Climate connectivity of the bobcat in the Great Lakes region

Abstract

The Great Lakes and the St. Lawrence River are imposing barriers for wildlife, and the additive effect of urban and agricultural development that dominates the lower Great Lakes region likely further reduces functional connectivity for many terrestrial species. As the climate warms, species will need to track climate across these barriers. It is important therefore to investigate land cover and bioclimatic hypotheses that may explain the northward expansion of species through the Great Lakes. We investigated the functional connectivity of a vagile generalist, the bobcat, as a representative generalist forest species common to the region. We genotyped tissue samples collected across the region at 14 microsatellite loci and compared different landscape hypotheses that might explain the observed gene flow or functional connectivity. We found that the Great Lakes and the additive influence of forest stands with either low or high canopy cover and deep lake-effect snow have disrupted gene flow, whereas intermediate forest cover has facilitated gene flow. Functional connectivity in southern Ontario is relatively low and was limited in part by the low amount of forest cover. Pathways across the Great Lakes were through the Niagara region and through the Lower Peninsula of Michigan over the Straits of Mackinac and the St. Marys River. These pathways are important routes for bobcat range expansion north of the Great Lakes and are also likely pathways that many other mobile habitat generalists must navigate to track the changing climate. The extent to which species can navigate these routes will be important for determining the future biodiversity of areas north of the Great Lakes.

Keywords: Bobcat; Great Lakes region; Lynx rufus; functional connectivity; gene flow; landscape genetics; range expansion.

Figure 1

Predictions of northward expansion of a vagile habitat generalist across the Great Lakes region in Canada and the United States. MN, Minnesota, USA; NY, New York, USA; LPM, Lower Peninsula of Michigan, USA; UPM, Upper Peninsula of Michigan, USA. Spatial layers for administrative boundaries were gathered from the Database of Global Administrative Areas

Figure 2

Location of 240 bobcat (Lynx rufus) fur samples collected from a variety of sources between 2012 and 2017 across the Great Lakes region in Canada and the United States. The shaded area is the consensus bobcat range according to the IUCN and the Nature Conservancy. Lag1 is the first axis from a spatial principal component analysis on the alleles scores of bobcats. It represents the only significant major spatial variation across bobcats in the Great Lakes region. Labels are as follows: ON, Ontario, Canada; QC, Quebec, Canada; MN, Minnesota, USA; WI, Wisconsin, USA; MI, Michigan, USA; NY, New York, USA; VT, Vermont, USA; IA, Iowa, USA; IN, Indiana, USA; OH, Ohio, USA; PA, Pennsylvania, USA; WV, West Virginia; LS, Lake Superior, LM, Lake Michigan; LH, Lake Huron; LE, Lake Erie; LO, Lake Ontario

Figure 3

Landscape maps used to test isolation‐by‐resistance hypotheses of bobcat gene flow across the Great Lakes region. (a) Great Lake land barrier, (b) forest cover, (c) road density, and (d) annual snowfall. In total, 8 isolation‐by‐resistance models were tested and included only combinations of the Great Lakes with all other three maps. We also compared these models to a null model of panmixia (H ₀) and isolation by distance (H ₁)

Figure 4

Optimized average standard resistance transformation. (a) Forest cover optimized resistance transformation. In some shorelines, areas forest cover overlapped the Great Lakes layer and values were optimized as if they were the Great Lakes land barrier; therefore, these shore line areas received high average standard resistance simply because of the mismatch between spatial layers. However, in the interior, intermediate forest cover around 60% amplifies gene flow, while low and high forest cover impedes gene flow, but high cover impeded gene flow more over the Great Lakes region. (b) Annual snowfall optimized resistance transformation. Annual snowfall on the lakes was generally transformed to high resistance values compared with land. On land, low annual snowfall impeded gene flow the most, while high annual usually found in lake‐effect areas also impeded gene flow. Like forest cover, intermediate amounts of annual snowfall amplified gene flow over the Great Lakes region

Figure 5

The average standard resistance from 999 replicates of the top model that was fit using resistance surface optimization of a landscape model that included the additive effect of the Great Lake, forest cover, and annual snowfall. These models were fit to the genetic similarity of bobcat samples across the study area. Labels are as follows: nwON, northwestern Ontario, Canada; neON, northeastern Ontario, Canada; cON, central Ontario, Canada; sON, southern Ontario, Canada; QC, Quebec, Canada; MN, Minnesota, USA; WI, Wisconsin, USA; UPM, Upper Peninsula of Michigan, USA; LPM, Lower Peninsula of Michigan, USA; NY, New York, USA; VT, Vermont, USA; IA, Iowa, USA; IN, Indiana, USA; OH, Ohio, USA; PA, Pennsylvania, USA; WV, West Virginia; LS, Lake Superior, LM, Lake Michigan; LH, Lake Huron; LE, Lake Erie; LO, Lake Ontario; KB, Keweenaw Bay; MNI, Manitoulin Island, Ontario, Canada

Figure 6

Current density for gene flow through the Great Lakes region. Current density was estimated from the pairwise current of 100 nodes placed on the extremity of all sides of the study area. We used the average standard resistance surface of the top model and rescaled the values from 1 to 100 and used Circuitscape to calculate the cumulative current density of the pairwise iteration of the 100 nodes. We than natural log‐transformed, standardized, and scaled the current density to the mean. A value of 0 indicates areas that have average log‐transformed current density, and value below and above indicate below and above average log‐transformed current density