Downscaling species occupancy from coarse spatial scales

Abstract

Abstract. The measurement and prediction of species' populations at different spatial scales is crucial to spatial ecology as well as conservation biology. An efficient yet challenging goal to achieve such population estimates consists of recording empirical species' presence and absence at a specific regional scale and then trying to predict occupancies at finer scales. So far the majority of the methods have been based on particular species' distributional features deemed to be crucial for downscaling occupancy. However, only a minority of them have dealt explicitly with specific spatial features. Here we employ a wide class of spatial point processes, the shot noise Cox processes (SNCP), to model species occupancies at different spatial scales and show that species' spatial aggregation is crucial for predicting population estimates at fine scales starting from coarser ones. These models are formulated in continuous space and locate points regardless of the arbitrary resolution that one employs to study the spatial pattern. We compare the performances of nine models, calibrated at regional scales and demonstrate that a very simple class of SNCP, the Thomas process, is able to outperform other published models in predicting occupancies down to areas four orders of magnitude smaller than the ones employed for the parameterization. We conclude by explaining the ability of the approach to infer spatially explicit information from spatially implicit measures, the potential of the framework to combine niche and spatial models, and the possibility of reversing the method to allow upscaling.

Spatially-Explicit Estimation of Geographical Representation in Large-Scale Species Distribution Datasets

“…This fits the prediction that species of higher latitudes, such as those of a plant atlas of north European species, have a larger distribution range [1]. These results indicate that the mapping protocols themselves did not affect the results of the species occupancy frequency comparison, and that resampling the Hultén & Fries atlas to the 50-km spatial resolution of the AFE was appropriate; two important prerequisites when comparing or merging atlas datasets [9], [44].…”

Comparative analysis of abundance–occupancy relationships for species at risk at both broad taxonomic and spatial scales

“…Consequently, the development of accurate and time-efficient methods can be effective in enabling scientists, managers, and policymakers to optimize species' conservation strategies. Species-independent abundance-occupancy relationships, such as those documented here, might also provide informative tools in forecasting changes in abundance with change in habitat (He and Gaston 2000;Hui et al 2009Hui et al , 2012Azaele et al 2012). Generalized abundance-occupancy relationships could also serve as a first step in developing species-independent recovery strategies.…”

The need for an integrated biodiversity policy support process – Building the European contribution to a global Biodiversity Observation Network (EU BON)

“…Bradter et al 2011). The project will also help to improve access to novel techniques for downscaling species' distributional information (Azaele et al 2012) and upscaling biodiversity data, two key challenges in the application of biodiversity datasets in conservation planning. We also work on developing enhanced species distribution models, also called environmental niche models, to better incorporate information on spatial patterning .…”