Integrating species distribution modelling into decision-making to inform conservation actions

Villero, Dani; Pla, Magda; Camps, David; Ruiz‐Olmo, Jordi; Brotons, Lluı́s

doi:10.1007/s10531-016-1243-2

Cited by 102 publications

(77 citation statements)

References 100 publications

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“…), there remain very few examples illustrating how SDMs can aid the development of species recovery plans (Villero et al. ).…”

Section: Introductionmentioning

confidence: 99%

“…Ideally, a good recovery plan requires adaptive management (i.e., information obtained on management effectiveness through monitoring of outcomes) (McCarthy & Possingham 2007), with the aim of updating knowledge and improving decision making over time (Canessa et al 2016). Although the role of SDMs in supporting conservation decision making has increased over the last 20 years (Guisan et al 2013), there remain very few examples illustrating how SDMs can aid the development of species recovery plans (Villero et al 2017). Numerous studies have demonstrated the importance of incorporating functional variables into SDMs frameworks to account for processes and functions related to the exchange of matter and energy (i.e., ecosystem functioning [sensu Cabello et al 2012]).…”

Section: Introductionmentioning

confidence: 99%

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Integrating intraseasonal grassland dynamics in cross‐scale distribution modeling to support waterbird recovery plans

Regos

Vidal

Lorenzo

et al. 2019

Conservation Biology

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Despite much discussion about the utility of remote sensing for effective conservation, the inclusion of these technologies in species recovery plans remains largely anecdotal. We developed a modeling approach for the integration of local, spatially measured ecosystem functional dynamics into a species distribution modeling (SDM) framework in which other ecologically relevant factors are modeled separately at broad scales. To illustrate the approach, we incorporated intraseasonal water‐vegetation dynamics into a cross‐scale SDM for the Common Snipe (Gallinago gallinago), which is highly dependent on water and vegetation dynamics. The Common Snipe is an Iberian grassland waterbird characteristic of European agricultural meadows and a member of one of the most threatened bird guilds. The intraseasonal dynamics of water content of vegetation were measured using the standard deviation of the normalized difference water index time series computed from bimonthly images of the Sentinel‐2 satellite. The recovery plan for the Common Snipe in Galicia (northwestern Iberian Peninsula) provided an opportunity to apply our modeling framework. Model accuracy in predicting the species’ distribution at a regional scale (resulting from integration of downscaled climate projections with regional habitat–topographic suitability models) was very high (area under the curve [AUC] of 0.981 and Boyce's index of 0.971). Local water‐vegetation dynamic models, based exclusively on Sentinel‐2 imagery, were good predictors (AUC of 0.849 and Boyce's index of 0.976). The predictive power improved (AUC of 0.92 and Boyce's index of 0.98) when local model predictions were restricted to areas identified by the continental and regional models as priorities for conservation. Our models also performed well (AUC of 0.90 and Boyce's index of 0.93) when projected to updated water‐vegetation conditions. Our modeling framework enabled incorporation of key ecosystem processes closely related to water and carbon cycles while accounting for other factors ecologically relevant to endangered grassland waterbirds across different scales, allowed identification of priority areas for conservation, and provided an opportunity for cost‐effective recovery planning by monitoring management effectiveness from space.

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“…), there remain very few examples illustrating how SDMs can aid the development of species recovery plans (Villero et al. ).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrating intraseasonal grassland dynamics in cross‐scale distribution modeling to support waterbird recovery plans

Regos

Vidal

Lorenzo

et al. 2019

Conservation Biology

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“…Updating our SDMs periodically as described above will hopefully confirm their utility. We hope that these results will encourage effective management plans (Villero, Pla, Camps, Ruiz-Olmo, & Brotons, 2017) specific to the species of the proposed priority areas, so that conservation efforts can be better targeted.…”

Section: Model Limitationsmentioning

confidence: 97%

Predicting the impacts of climate change on Papio baboon biogeography: Are widespread, generalist primates ‘safe’?

