Micheline Manseau

Spatial Graphs: Principles and Applications for Habitat Connectivity

A BSTRACTWell-founded methods to assess habitat connectivity are essential to inform land management decisions that include conservation and restoration goals. Indeed, to be able to develop a conservation plan that maintains animal movement through a fragmented landscape, spatial locations of habitat and paths among them need to be represented. Graph-based approaches have been proposed to determine paths among habitats at various scales and dispersal movement distances, and balance data requirements with information content. Conventional graphs, however, do not explicitly maintain geographic reference, reducing communication capacity and utility of other geo-spatial information. We present spatial graphs as a unifying theory for applying graph-based methods in a geographic context. Spatial graphs integrate a geometric reference system that ties patches and paths to specific spatial locations and spatial dimensions. Arguably, the complete graph, with paths between every pair of patches, may be one of the most relevant graphs from an ecosystem perspective, but it poses challenges to compute, process and visualize. We developed Minimum Planar Graphs as a spatial generalization of Delaunay triangulations to provide a reasonable approximation of complete graphs that facilitates visualization and comprehension of the network of connections across landscapes. If, as some authors have suggested, the minimum spanning tree identifies the connectivity “backbone” of a landscape, then the Minimum Planar Graph identifies the connectivity “network”. We applied spatial graphs, and in particular the Minimum Planar Graph, to analyze woodland caribou habitat in Manitoba, Canada to support the establishment of a national park.

Indigenous Knowledge and Values in Planning for Sustainable Forestry: Pikangikum First Nation and the Whitefeather Forest Initiative

Although still posing challenges, science-based knowledge (including interdisciplinary work) is leading current forest-management planning. How then can indigenous communities mobilize their own knowledge to support their desire to develop new ways of managing the forest? In northern Ontario, the provincial government has developed a cross-scale planning approach that allocates certain responsibilities to First Nations in order to support their vision and knowledge, yet at the same time addresses provincial planning goals. Within this context, research on woodland caribou ( Rangifer tarandus) was conducted in collaboration with Pikangikum First Nation to support their participation in forest-management planning. The outcomes of this research are used as a focal point for discussing some of the stressors that influence cross-scale planning for forestry in northern Ontario. The paper concludes that resolving cultural differences in a forest- management planning context is not entirely necessary to move forward with collaborative planning for the conservation of woodland caribou habitat.

Allelematch: an R package for identifying unique multilocus genotypes where genotyping error and missing data may be present

We present allelematch, an R package, to automate the identification of unique multilocus genotypes in data sets where the number of individuals is unknown, and where genotyping error and missing data may be present. Such conditions commonly occur in noninvasive sampling protocols. Output from the software enables a comparison of unique genotypes and their matches, and facilitates the review of differences between profiles. The software has a variety of applications in molecular ecology, and may be valuable where a large number of samples must be processed, unique genotypes identified, and repeated observations made over space and time. We used simulations to assess the performance of allelematch and found that it can reliably and accurately determine the correct number of unique genotypes (±3%) across a broad range of data set properties. We found that the software performs with highest accuracy when genotyping error is below 4%. The R package is available from the Comprehensive R Archive Network (http://cran.r‐project.org/). Supplementary documentation and tutorials are provided.

Natural regulation of cervidae along a 1000 km latitudinal gradient: Change in trophic dominance

