ESR 52:81-95 (2023)  -  DOI: https://doi.org/10.3354/esr01270

ABSTRACT: Effective protection of threatened species living in highly urbanized landscapes requires detailed information on their population distribution. For species that are difficult to detect, species distribution models (SDMs) can be valuable tools for predicting their occurrence. We created an SDM to predict breeding pond locations of the endangered Jefferson salamander Ambystoma jeffersonianum in southern Ontario, the most highly developed and populated region in Canada. Using a maximum entropy modelling algorithm (Maxent), we combined known breeding pond occurrences with climate, land type, soil, and topography data to capture the ecological niche of the Jefferson salamander. Our SDM showed excellent performance (AUC = 0.919), with land type being the most important predictor variable. We produced a continuous habitat suitability map that predicted most hotspots of suitable habitat to occur along the Niagara Escarpment, with small patches in hedge rows and forest fragments in surrounding agricultural areas. Our refined presence-absence map predicted a high suitability area of 305 km² with high specificity and moderate overall accuracy. Over half of this area was within the Ontario Greenbelt, demonstrating the importance of protecting this land from future development. Our work demonstrates how SDMs can be used to inform decisions on endangered species and direct conservation efforts towards critical habitats.

KEY WORDS: Ambystoma jeffersonianum · Conservation · Ecological niche · Maxent · Ontario Greenbelt · Jefferson salamander · Species distribution modeling

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Cite this article as: Siemens AL, Bogart JP, Linton JE, Norris DR (2023) Predicting the occurrence of an endangered salamander in a highly urbanized landscape. Endang Species Res 52:81-95. https://doi.org/10.3354/esr01270 Export citation Share:    Facebook - - linkedIn