Abstract
Dispersal costs need to be quantified from empirical data and incorporated into dispersal models to improve our understanding of the dispersal process. We are interested in quantifying how landscape features affect the immediately incurred direct costs associated with the transfer of an organism from one location to another. We propose that least-cost modelling is one method that can be used to quantify direct transfer costs. By representing the landscape as a cost-surface, which describes the costs associated with traversing different landscape features, least-cost modelling is often applied to measure connectivity between locations in accumulated-cost units that are a combination of both the distance travelled and the costs traversed. However, we take an additional step by defining an accumulated-cost dispersal kernel, which describes the probability of dispersal in accumulated-cost units. This novel combination of cost-surface and accumulated-cost dispersal kernel enables the transfer stage of dispersal to incorporate the effects of landscape features by modifying the direction of dispersal based on the cost-surface and the distance of dispersal based on the accumulated-cost dispersal kernel. We apply this approach to the common brushtail possum (Trichosurus vulpecula) within the North Island of New Zealand, demonstrating how commonly collected empirical dispersal data can be used to calibrate a cost-surface and associated accumulated-cost dispersal kernel. Our results indicate that considerable improvements could be made to the modelling of the transfer stage of possum dispersal by using a cost-surface and associated accumulated-cost dispersal kernel instead of a more traditional straight-line distance based dispersal kernel. We envisage a variety of ways in which the information from this novel combination of a cost-surface and accumulated-cost dispersal kernel could be gainfully incorporated into existing dispersal models. This would enable more realistic modelling of the direct transfer costs associated with the dispersal process, without requiring existing dispersal models to be abandoned.
Highlights
Dispersal is an important process for ecology and evolution, affecting organisms at the individual, population, and species levels by influencing population dynamics and gene flow
The landscape genetics-based Akaike’s Information Criterion (AIC) ranking of the landscape representations (Table 2) showed little support for the uniform landscape representation (D, in Table 2) for which connectivity was measured using straight-line distance (Figure 4A). This representation ranked 46 out of 48, with the wi and r2 values indicating that there was minimal evidence to support this option as it does not explain differences in genetic distance (Figure 4B). This was in stark contrast to the highest ranked cost-surface landscape representation (DTR, in Table 2) for which connectivity was measured using the accumulated-cost of least-cost paths (LCPs) (Figure 4C)
The LCP accumulated-cost connectivity values derived from our cost-surface landscape representations explained about onethird of the variation in genetic differentiation (Table 2)
Summary
Dispersal is an important process for ecology and evolution, affecting organisms at the individual, population, and species levels by influencing population dynamics and gene flow. A distance-based dispersal kernel, calibrated from data on straight-line distances between the start and end locations of known dispersal events (Figure 1), is used within dispersal models to quantify the likelihood of an organism dispersing a given distance in any direction [3] This approach assumes that the landscape does not affect dispersal directions or distances. As a cost-benefit surface in which there are ‘peaks’ where benefits outweigh costs and ‘valleys’ where costs exceed benefits’’ This representation of the landscape as a cost-benefit surface fits nicely with both the conceptual model of the direct energy, time, or risk costs associated with the transfer stage of dispersal [1], and with measuring connectivity using least-cost modelling [4,5]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