SummaryThe biomass of forage, herbivores (caribou and moose) and predators (wolf) were estimated for four assemblages of large mammals along a latitudinal gradient in the Québec-Labrador peninsula and related to predictions made by two types of multitrophic level models. Wolves were present in three study areas, but they had been extirpated in the last one. Annual production of preferred forage exhibited a clear north-south increase for moose, but not for caribou. Neither the herbivore nor predator biomass increased along the latitudinal gradient: the highest herbivore biomass occurred in the wolf-free area and in the northernmost site, while the greatest predator density was observed in the southernmost site. Consequently, the ratio of the herbivore to forage biomass was the highest in the area devoid of wolves and in the northernmost site occupied by migratory caribou. Availability of forage per herbivore was the greatest in the moose-wolf and the caribou-moose-wolf assemblages. The observed data supported the multitrophic level model incorporating classical predator-prey relationships and producing stepwise accrual of trophic level biomass with increasing food chain length. In the northernmost site, the system was limited to two functional trophic levels and caribou were regulated by summer forage. Three functional trophic levels appeared to exist in the central study area where caribou and moose were preyed upon by wolves. Both herbivores were at very low density, the first one due probably to its poor adaptation to predation and the second because of an unproductive range. In the southernmost site, moose were clearly regulated by predation and kept much below the carrying capacity. With the extirpation of wolves in the last study area, moose were regulated by forage and the density exceeded that in the moose-wolf system by seven times even in a less productive range. Caribou, having primarily evolved under resource limitation, is replaced by a cervid better adapted to predation, the moose, in more productive ‘three-link’ ecosystems.

Grains of connectivity: analysis at multiple spatial scales in landscape genetics

Landscape genetic analyses are typically conducted at one spatial scale. Considering multiple scales may be essential for identifying landscape features influencing gene flow. We examined landscape connectivity for woodland caribou (Rangifer tarandus caribou) at multiple spatial scales using a new approach based on landscape graphs that creates a Voronoi tessellation of the landscape. To illustrate the potential of the method, we generated five resistance surfaces to explain how landscape pattern may influence gene flow across the range of this population. We tested each resistance surface using a raster at the spatial grain of available landscape data (200 m grid squares). We then used our method to produce up to 127 additional grains for each resistance surface. We applied a causal modelling framework with partial Mantel tests, where evidence of landscape resistance is tested against an alternative hypothesis of isolation‐by‐distance, and found statistically significant support for landscape resistance to gene flow in 89 of the 507 spatial grains examined. We found evidence that major roads as well as the cumulative effects of natural and anthropogenic disturbance may be contributing to the genetic structure. Using only the original grid surface yielded no evidence for landscape resistance to gene flow. Our results show that using multiple spatial grains can reveal landscape influences on genetic structure that may be overlooked with a single grain, and suggest that coarsening the grain of landcover data may be appropriate for highly mobile species. We discuss how grains of connectivity and related analyses have potential landscape genetic applications in a broad range of systems.

Representing Traditional Knowledge: Resource Management and Inuit Knowledge of Barren-Ground Caribou

Comanagement regimes in Canadas North rarely include indigenous systems for understanding the environment. Mapped representations and accompanying narratives illustrating the collective knowledge of indigenous hunters can make unique management contributions. Both the multigenerational knowledge of indigenous communities and opportunities allowing a discussion of diverse ways of interpreting environmental observations are crucial to involving indigenous learning systems within current regional wildlife management. It is not just the factual “data” of indigenous hunters that are relevant to resource management. It is the opportunities for social learning or for resource managers to understand how indigenous hunters learn about the environment that are directly relevant to resource management decision making.

MEMGENE: Spatial pattern detection in genetic distance data

Summary 1. Landscape genetics studies using neutral markers have focused on the relationship between gene flow and landscape features. Spatial patterns in the genetic distances among individuals may reflect spatially uneven patterns of gene flow caused by landscape features that influence movement and dispersal. 2. We present a method and software for identifying spatial neighbourhoods in genetic distance data that adopts a regression framework where the predictors are generated using Moran’s eigenvectors maps (MEM), a multivariate technique developed for spatial ecological analyses and recommended for genetic applications. 3. Using simulated genetic data, we show that our MEMGENE method can recover patterns reflecting the landscape features that influenced gene flow. We also apply MEMGENE to genetic data from a highly vagile ungulate population and demonstrate spatial genetic neighbourhoods aligned with a river likely to reduce, but not eliminate, gene flow. 4. We developed the MEMGENE package for R in order to detect and visualize relatively weak or cryptic spatial genetic patterns and aid researchers in generating hypotheses about the ecological processes that may underlie these patterns. MEMGENE provides a flexible set of R functions that can be used to modify the analysis. Detailed supplementary documentation and tutorials are provided.

