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      Where vectors collide: the importance of mechanisms shaping the realized niche for modeling ranges of invasive Aedes mosquitoes

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      Biological Invasions

      Springer Science and Business Media LLC

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          Concluding Remarks

           G. Hutchinson (1957)
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            Species Distribution Models: Ecological Explanation and Prediction Across Space and Time

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              Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges.

              Species distribution models (SDMs) use spatial environmental data to make inferences on species' range limits and habitat suitability. Conceptually, these models aim to determine and map components of a species' ecological niche through space and time, and they have become important tools in pure and applied ecology and evolutionary biology. Most approaches are correlative in that they statistically link spatial data to species distribution records. An alternative strategy is to explicitly incorporate the mechanistic links between the functional traits of organisms and their environments into SDMs. Here, we review how the principles of biophysical ecology can be used to link spatial data to the physiological responses and constraints of organisms. This provides a mechanistic view of the fundamental niche which can then be mapped to the landscape to infer range constraints. We show how physiologically based SDMs can be developed for different organisms in different environmental contexts. Mechanistic SDMs have different strengths and weaknesses to correlative approaches, and there are many exciting and unexplored prospects for integrating the two approaches. As physiological knowledge becomes better integrated into SDMs, we will make more robust predictions of range shifts in novel or non-equilibrium contexts such as invasions, translocations, climate change and evolutionary shifts.
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                Author and article information

                Journal
                Biological Invasions
                Biol Invasions
                Springer Science and Business Media LLC
                1387-3547
                1573-1464
                August 2018
                January 25 2018
                August 2018
                : 20
                : 8
                : 1913-1929
                10.1007/s10530-018-1674-7
                © 2018

                http://www.springer.com/tdm

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