Complementary network-based approaches for exploring genetic structure and functional connectivity in two vulnerable, endemic ground squirrels

The persistence of small populations is influenced by genetic structure and functional connectivity. We used two network-based approaches to understand the persistence of the northern Idaho ground squirrel (Urocitellus brunneus) and the southern Idaho ground squirrel (U. endemicus), two congeners of conservation concern. These graph theoretic approaches are conventionally applied to social or transportation networks, but here are used to study population persistence and connectivity. Population graph analyses revealed that local extinction rapidly reduced connectivity for the southern species, while connectivity for the northern species could be maintained following local extinction. Results from gravity models complemented those of population graph analyses, and indicated that potential vegetation productivity and topography drove connectivity in the northern species. For the southern species, development (roads) and small-scale topography reduced connectivity, while greater potential vegetation productivity increased connectivity. Taken together, the results of the two network-based methods (population graph analyses and gravity models) suggest the need for increased conservation action for the southern species, and that management efforts have been effective at maintaining habitat quality throughout the current range of the northern species. To prevent further declines, we encourage the continuation of management efforts for the northern species, whereas conservation of the southern species requires active management and additional measures to curtail habitat fragmentation. Our combination of population graph analyses and gravity models can inform conservation strategies of other species exhibiting patchy distributions

Gene flow in an arctic wetland: modelling landscape effects on fine-scale genetic variation in an isolated muskrat «Ondatra zibethicus» population

This thesis evaluates the genetic diversity, the population genetic structure as well as the functional connectivity of an isolated muskrat (Ondatra zibethicus) population at the northernmost limit of its geographic distribution in North America. In the first chapter, nine microsatellite markers were used to assess the genetic characteristics of muskrats sampled from 21 different lakes spread across the Old Crow Flats (OCF), an arctic wetland complex in Northern Yukon. Genetic diversity was relatively low, while patterns of genetic variation were structured into two population clusters within the OCF, suggesting reduced levels of gene flow in arctic habitats. In chapter 2, the relationship between genetic differentiation and the connectivity of wetland features in the OCF was investigated using circuit theory to model isolation by resistance (IBR) based on landscape cover. Resistance surfaces were parameterized using a machine learning algorithm to optimize the fit between an IBR model and the observed genetic distance between lakes. The optimized IBR model was subsequently compared to the alternative models of isolation by distance (IBD) and isolation by barrier (IBB) using two complementary approaches: (1) causal modelling, and (2) partitioning of genetic variation explained by spatial eigenfunctions (Moran's eigenvector maps). The optimized IBR model revealed that hydrological features facilitated gene flow in the landscape. Gene flow through most terrestrial habitats was relatively unimpeded, though woodlands were severely obstructive. Causal modelling showed greater support for the optimized IBR model then for IBD or IBB models. Partitioning of variation suggested that both IBR and IBB models contributed unique explanatory spatial structures, while the IBD model was redundant. This thesis highlights the first muskrat population genetic structures detected at fine spatial scales and identifies the landscape variables that drive this spatial pattern using new modelling approaches.Cette thèse évalue la diversité génétique, la structure génétique ainsi the la connectivité fonctionnelle au sein d'une population isolée de rats musqués (Ondatra zibethicus) située à la limite septentrionale de leur distribution géographique en Amérique du Nord. Dans le premier chapitre, neufs marqueurs microsatellites ont été utilisés pour caractériser la variation génétique de cette population en échantillonnant 21 lacs éparpillés dans le paysage d'Old Crow Flats (OCF), un complexe de terres humides arctiques situé dans le nord du Yukon. Les résultats démontrent une diversité génétique relativement basse, en plus de révéler une structure génétique suggérant deux groupes distincts au sein de la population d'OCF. Le deuxième chapitre évalue la relation entre la différentiation génétique et la connectivité des caractéristiques hydrologiques d'OCF en utilisant la théorie des circuits pour produire un modèle d'isolement par résistance (IPR) depuis la couverture et l'hydrologie du paysage. À cet effet, les surfaces de résistances ont été paramétrées à l'aide d'un algorithme d'apprentissage optimisant la corrélation entre le modèle d'IPR et la différentiation génétique entre les lacs. Le modèle d'IPR optimisé fut ensuite comparé aux modèles alternatifs d'isolement par distance (IPD) et d'isolement par obstacle (IPO) à l'aide de deux approches complémentaires: (1) la modélisation causale et (2) le partitionnement de la variation génétique expliqué par les vecteurs propres de Moran. Le modèle optimisé d'IPR suggère que le flux génétique est facilité par les habitats aquatiques et que peu de résistance est rencontrée en milieu terrestre, quoique les milieux forestiers imposent un obstacle presque absolu au sein d'OCF. La modélisation causale appuie le modèle d'IPR au détriment des modèles d'IPD et d'IPO. Le partitionnement de la variation génétique révèle que les modèles d'IPR et d'IPO contribuent des structures spatiales explicatives presque distinctes, alors que le modèle d'IPD est complètement redondant. Cette thèse souligne, chez le rat musqué, les premières structures génétiques identifiées à fine échelle spatiale en plus d'illustrer les caractéristiques du paysage à l'origine de la différentiation génétique au sein de la population d'OCF à l'aide de nouvelles approches de modélisation

Multi-sensor detection of spring breakup phenology of Canada's lakes

The ice phenology of freshwater lakes throughout the Northern Hemisphere has undergone important climate-induced shifts over the past century. In Canada's North, where freshwater lakes and wetlands cover 15 to 40% of the landscape, monitoring ice phenology is vital to understand its impacts on climate, socio-economic, ecological, and hydrological systems. The rapid and dynamic nature of ice phenology events has restricted monitoring efforts to the use of satellite sensors with frequent revisit times (e.g., MODIS, AVHRR), but their low resolution (e.g., > 500 m) limits observations to larger water bodies. However, the increased abundance of high-resolution open-access satellite imagery combined with the rise of cloud-computing technologies has provided opportunities to reduce the trade-off between temporal (i.e., revisit time) and spatial (i.e., pixel size) resolution allowing for lake ice monitoring over broad scales. In this study, we present the Open Pixel-based Earth eNgine Ice (OPEN-ICE) algorithm implemented in Google Earth Engine (GEE), which classifies imagery from multiple open-access optical sensors, then combines them to construct dense annual time series of ice-water observations and estimate pixel spring breakup dates at a 30-m resolution. Using Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI scenes over lakes spanning northern latitudes, we build reference datasets to train decision trees that discriminate between ice, water, and clouds. We combine ice-water classifications from each sensor into annual time series and remove misclassifications with a temporal filter applied using a pixel-wise logistic regression. We then detect the sequence of transition from ice to water in each pixel's time series to estimate the occurrence of breakup each year. We deploy the OPEN-ICE algorithm over all freshwater pixels of Canada for the period of 2013 to 2021. Spring ice phenology events estimated by OPEN-ICE show high accuracy when compared to whole-lake breakup dates measured by the Canadian Ice Service in 105 lakes across 9 years, with mean bias errors of −1.10 and − 0.69 days for breakup start and end, respectively. We apply the OPEN-ICE algorithm to 4000 lakes across Canada and evaluate differences in breakup dates across ecozones and lake sizes. Our new OPEN-ICE tool provides accurate estimates of annual spring breakup events applicable across all boreal and arctic regions to monitor the rapid changes taking place in these vulnerable ecosystems.ISSN:0034-425