Community modelling
Introduction
It is increasingly recognized that conservation planning and management for biodiversity cannot be met by focusing on one species at a time, but requires the adoption of multispecies or entire-community approaches. Community-level modelling combines distributions from several species to produce synthetic representations of the spatial pattern in the distribution of biodiversity at the community level (Ferrier & Guisan 2006).
Three modeling approaches
Three modelling approaches have been proposed:
'Assemble first, predict later': Species distributions are first combined with classification or ordination methods and the resulting assemblages are then modelled using correlative approaches.
'Predict first, assemble later': Individual species distributions are modelled first and the resulting potential species distributions are then combined.
'Assemble and predict together': Species distribution models are fitted using both environmental predictors and information on species co-occurrence.
According to Baselga & Araújo (2010), the three approaches are rooted in different concepts of community ecology - the Clementsian and the Gleasonian - and therefore represent different hypotheses of the mechanisms driving variation in the composition of assemblages. The Clementsian concept views communities as rigid combinations of co-occurring species, and thus underlies the 'assemble first, predict later' approach. In contrast, the 'predict first, assemble later' approach can be interpreted as a formalization of the Gleasonian concept, which views assemblages as the result of collective individualistic responses of species to abiotic factors. Finally, the 'assemble and predict together' strategy assumes the interactions between species, but it avoids the two extremes. Perhaps the greatest hurdle in community-wide modelling is that communities are composed of both co-occurring groups of species and species arranged independently along environmental gradients (Olden et al. 2006).
Tests assessing the merits of the three community-based modelling strategies are scarce and the results are inconclusive.
For example, Ferrier et al. (2002) found no major differences between the ‘assemble first, predict later’ and the ‘predict first, assemble later’ strategies. Olden et al. (2006) found that a particular ‘assemble and predict together’ strategy (implemented with a multi-response artificial neural network, MANN) outperformed the predictive capacity of two alternative community-level modelling strategies (implemented with logistic regression and multiple discriminant analysis, respectively). However, because different methods were used in this latter study it is difficult to know whether differences between model outputs arose because different algorithms were used or because of differences in the conceptual underpinning of the models. Ideally, if the goal is to assess the conceptual implications of alternative modelling strategies, the algorithms should be standardized to ensure comparability. When such standardizationwas carried out, the overall accuracy of communitybased strategies (i.e. ‘assemble and predict together’) was reduced compared with that of familiar individualistic models (‘predict first, assemble later’) (Baselga & Araújo 2009).
Discussion
Baselga & Araújo (2010) compare the three approaches at the European levels, using 50x50 km grid cells. At this coarse scale, biogeographical processes are the prevailing ones. Furthermore, interactions between species, if they exist, occur at much finer scale.
Bibliography
Baselga, A. & Araujo, M. B. (2009) Individualistic vs community modelling of species distributions under climate change. Ecography, 32, 55.
Baselga, A. & Araújo, M. (2010) Do community-level models describe community variation effectively? Journal of Biogeography, 37, 1842-1850.
Ferrier, S. & Guisan, A. (2006) Spatial modelling of biodiversity at the community level. Ecology, 43, 393-404.
Olden, J., Joy, M. & Death, R. (2006) Rediscovering the species in community-wide predictive modeling. Ecological Applications, 16, 1449-1460.