs that predict future climate change events can help to prioritize both present and future biodiversity conservation efforts by identifying newly available habitats under changing climate (Bellard et al 2012, Schwartz 2012.…”