Methodological advances in the field of landscape genetics
Spatial analysis of genetic data is a valuable tool for answering questions about dispersal and functional connectivity. I have made methodological contributions to the growing field of landscape genetics.
The effect of cost surface parameterization on landscape resistance estimates
A cost surface is a representation of a landscape’s permeability to animal movement or gene flow and is a tool for measuring functional connectivity in landscape ecology and genetics studies. Parameterizing cost surfaces by assigning weights to different landscape elements has been challenging however, because true costs are rarely known. Assigning weights would be made easier if the sensitivity of different landscape resistance estimates to relative costs was known. We carried out a sensitivity analysis of three methods to parameterize a cost surface and two models of landscape permeability.
The effect of map boundary on estimates of landscape resistance to animal movement
Artificial boundaries on a map occur when the map extent does not cover the entire area of study; edges on the map do not exist on the ground. These artificial boundaries might bias the results of animal dispersal and landscape connectivity models by creating artificial barriers to movement for model organisms where there are no barriers for real organisms. We found that artificial boundaries result in an overestimate of landscape resistance. We then proposed and tested a solution to artificially inflated resistance values whereby we placed a buffer around the artificial boundary as a substitute for the true, but unknown, habitat.
The sensitivity of genetic connectivity measures to unsampled and under-sampled sites
Landscape genetic analyses assess the influence of landscape structure on genetic differentiation. It is rarely possible to collect genetic samples from all individuals on the landscape and thus it is important to assess the sensitivity of landscape genetic analyses to the effects of unsampled and under-sampled sites. Network-based measures of genetic distance, such as conditional genetic distance, might be particularly sensitive to sampling intensity because pairwise estimates are relative to the entire network. We addressed this question by subsampling microsatellite data from two empirical datasets.
Node-based measures of connectivity in genetic networks
At-site environmental conditions can have strong influences on genetic connectivity, and in particular on the immigration and settlement phases of dispersal. However, at-site processes are rarely explored in landscape genetic analyses. Networks can facilitate the study of at-site processes, where network nodes are used to model site-level effects. We used simulated genetic networks to compare and contrast the performance of 7 node-based (as opposed to edge-based) genetic connectivity metrics. We found that two metrics in particular, the average edge weight of a node and the average inverse edge weight of a node, varied linearly with simulated connectivity. We demonstrated the use of average inverse edge weight to describe the influence of at-site habitat characteristics on genetic connectivity of American martens (Martes americana) in Ontario, Canada.
Landscape connectivity for wildlife: development and validation of multispecies linkage maps
The ability to identify regions of high functional connectivity for multiple wildlife species is of conservation interest with respect to habitat management and corridor planning. We present a method that does not require independent, field-collected data, is insensitive to the placement of source and destination sites (nodes) for modeling connectivity, and does not require the selection of a focal species. In the first step of our approach, we created a cost surface that represented permeability of the landscape to movement for a suite of species. We randomly selected nodes around the perimeter of the buffered study area and used circuit theory to connect pairs of nodes. When the buffer was removed, the resulting current density map represented, for each grid cell, the probability of use by moving animals. We tested our method by creating a map of connectivity in the Algonquin to Adirondack region in eastern North America, and we validated the map with independently collected data. We found that amphibians and reptiles were more likely to cross roads in areas of high current density, and fishers (Pekania pennanti) used areas with high current density within their home ranges.
Publications
Koen EL, J Bowman, and PJ Wilson. 2016. Node-based measures of connectivity in genetic networks. Molecular Ecology Resources 16(1): 69-79. Link
Koen EL, J Bowman, C Sadowski, and AA Walpole. 2014. Landscape connectivity for wildlife: development and validation of multi-species linkage maps. Methods in Ecology and Evolution 5(7): 626-633. Link
Koen EL, J Bowman, CJ Garroway, and PJ Wilson. (2013). The sensitivity of genetic connectivity measures to unsampled and under-sampled sites. PLoS ONE 8(2): e56204. Link
Koen EL, J Bowman, and AA Walpole (2012) The effect of cost surface parameterization on landscape resistance estimates. Molecular Ecology Resources 12:686-696. Link
Koen EL, CJ Garroway, PJ Wilson, and J Bowman (2010) The effect of map boundary on estimates of landscape resistance to animal movement. PLoS ONE 5(7): e11785. Link
The effect of cost surface parameterization on landscape resistance estimates
A cost surface is a representation of a landscape’s permeability to animal movement or gene flow and is a tool for measuring functional connectivity in landscape ecology and genetics studies. Parameterizing cost surfaces by assigning weights to different landscape elements has been challenging however, because true costs are rarely known. Assigning weights would be made easier if the sensitivity of different landscape resistance estimates to relative costs was known. We carried out a sensitivity analysis of three methods to parameterize a cost surface and two models of landscape permeability.
