Rediscovering the species in community-wide predictive modeling

Broadening the scope of conservation efforts to protect entire communities provides several advantages over the current species-specific focus, yet ecologists have been hampered by the fact that predictive modeling of multiple species is not directly amenable to traditional statistical approaches. Perhaps the greatest hurdle in community-wide modeling is that communities are composed of both co-occurring groups of species and species arranged independently along environmental gradients. Therefore, commonly used "short-cut" methods such as the modeling of so-called "assemblage types" are problematic. Our study demonstrates the utility of a multiresponse artificial neural network (MANN) to model entire community membership in an integrative yet species-specific manner. We compare MANN to two traditional approaches used to predict community composition: (1) a species-by-species approach using logistic regression analysis (LOG) and (2) a "classification-then-modeling" approach in which sites are classified into assemblage "types" (here we used two-way indicator species analysis and multiple discriminant analysis [MDA]). For freshwater fish assemblages of the North Island, New Zealand, we found that the MANN outperformed all other methods for predicting community composition based on multiscaled descriptors of the environment. The simple-matching coefficient comparing predicted and actual species composition was, on average, greatest for the MANN (91%), followed by MDA (85%), and LOG (83%). Mean Jaccard's similarity (emphasizing model performance for predicting species' presence) for the MANN (66%) exceeded both LOG (47%) and MDA (46%). The MANN also correctly predicted community composition (i.e., a significant proportion of the species membership based on a randomization procedure) for 82% of the study sites compared to 54% (MDA) and 49% (LOG), resulting in the MANN correctly predicting community composition in a total of 311 sites and an additional 117 sites (n = 379), on average, compared to LOG and MDA. The MANN also provided valuable explanatory power by simultaneously quantifying the nature of the relationships between the environment and both individual species and the entire community (composition and richness), which is not readily available from traditional approaches. We discuss how the MANN approach provides a powerful quantitative tool for conservation planning and highlight its potential for biomonitoring programs that currently depend on modeling discrete assemblage types to assess aquatic ecosystem health.

Movement behaviour and dynamics of an aquatic insect in a stream benthic landscape

Understanding not just where organisms move but how they move is an important step towards integrating animal behaviour into landscape ecology. The three-dimensional landscape of a streambed provides an ideal setting for forging this integration because of the persuasive effects of flowing water. In this study, we experimentally examine the larval movement of the case-building caddisfly Agapetus boulderensis Milne, 1936 in response to two current velocities in each of five levels of contrasting habitat types (i.e., smooth patches that facilitate movement and thick algal patches that constrain movement). Detailed behavioural observations showed that larvae employed two distinctly different strategies of movement in different current velocities: faster crawling and slower pivoting. Our results suggest that individual decision-making between crawling and pivoting is related to the magnitude of current velocity across the streambed, and the frequency at which larvae employ these behaviours translates into differential movement rates and directions. Strong concordance between a conceptual model and our results supports the notion that the presence of structural "nonhabitat" patches at high current velocities may create areas of local flow interruption and refugia. This, in turn, plays an important role in eliciting either crawling or pivoting and in shaping patterns and directions of larval movement, and by extension resource acquisition.

Toward a mechanistic understanding and prediction of biotic homogenization

The widespread replacement of native species with cosmopolitan, nonnative species is homogenizing the global fauna and flora. While the empirical study of biotic homogenization is substantial and growing, theoretical aspects have yet to be explored. Consequently, the breadth of possible ecological mechanisms that can shape current and future patterns and rates of homogenization remain largely unknown. Here, we develop a conceptual model that describes 14 potential scenarios by which species invasions and/or extinctions can lead to various trajectories of biotic homogenization (increased community similarity) or differentiation (decreased community similarity); we then use a simulation approach to explore the model's predictions. We found changes in community similarity to vary with the type and number of nonnative and native species, the historical degree of similarity among the communities, and, to a lesser degree, the richness of the recipient communities. Homogenization is greatest when similar species invade communities, causing either no extinction or differential extinction of native species. The model predictions are consistent with current empirical data for fish, bird, and plant communities and therefore may represent the dominant mechanisms of contemporary homogenization. We present a unifying model illustrating how the balance between invading and extinct species dictates the outcome of biotic homogenization. We conclude by discussing a number of critical but largely unrecognized issues that bear on the empirical study of biotic homogenization, including the importance of spatial scale, temporal scale, and data resolution. We argue that the study of biotic homogenization needs to be placed in a more mechanistic and predictive framework in order for studies to provide adequate guidance in conservation efforts to maintain regional distinctness of the global biota.

Assessing the robustness of randomization tests: examples from behavioural studies

Behavioural studies are commonly plagued with data that violate the assumptions of parametric statistics. Consequently, classic nonparametric methods (e.g. rank tests) and novel distribution-free methods (e.g. randomization tests) have been used to a great extent by behaviourists. However, the robustness of such methods in terms of statistical power and type I error have seldom been evaluated. This probably reflects the fact that empirical methods, such as Monte Carlo approaches, are required to assess these concerns. In this study we show that analytical methods cannot always be used to evaluate the robustness of statistical tests, but rather Monte Carlo approaches must be employed. We detail empirical protocols for estimating power and type I error rates for parametric, nonparametric and randomization methods, and demonstrate their application for an analysis of variance and a regression/correlation analysis design. Together, this study provides a framework from which behaviourists can compare the reliability of different methods for data analysis, serving as a basis for selecting the most appropriate statistical test given the characteristics of data at hand. Copyright 2001 The Association for the Study of Animal Behaviour.