Spread rates on fragmented landscapes: the interacting roles of demography, dispersal and habitat availability

Abstract

Aim We still lack a comprehensive understanding of the relative importance of demographic, dispersal and landscape characteristics on species' rates of range expansion (RRE) and on how these factors interact. Here, we provide an analysis of these effects for passive dispersers, by investigating how habitat characteristics, such as habitat quality, availability and fragmentation, interplay with species' dispersal characteristics in determining species' RRE. In addition, we assessed the predictability of RRE in cases where we have the knowledge of a species' demography, dispersal and habitat availability.Methods Using the newly available individual-based modelling platform, RANGESHIFTER we simulated the range expansion of species with different dispersal abilities, by varying mean dispersal distance and number of emigrants, on various landscapes. Landscapes varied in habitat quality (in terms of carrying capacity and species' growth rates) and in habitat availability (in terms of the proportion of suitable habitat and its degree of fragmentation).Results Our results show that 55% of the total variation in RRE was explained by our six main effects, being considerably faster in landscapes with more suitable habitat, but only slightly affected by the degree of habitat fragmentation. Also, synergies between the amount of suitable habitat and species dispersal characteristics had significant positive effects on range expansion. Notably, however, 33% of variation in RRE was not explained by any of the tested factors or interactions between them and can be considered inherent and irreducible uncertainty.Main conclusions Simulation-based approaches provide important insights into the drivers of RRE that are relevant for conservation planning. For instance, our results indicate when it is likely to be better to allocate resources to improve existing habitat rather than creating new habitat, and vice versa. Additionally, our results emphasize that there will often be substantial uncertainty in the RRE, which needs to be taken into account for ecological management.

Gaps in kelp cover may threaten the recovery of California sea otters

“…Because female sea otters exhibit strong site fidelity and typically are poor pioneers (Tinker et al 2008b, Lafferty andTinker 2014), large gaps in kelp forests along the range fronts present a substantial obstacle to natural range expansion. Full recovery of the California sea otter may require consideration of management strategies that overcome marginal habitats and population sinks along the range edges, perhaps by increasing the rate of female dispersal (Barros et al 2016) to more sheltered habitats that could support reproduction and act as a source to repopulate areas farther north. In addition to fostering recovery, sea otter population expansion along its historic range would enhance restoration of both wetland and nearshore coastal habitats throughout California.…”

Integrating an individual-based model with approximate Bayesian computation to predict the invasion of a freshwater fish provides insights into dispersal and range expansion dynamics

“…Thus, when these species are introduced into a new region, whether intentionally or accidentally (Gozlan et al 2010a), the data available for predicting their invasiveness are often limited (Top et al 2018), resulting in poorly informed model parameters (Heikkinen et al 2014;Urban et al 2016) and final models with high uncertainty (Parry et al 2013). We showed that monitoring programmes can provide dispersal time series that, when coupled with ABC methods, can help overcome this lack of prior data, enabling more robust predictions of the dispersal dynamics and future invasiveness of the modelled species (Neeson et al 2012;Barros et al 2016;Samson et al 2017).…”

Spatial and temporal heterogeneity in climate change limits species' dispersal capabilities and adaptive potential

“…In this scenario, the heterogeneous spatial pattern of climate change regimes may mimic classical habitat mosaics, with some regions being more hospitable to particular genotypes than others (Barros et al 2016). In general, dispersal across heterogeneous landscapes is more difficult than across homogeneous ones, with patterns of local adaptation exacerbating this disparity (Zhang and Buckling 2016).…”

“…Our simulated landscape was evenly populated, and only varied in one habitat parameter -climate asynchronicity. Natural habitat matrices are far more complex and it is unlikely that even the most abundant species are distributed evenly across their range (Barros et al 2016). Similarly, the genetic architecture of our simulated quantitative trait was simplistic.…”