Potential for climate change driven spatial mismatches between apple crops and their wild bee pollinators at a continental scale

Marshall, Leon; Leclercq, Nicolas; Weekers, Timothy; El Abdouni, Insafe; Carvalheiro, Luísa G.; Kuhlmann, Michael; Michez, Denis; Rasmont, Pierre; Roberts, Stuart P.M.; Smagghe, Guy; Vandamme, Peter; Wood, Thomas; Vereecken, Nicolas J.

doi:10.1016/j.gloenvcha.2023.102742

Cited by 1 publication

(1 citation statement)

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“…SDM relies on the relationship between environmental variables and empirical data on the target-species occurrence. The most relevant ranges and composition of environmental factors in explaining species occurrence are quantified and mapped, generating a baseline scenario (Diniz-Filho et al, 2009;Espıńdola and Pliscoff, 2018;Hao et al, 2019;Huang et al, 2022;Marshall et al, 2023). Subsequently, the characteristics detected in the baseline scenario are then projected on a new set of variables with predicted variations for future climate change scenarios (Hulme, 2005;Ludena and Yoon, 2015;Li et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A methodological approach to identify priority zones for monitoring and assessment of wild bee species under climate change

Acosta,

dos Santos,

Imperatriz-Fonseca

et al. 2024

Front. Bee Sci.

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Climate change is affecting wild populations worldwide, and assessing the impacts on these populations is essential for effective conservation planning. The integration of advanced analytical techniques holds promise in furnishing detailed, spatially explicit information on climate change impacts on wild populations, providing fine-grained metrics on current environmental quality levels and trends of changes induced by estimated climate change scenarios. Here, we propose a framework that integrates three advanced approaches aiming to designate the most representative zones for long-term monitoring, considering different scenarios of climate change: Species Distribution Modeling (SDM), Geospatial Principal Component Analysis (GPCA) and Generalized Procrustes Analysis (GPA). We tested our framework with a climatically sensible Neotropical stingless bee species as study case, Melipona (Melikerria) fasciculata Smith, 1854. We used the SDM to determine the climatically persistent suitable areas for species, i.e. areas where the climate is suitable for species today and in all future scenarios considered. By using a GPCA as a zoning approach, we sliced the persistent suitable area into belts based on the variability of extremes and averages of meaningful climate variables. Subsequently, we measured, analyzed, and described the climatic variability and trends (toward future changes) in each belt by applying GPA approach. Our results showed that the framework adds significant analytical advantages for priority area selection for population monitoring. Most importantly, it allows a robust discrimination of areas where climate change will exert greater-to-lower impacts on the species. We showed that our results provide superior geospatial design, qualification, and quantification of climate change effects than currently used SDM-only approaches. These improvements increase assertiveness and precision in determining priority areas, reflecting in better decision-making for conservation and restoration.

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