A Metacommunity Framework for Enhancing the Effectiveness of Biological Monitoring Strategies

<div><p>Because of inadequate knowledge and funding, the use of biodiversity indicators is often suggested as a way to support management decisions. Consequently, many studies have analyzed the performance of certain groups as indicator taxa. However, in addition to knowing whether certain groups can adequately represent the biodiversity as a whole, we must also know whether they show similar responses to the main structuring processes affecting biodiversity. Here we present an application of the metacommunity framework for evaluating the effectiveness of biodiversity indicators. Although the metacommunity framework has contributed to a better understanding of biodiversity patterns, there is still limited discussion about its implications for conservation and biomonitoring. We evaluated the effectiveness of indicator taxa in representing spatial variation in macroinvertebrate community composition in Atlantic Forest streams, and the processes that drive this variation. We focused on analyzing whether some groups conform to environmental processes and other groups are more influenced by spatial processes, and on how this can help in deciding which indicator group or groups should be used. We showed that a relatively small subset of taxa from the metacommunity would represent 80% of the variation in community composition shown by the entire metacommunity. Moreover, this subset does not have to be composed of predetermined taxonomic groups, but rather can be defined based on random subsets. We also found that some random subsets composed of a small number of genera performed better in responding to major environmental gradients. There were also random subsets that seemed to be affected by spatial processes, which could indicate important historical processes. We were able to integrate in the same theoretical and practical framework, the selection of biodiversity surrogates, indicators of environmental conditions, and more importantly, an explicit integration of environmental and spatial processes into the selection approach.</p> </div

Adjusted canonical coefficients of determination associated with the “pure effects” of predictors on the predetermined indicator taxa and random subsets.

<p>(A) Pure environmental fraction; (B) Pure spatial fraction. Gray triangle: ephemeropterans; gray square: trichopterans; inverted gray triangle: chironomids; black triangle: coleopterans; black square: EPT; inverted black triangle: EPTC.</p

Congruence between predetermined indicator taxa and random subsets with the entire metacommunity.

<p>(A) In the main patterns in community composition; (B) Constrained by environmental variables; (C) Constrained by spatial variables. Gray triangle: ephemeropterans; gray square: trichopterans; inverted gray triangle: chironomids; black triangle: coleopterans; black square: EPT; inverted black triangle: EPTC.</p

Congruence in environmentally constrained ordination axes (extracted from a pRDA) between each predetermined indicator taxon (indicated by the arrow) and between the 1,000 random subsets with the entire metacommunity.

<p>Random subsets have the same genus richness as the predetermined indicator taxon under comparison. Results regarding the congruence in spatially constrained ordination axes were very similar to that shown in this figure, and are not presented because of considerations of space.</p

Congruence in community composition between each predetermined indicator taxon (indicated by the arrow) and between the 1,000 random subsets with the entire metacommunity.

<p>Random subsets have the same genus richness as the predetermined indicator taxon under comparison.</p

Adjusted canonical coefficients of determination associated with the “pure effects” of environmental predictors on each predetermined indicator taxon (indicated by the arrow) and random subsets.

<p>Random subsets have the same genus richness as the predetermined indicator taxon under comparison. Results regarding “pure effects” of spatial predictors were very similar to the one shown in this figure, and are not presented because of considerations of space.</p

Appendix C. Tests of models assumptions.

Tests of models assumptions

Summary of final generalized linear mixed effects models for each response variable.

<p>Summary of final generalized linear mixed effects models for each response variable.</p

SADIE spatial aggregation index, <i>I</i>_a (<i>P</i>-value), association analysis of local cluster indices of seeds and seedling in bamboo (B) and non-bamboo (NB) stands in two sampling periods.

<p>SADIE spatial aggregation index, <i>I</i>_a (<i>P</i>-value), association analysis of local cluster indices of seeds and seedling in bamboo (B) and non-bamboo (NB) stands in two sampling periods.</p

Estimators of species richness (Chao2, Jackknife 1, Jackknife 2 and Bootstrap) for different sampling periods (2004–2005 and 2007–2009) and habitats (bamboo and non-bamboo).

<p>Estimators of species richness (Chao2, Jackknife 1, Jackknife 2 and Bootstrap) for different sampling periods (2004–2005 and 2007–2009) and habitats (bamboo and non-bamboo).</p