Field-validated species distribution model of Canada Warbler (Cardellina canadensis) in Northwestern Ontario

Description

The Canada Warbler (CAWA) is a species of conservation concern, but its ecological needs and distribution remain poorly understood. Additionally, contradictory findings exist regarding the impact of logging on CAWA abundance and habitat use. Furthermore, its habitat needs may be distorted by limitations in current habitat availability compared to historical conditions. We developed a predictive high-resolution (30 m) field-validated species distribution model (SDM) in Ecoregion 4W of Northwestern Ontario, Canada, where little field-derived information about the species is available. We aimed to assess how time since logging affects CAWA occurrence and distribution. The SDM was built on occurrences from various large datasets (including eBird (2000-2021), Breeding Bird Survey (2000-2019), and Ontario Breeding Bird Atlas (2000-2005) and data from long-term songbird monitoring of Quetico Provincial Park (2014-2019), and we supplemented the dataset with our field collected data from the breeding season of 2021. We filtered the dataset excluding inaccurate coordinates, sites within 250 m, and sites located on the cloud range of Landsat images used. We got a total of 122 observations from 2001 to 2021. The SDM’s environmental covariates included a bare soil index (BASI), a normalized water index (NDWI), an enhanced vegetation index (EVI), a digital elevation model (DEM), years of forest loss (LOSS [usually by logging] ) for forests ˂20 yr old, distance to mature coniferous forest (D_CONIF), and tree canopy height (CAN). We field-validated the model using targeted data collected (30 observations) in the breeding season of 2022.

Steps to reproduce

With the Maxent (java) console, run the species distribution models for Canada Warbler using the occurrences available and the covariates for the Ecoregion 4W of Northwestern Ontario. For the first model use the dataset of 2001-2021 for training data and 25% of it as test data (random seed) in 10 cross-validated replicates. For a second and field-validated model, use the dataset 2001-2021 as training data and the 2022 dataset as test data in 10 cross-validated replicates. You can contrast and verify the results with the output layer and the compiled results provided here for both models (the model using only the 2001-2021 dataset and the one validated using the 2022 dataset)..

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