Consequences of ignoring dispersal variation in network models for landscape connectivity

Abstract

Habitat loss and fragmentation can negatively influence population persistence and biodiversity, but the effects can be mitigated if species successfully disperse between isolated habitat patches. Network models are the primary tool for quantifying landscape connectivity, yet in practice, an overly simplistic view of species dispersal is applied. These models often ignore individual variation in dispersal ability under the assumption that all individuals move the same fixed distance with equal probability. We developed a modeling approach to address this problem. We incorporated dispersal kernels into network models to determine how individual variation in dispersal alters understanding of landscape-level connectivity and implemented our approach on a fragmented grassland landscape in Minnesota. Ignoring dispersal variation consistently overestimated a population's robustness to local extinctions and underestimated its robustness to local habitat loss. Furthermore, a simplified view of dispersal underestimated the amount of habitat substructure for small populations but overestimated habitat substructure for large populations. Our results demonstrate that considering biologically realistic dispersal alters understanding of landscape connectivity in ecological theory and conservation practice.

Integrating dispersal, breeding and abundance data with graph theory for the characterization and management of functional connectivity in amphibian pondscapes

“…The lack of dispersal records in some species in our study can be explained by a low number of recaptures (L. boscai) or by their low abundance (H. molleri and D. galganoi), because the number of detected movements is often proportional to population size (Sullivan et al 2021). For these species, reconstructing representative dispersal kernels requires specific surveys focused on their breeding sites and/or longer time series.…”

“…To account for the intraspecific variability in dispersal frequencies and distances, dispersal can be regarded as a probabilistic variable rather than a fixed maximum value when designing a graph (Sullivan et al 2021 ). This method is more biologically realistic, relying on the use of dispersal kernels to estimate dispersal probabilities.…”

Using theoretical models to explore dispersal variation and fragmentation in urban environments

“…Understanding the consequences of variation in dispersal is essential to understanding how plants will respond to changing environmental conditions under climate change, habitat fragmentation, and increasing urbanization (Bullock et al, 2017; Sullivan et al, 2021). Yet most mathematical models of dispersal either ignore variation altogether (using mean population estimates of dispersal like dispersal distance or rate) or use a simplistic version of dispersal variation.…”

“…Such simplifications have important implications for the conclusions drawn from models. For example, Sullivan et al (2021) found that ignoring dispersal variation in network models of connectivity in grasslands overestimated a population's resilience to local extinctions and underestimated its resilience to fragmentation. Furthermore, assuming individuals do not disperse beyond the mean dispersal distance ignores the importance of long‐distance dispersal events.…”

Influence of natal habitat preference on habitat selection during extra‐home range movements in a large ungulate

“…Propensity to make prospecting EHRMs prior to dispersal may be tied to individual personality (Burkhalter et al, 2015 ; Debeffe et al, 2013 ), so it follows that habitat selection during both prospecting and dispersal EHRMs also may vary by individual (Wey et al, 2015 ). While studies of landscape connectivity attempt to link features and conditions of the environment with animal movement at a population level (Taylor et al, 1993 ; Tischendorf & Fahrig, 2000 ), individual behavioral variation may complicate inference of connectivity (Bélisle, 2005 ; Sullivan et al, 2021 ). Indeed, generalist species typically exhibit greater degrees of behavioral specialization (Bolnick et al, 2003 ; Carlson et al, 2021 ; Woo et al, 2008 ) and thus may adhere less to population‐level patterns of connectivity; additionally, this could be coupled with individual attraction to familiar landscape features (for an example with generalist species [wapiti, Cervus canadensis ] see (Wolf et al, 2009 )).…”