Downscaling species occupancy from coarse spatial scales

Area of Science:

• Spatial Ecology
• Conservation Biology
• Statistical Ecology

Background:

• Accurate species population measurement and prediction across spatial scales are essential for ecological research and conservation efforts.
• Current methods for downscaling species occupancy often focus on species' distributional traits, with limited attention to spatial features.
• Predicting fine-scale occupancy from coarse-scale data presents significant challenges in spatial ecology.

Purpose of the Study:

• To introduce and evaluate shot noise Cox processes (SNCP) for modeling species occupancies at various spatial scales.
• To demonstrate the critical role of species' spatial aggregation in downscaling population estimates.
• To compare the predictive performance of different spatial models for occupancy.

Main Methods:

• Employed a class of spatial point processes, specifically shot noise Cox processes (SNCP), formulated in continuous space.
• Calibrated nine different models at regional scales using species presence and absence data.
• Assessed model performance in predicting species occupancy at finer spatial scales.

Main Results:

• Species' spatial aggregation is a crucial factor for accurately predicting population estimates at finer scales from coarser ones.
• A specific type of SNCP, the Thomas process, significantly outperformed other tested models.
• The Thomas process successfully predicted occupancies at scales four orders of magnitude smaller than the calibration scales.

Conclusions:

• The proposed SNCP framework, particularly the Thomas process, offers a powerful tool for inferring spatially explicit information from implicit measures.
• This approach facilitates the integration of niche and spatial models for enhanced ecological predictions.
• The methodology allows for the potential reversal to upscale species occupancy predictions.