Abstract

AbstractAimOur ability to model species distributions and abundances is a valuable ecological tool in predicting future distributions of species. Effectively incorporating connectivity into these predictions is crucial; however, many connectivity measures utilize metrics which may not have a direct relation to the dispersal capacity of the species they are attempting to model. The identification of more relevant metrics is therefore a vital step forward in species distribution modelling.Location85 freshwater lakes across a latitudinal gradient in Sweden, and an additional 282 freshwater lakes in one drainage basin in northern Norway.MethodsTo investigate the effect of different connectivity measures, we first record recolonization of fish into lakes previously treated with the piscicide rotenone. Two invasive fish species, the northern pike (Esox lucius) and the European perch (Perca fluviatilis), were used as focal study species. We model the distributions of these species in a drainage basin with snapshot data of present‐day distributions to see how well the effects of the different connectivity measures correspond to the effects seen in our recolonization study. Connectivity is quantified using slope and distance along streams connecting lacustrine populations.ResultsThe effects of connectivity variables were similar in both the recolonization study and the species distribution modelling. Incorporation of connectivity improved species distribution models significantly. There was little evidence for the inclusion of distance between populations, while there was strong evidence for the inclusion of different slope parameters for both species.Main conclusionsOur study demonstrates the need to ensure the relevance of connectivity measures when accounting for dispersal limitation in distribution models. The correspondence of estimated connectivity measures from recolonization studies to those estimated from species distribution models demonstrates a link between species dispersal capacity and the connectivity measures employed, and is likely to improve our ability to predict species future distributions.

Highlights

  • In the wake of global trends in species distribution range shifts and population decline, providing reliable estimates of shifts in species distribution and abundance has become one of the foremost goals of ecology (Briscoe et al, 2019; Rahel & Olden, 2008; Spooner, Pearson, & Freeman, 2018)

  • Most distribution models use measures of connectivity based on Euclidean distance, which may in some cases be a poor representation of species dispersal limitations (Calabrese & Fagan, 2004)

  • We investigate the effect of introducing connectivity measures to species distribution modelling and directly test for how these measures relate to species dispersal capacity

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Summary

| INTRODUCTION

In the wake of global trends in species distribution range shifts and population decline, providing reliable estimates of shifts in species distribution and abundance has become one of the foremost goals of ecology (Briscoe et al, 2019; Rahel & Olden, 2008; Spooner, Pearson, & Freeman, 2018). By investigating the effects of several connectivity measures on two species over the two study systems, we will test the effect of using connectivity measures in modelling distributions, and how the effects of these same measures correspond to their direct impact on species dispersal ability. This will indicate how effectively and accurately connectivity measures can be incorporated into and interpreted in larger-scale distribution modelling. We expect our selected connectivity measures to show similar effects across study systems, with different connectivity measures potentially varying in their effects across species

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| DISCUSSION
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