The effect of map boundary on estimates of landscape resistance to animal movement
Artificial boundaries on a map occur when the map extent does not cover the entire area of study; edges on the map do not exist on the ground. These artificial boundaries might bias the results of animal dispersal and landscape connectivity models by creating artificial barriers to movement for model organisms where there are no barriers for real organisms. We found that artificial boundaries result in an overestimate of landscape resistance. We then proposed and tested a solution to artificially inflated resistance values whereby we placed a buffer around the artificial boundary as a substitute for the true, but unknown, habitat.
The sensitivity of genetic connectivity measures to unsampled and under-sampled sites
Landscape genetic analyses assess the influence of landscape structure on genetic differentiation. It is rarely possible to collect genetic samples from all individuals on the landscape and thus it is important to assess the sensitivity of landscape genetic analyses to the effects of unsampled and under-sampled sites. Network-based measures of genetic distance, such as conditional genetic distance, might be particularly sensitive to sampling intensity because pairwise estimates are relative to the entire network. We addressed this question by subsampling microsatellite data from two empirical datasets.
Node-based measures of connectivity in genetic networks
At-site environmental conditions can have strong influences on genetic connectivity, and in particular on the immigration and settlement phases of dispersal. However, at-site processes are rarely explored in landscape genetic analyses. Networks can facilitate the study of at-site processes, where network nodes are used to model site-level effects. We used simulated genetic networks to compare and contrast the performance of 7 node-based (as opposed to edge-based) genetic connectivity metrics. We found that two metrics in particular, the average edge weight of a node and the average inverse edge weight of a node, varied linearly with simulated connectivity. We demonstrated the use of average inverse edge weight to describe the influence of at-site habitat characteristics on genetic connectivity of American martens (Martes americana) in Ontario, Canada.
Landscape connectivity for wildlife: development and validation of multispecies linkage maps
The ability to identify regions of high functional connectivity for multiple wildlife species is of conservation interest with respect to habitat management and corridor planning. We present a method that does not require independent, field-collected data, is insensitive to the placement of source and destination sites (nodes) for modeling connectivity, and does not require the selection of a focal species. In the first step of our approach, we created a cost surface that represented permeability of the landscape to movement for a suite of species. We randomly selected nodes around the perimeter of the buffered study area and used circuit theory to connect pairs of nodes. When the buffer was removed, the resulting current density map represented, for each grid cell, the probability of use by moving animals. We tested our method by creating a map of connectivity in the Algonquin to Adirondack region in eastern North America, and we validated the map with independently collected data. We found that amphibians and reptiles were more likely to cross roads in areas of high current density, and fishers (Pekania pennanti) used areas with high current density within their home ranges.
Publications
Koen EL, J Bowman, and PJ Wilson. 2016. Node-based measures of connectivity in genetic networks. Molecular Ecology Resources 16(1): 69-79. Link
Koen EL, J Bowman, C Sadowski, and AA Walpole. 2014. Landscape connectivity for wildlife: development and validation of multi-species linkage maps. Methods in Ecology and Evolution 5(7): 626-633. Link
Koen EL, J Bowman, CJ Garroway, and PJ Wilson. (2013). The sensitivity of genetic connectivity measures to unsampled and under-sampled sites. PLoS ONE 8(2): e56204. Link
Koen EL, J Bowman, and AA Walpole (2012) The effect of cost surface parameterization on landscape resistance estimates. Molecular Ecology Resources 12:686-696. Link
Koen EL, CJ Garroway, PJ Wilson, and J Bowman (2010) The effect of map boundary on estimates of landscape resistance to animal movement. PLoS ONE 5(7): e11785. Link