The North American beaver (C. canadensis) is a keystone species of ecological and biocultural importance in Mi’kma’ki (Nova Scotia). Working tirelessly as unpaid engineers of our environment, C. canadensis use organic materials to build dams in watercourses, modifying wetland habitat, supporting ecological communities, and enhancing ecosystem services.
As a generalist species, specific environmental conditions and food sources which drive C. canadensis distribution vary greatly throughout their continental range, prompting the need for local study. In Nova Scotia, occurrence data have never been systematically collected as C. canadensis is a common and unthreatened species, resulting in geographic knowledge gaps. With increasing pressures from climate change and human footprint on wetland ecosystems and biodiversity, it is critical to understand local factors governing their distribution as wetland stewards.
This project augmented poor-quality data in Nova Scotia using Maximum Entropy modelling (Maxent). Input layers consisted of predominantly citizen-science occurrence data, and environmental covariates characterized geomorphology and forest composition. Correlation analysis and reverse stepwise elimination were used to generate two models: an ecological model, and a human footprint model, where the latter investigated the influence of human footprint, adding an index layer from Hirsh-Pearson et al. (2022) representing anthropogenic disturbance.
The ecological model produced a high area under the receiver operating characteristic curve (AUC) on test data for ten replicated runs (0.80 +/- 0.02), though the human footprint model produced a higher AUC on test data (0.83 +/- 0.04), likely due to the added human footprint layer improving the ability of the model to predict omitted citizen-science collected occurrence points.
In a validation effort, a spatial data layer representing beaver flowage ponds from the Nova Scotia Forest Inventory was used to sample the ecological model in areas of historic beaver occupancy, revealing that predicted distribution and historic occupancy overlap at a landscape-scale.
The model outputs indicated that proximity to watercourses, low elevation, proximity to aspen, and distance from red oak and gray birch dominant forest stands were important habitat associations, consistent with many previous findings throughout North America.
|Telling the North American beaver tale: modelling distribution in Mi’kma’ki (Nova Scotia, Canada)
|Dalhousie University, Westwood Lab
|Maxent SDMs reveal habitat associations in NS and influence of human footprint on beaver distribution.
|1) 300m raster of predicted probability of occurrence for beaver in NS using geomorphological and forest composition covariates and 2) 300m raster of predicted probability of occurrence for beaver in NS using geomorphological and forest composition covariates with an addition of a human footprint layer
|output is open use
|Output models compared to beaver flowage layer interpreted from aerial imagery