Hill

Winder

2019

Journal of Biogeography

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Aims To explore whether wide‐ranging, generalist primates like baboons can be presumed ‘resilient’ in the face of climate change. We identify environmental variables influencing baboons’ current distributions and predict their future potential distributions under different climate change scenarios. Location Africa and Arabia. Taxon Baboons, Papio spp. Methods We used localities for olive, yellow, Guinea, hamadryas, chacma and Kinda baboons together with high‐resolution data on bioclimatic variables, altitude and vegetation to construct species distribution models (SDMs). These SDMs were run under current and future conditions, with future models based on three General Circulation Models (MIROC‐ESM, CCSM4 and HadGEM2‐ES) under two Representative Concentration Pathways (RCP2.6 and RCP6.0) for 2050 and 2070 to explore a range of different possible futures. Results All SDMs produced AUC values >0.916 suggesting excellent overall performance. Altitude was the most important variable influencing Guinea baboon distributions (contributing 41.6%), temperature seasonality for olive and yellow baboons (47.5% and 35.4% respectively), precipitation of the driest month for hamadryas baboons (24.4%), annual mean precipitation for the Kinda baboon (45.1%) and mean temperature of the driest quarter for chacma baboons (41.4%). Chacma and Kinda baboons are predicted to suffer substantial habitat loss, and Guinea baboons may do the same if conditions aridify as climates warm. In contrast, all models for the olive and hamadryas baboons predicted an increase in suitable habitat and only smaller changes were predicted for the yellow baboon. Main Conclusions Two or three of six baboon taxa are at risk of significant habitat loss as climates warm despite their apparent ecological flexibility. The chacma and Kinda baboons (both IUCN listed as Least Concern) will be worst affected, followed by the Guinea baboon (Near Threatened) if warming brings aridification. We recommend more focus on biogeographical tools as a means of exploring vulnerabilities in seemingly resilient species.

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“…SDM is a method of assessing areas that provide suitable abiotic environments (often, climate) for a given species, using the relationship between observed points of occurrence and environmental variables, to generate a spatial prediction of regions within which environmental conditions are suitable for species survival and population growth. Previous reviews of SDM have focused on their use to predict the distribution of invasive species (Barbosa, Schneck, & Melo, 2012), to determine MaxEnt model usage in wildlife research (Baldwin, 2009), and to inform conservation practitioners in decision-making (Villero, Pla, Camps, Ruiz-Olmo, & Brotons, 2017), among others. Cayuela et al (2009) pointed out that 39% of the papers they reviewed on SDM were mainly focused on the development of new methodologies and the evaluation of performance of different methods, while the other 61% of the studies applied SDM in contexts such as species conservation, biological invasions, climate change, autecology, and biogeography.…”

Section: Introductionmentioning

confidence: 99%

“…While uncertainty in species distributions is highly unknown for Latin America (but see modeling methods to estimate uncertainty in Diniz-Filho et al, 2009;Loyola, Lemes, Faleiro, Trindade-Filho, & Machado, 2012;Sales, Neves, De Marco, & Loyola, 2017), countries in the region are increasing their economic dependence on extraction activities, profoundly impacting their ecosystems (Rosales, 2008;Villarroya, Barros, & Kiesecker, 2014). As pinpointed by Villero et al (2017), SDM should inform decision-making for unknown and rare species, but the absence of information should be viewed as an opportunity for collaboration between institutions more than a weakness of the region. Networks of scientific collaboration could pave the road to improve species' knowledge not just on their distributions but on habitat requirements, tolerance to disturbance, and population dynamics, to have robust forecasting on the impacts of environmental change on species' conservation status (Franklin, 2010).…”

Section: Introductionmentioning

confidence: 99%

Species Distribution Modeling in Latin America: A 25-Year Retrospective Review

Urbina‐Cardona

Blair

Londoño

et al. 2019

Tropical Conservation Science

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Species distribution modeling (SDM) is a booming area of research that has had an exponential increase in use and development in recent years. We performed a search of scientific literature and found 5,533 documents published from 1993 to 2018 using SDM, representing a global network of 4,329 collaborating institutions from 155 countries, with Brazil and of Brasilia were the most productive. From this body of literature, the most frequently modeled taxonomic groups were Chordata and Insecta, and the most common realms of application were conservation planning and management, climate change, species conservation, epidemiology, evolutionary biology, and biological invasions. From the 36 modeling methods identified to generate SDMs, MaxEnt is used in 73.5% of the papers, followed by Genetic Algorithm for Rule-Set Prediction (GARP) with 18.7%, and just 7.4% of the papers compared between 3 and 10 modeling methods. In Latin American countries, productivity in SDM research could be improved as the network of collaborations diversifies and connects with other productive countries (such as United ). The scientific collaboration between Latin American countries should be increased, as the most prolific countries (Brazil, Mexico, Argentina, and Colombia) share less than 10% of its productivity. Some of the main challenges for SDM development in Latin America include bridging the gaps from (a) software use to research productivity and (b) translation to decision-making. To address these challenges, we propose to strengthen communities of practice where modelers, species experts, and decisionmakers come together to discuss and develop SDM to shift and enhance current paradigms on how science and decisionmaking are linked.

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Integrating species distribution modelling into decision-making to inform conservation actions

Cited by 102 publications

References 100 publications

Integrating intraseasonal grassland dynamics in cross‐scale distribution modeling to support waterbird recovery plans

Integrating intraseasonal grassland dynamics in cross‐scale distribution modeling to support waterbird recovery plans

Predicting the impacts of climate change on Papio baboon biogeography: Are widespread, generalist primates ‘safe’?

Species Distribution Modeling in Latin America: A 25-Year Retrospective Review

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