Integrating multiple analytical approaches to spatially delineate and characterize genetic population structure: an application to boreal caribou ( Rangifer tarandus caribou ) in central Canada

Individual-based clustering (IBC) methods have become increasingly popular for the characterization and delineation of genetic population units for numerous species. These methods delineate populations based on the genetic assumptions of a breeding unit which may provide a better representation of the behaviour of the species. The increasing use of IBC has resulted in the development of several analytical models all of which vary in their theoretical assumptions to infer genetic population structure. In this paper, we report a comparative strategy utilizing three IBC methods to characterize the spatial genetic structure of the boreal population of woodland caribou (Rangifer tarandus caribou) in central Canada. In addition, we implement both tests for isolation-by-distance (IBD) and frequency-based assignment tests to validate the consensus genetic clusters as defined by IBC. We also compare indirect metrics of genetic diversity and gene flow using both a priori defined herds and the IBC defined populations. Although our results show some concordance between both pre-defined herds and IBC derived genetic clusters, the IBC analyses identified a cluster that was cryptic to observation-based caribou herds and found no difference between several adjacent herds. By comparing multiple IBC methods and integrating both IBD and indirect genetic diversity metrics a posteriori, our strategy provides an effective means to delineate wildlife population structure and accurately assess genetic diversity and connectivity.

Phylogeographical analysis of mtDNA data indicates postglacial expansion from multiple glacial refugia in woodland caribou (Rangifer tarandus caribou).

Glacial refugia considerably shaped the phylogeographical structure of species and may influence intra-specific morphological, genetic, and adaptive differentiation. However, the impact of the Quaternary ice ages on the phylogeographical structure of North American temperate mammalian species is not well-studied. Here, we surveyed ∼1600 individuals of the widely distributed woodland caribou (Rangifer tarandus caribou) using mtDNA control region sequences to investigate if glacial refugia contributed to the phylogeographical structure in this subspecies. Phylogenetic tree reconstruction, a median-joining network, and mismatch distributions supported postglacial expansions of woodland caribou from three glacial refugia dating back to 13544–22005 years. These three lineages consisted almost exclusively of woodland caribou mtDNA haplotypes, indicating that phylogeographical structure was mainly shaped by postglacial expansions. The putative centres of these lineages are geographically separated; indicating disconnected glacial refugia in the Rocky Mountains, east of the Mississippi, and the Appalachian Mountains. This is in congruence with the fossil record that caribou were distributed in these areas during the Pleistocene. Our results suggest that the last glacial maximum substantially shaped the phylogeographical structure of this large mammalian North American species that will be affected by climatic change. Therefore, the presented results will be essential for future conservation planning in woodland caribou.

Finding the functional grain: comparing methods for scaling resistance surfaces

The influence of landscape features on the movement of an organism between two point locations is often measured as an effective distance. Typically, raster models of landscape resistance are used to calculate effective distance. Because organisms may experience landscape heterogeneity at different scales (i.e. functional grains), using a raster with too fine or too coarse a spatial grain (i.e. analysis grain) may lead to inaccurate estimates of effective distance. We adopted a simulation approach where the true functional grain and effective distance for a theoretical organism were defined and the analysis grains of landscape connectivity models were systematically changed. We used moving windows and grains of connectivity, a recently introduced landscape graph method that uses an irregular tessellation of the resistance surface to coarsen the landscape data. We then used least-cost path metrics to measure effective distance and found that matching the functional and analysis grain sizes was most accurate at recovering the expected effective distance, affirming the importance of multi-scale analysis. Moving window scaling with a maximum function (win.max) performed well when the majority of landscape structure influencing connectivity consisted of high resistance features. Moving window scaling with a minimum function (win.min) performed well when the relevant landscape structure consisted of low resistance regions. The grains of connectivity method performed well under all scenarios, avoiding an a priori choice of window function, which may be challenging in complex landscapes. Appendices are provided that demonstrate the use of grains of connectivity models.