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Can the cause of aggregation be inferred from species distributions?
Author(s) -
Van Teeffelen Astrid J.A.,
Ovaskainen Otso
Publication year - 2007
Publication title -
oikos
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.672
H-Index - 179
eISSN - 1600-0706
pISSN - 0030-1299
DOI - 10.1111/j.2006.0030-1299.15131.x
Subject(s) - occupancy , rigour , computer science , population , data aggregator , species distribution , data mining , ecology , biology , habitat , mathematics , computer network , demography , geometry , wireless sensor network , sociology
Species distributions often show an aggregated pattern, which can be due to a number of endo‐ and exogenous factors. While autologistic models have been used for modelling such data with statistical rigour, little emphasis has been put on disentangling potential causes of aggregation. In this paper we ask whether it is possible to infer sources of aggregation in species distributions from a single set of occurrence data by comparing the performance of various autologistic models. We create simulated data sets, which show similar occupancy patterns, but differ in the process that causes the aggregation. We model the distribution of these data with various autologistic models, and show how the relative performance of the models is sensitive to the factor causing aggregation in the data. This information can be used when modelling real species data, where causes of aggregation are typically unknown. To illustrate, we use our approach to assess the potential causes of aggregation in data of seven bird species with contrasting statistical patterns. Our findings have important implications for conservation, as understanding the mechanisms that drive population fluctuations in space and time is critical for the development of effective management actions for long‐term conservation.