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Species distribution models of a predator-prey system under climate change
  • Xuezhen Ge,
  • Cort Griswold,
  • Jonathan Newman
Xuezhen Ge
University of Guelph

Corresponding Author:[email protected]

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Cort Griswold
University of Guelph
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Jonathan Newman
Wilfrid Laurier University
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Mechanistic and correlative models are two types of species distribution models (SDMs). They each have distinct foci, conceptual foundations, and levels of dependency on data availability, leading to potentially different estimates of species’ ecological niches and distributions. Mechanistic SDMs integrate detailed biological processes, making it possible to account for species’ biotic interactions. Despite their assumed importance, interactions in species distribution modeling remain uncommon. In this study, we applied an ensemble model of multiple correlative SDMs, a mechanistic SDM of the focal species (prey) alone, and a mechanistic SDM of the predator-prey interactions, to compare the predictions of correlative and mechanistic approaches and assess their relative strengths and limitations. We predict there are considerable and subtle differences in various predictions generated by the correlative and mechanistic approaches for each aphid species, which call for prior knowledge concerning species’ presence data or life histories. Our mechanistic SDMs allowed for the assessment of the relative significance of abiotic and biotic factors, along with their interactions, in determining species’ habitat suitability. Additionally, we predict aphid habitat suitability decreases across continents due to the effect of predation. However, this decrease may be offset or enhanced by the interaction effect between predation and climate change in different regions. This suggests the necessity of accounting for biotic interactions and the interplay between abiotic and biotic factors in mechanistic approaches. Our research highlights the impact of model philosophies in SDM studies and addresses the importance of selecting an appropriate modeling approach in line with the study’s objectives. Furthermore, our study suggests that mechanistic SDMs could serve as a valuable addition for assessing the robustness of correlative SDM predictions.