Size and spatial structure in deep versus shallow populations of the Mediterranean gorgonian <Emphasis Type="Italic">Eunicella singularis</Emphasis> (Cap de Creus,northwestern Mediterranean Sea)

In the Western Mediterranean Sea, the gorgonian Eunicella singularis (Esper, 1794) is found at high densities on sublittoral bottoms at depths from 10 to 70 m. Shallow colonies have symbiotic zooxanthellae that deeper colonies lack. While knowledge of the ecology of the shallow populations has increased during the last decades, there is almost no information on the ecology of the deep sublittoral populations. In October and November 2004 at Cap de Creus (42°19′12″ N; 03°19′34″ E), an analysis of video transects made by a remotely operated vehicle showed that shallow populations (10–25 m depth) were dominated by small, non-reproductive colonies, while deep sublittoral populations (50–67 m depth) were dominated by medium-sized colonies. Average and maximum colony heights were greater in the deeper populations, with these deeper populations also forming larger patch sizes and more extensive regions of continuous substrate coverage. These results suggest that shallow habitats are suitable for E. singularis, as shown by the high recruitment rate, but perturbations may limit or delay the development of these populations into a mature stage. This contrasts with the deep sublittoral habitats where higher environmental stability may allow the development of mature populations dominated by larger, sexually mature colonies.     

Estimating landscape-scale species richness: reconciling frequency- and turnover-based approaches

One hypothesis for why estimators of species richness tend to underestimate total richness is that they do not explicitly account for increases in species richness due to spatial or environmental turnover in species composition (beta diversity). I analyze the similarity of a data set of native trees in Great Smoky Mountains National Park, USA, and assess the robustness of these estimators against recently developed ones that incorporate turnover explicitly: the total species accumulation method (T-S) and a method based on the distance decay of similarity. I show that the T-S estimator can give reliable estimates of species richness, given an appropriate grouping of sites. The estimator based on distance decay of similarity performed poorly. There are two main reasons for this: sample size effects and the assumption that distance decay of similarity exhibits a power law relationship. I show that estimators based on distance-decay relationships exhibit systematically lower rates of distance decay for samples with few individuals per site independent of environmental variation. Second, the data presented here and many other survey data sets exhibit exponential rather than power law distance-decay relationships. Richness estimators that explicitly incorporate beta diversity can be improved by beginning from an exponential distance-decay relationship and adjusting for the systematic errors introduced by small sample sizes.     

How complex do models need to be to predict dispersal of threatened species through matrix habitats?

Persistence of species in fragmented landscapes depends on dispersal among suitable breeding sites, and dispersal is often influenced by the "matrix" habitats that lie between breeding sites. However, measuring effects of different matrix habitats on movement and incorporating those differences into spatially explicit models to predict dispersal is costly in terms of time and financial resources. Hence a key question for conservation managers is: Do more costly, complex movement models yield more accurate dispersal predictions? We compared the abilities of a range of movement models, from simple to complex, to predict the dispersal of an endangered butterfly, the Saint Francis' satyr (Neonympha mitchellii francisci). The value of more complex models differed depending on how value was assessed. Although the most complex model, based on detailed movement behaviors, best predicted observed dispersal rates, it was only slightly better than the simplest model, which was based solely on distance between sites. Consequently, a parsimony approach using information criteria favors the simplest model we examined. However, when we applied the models to a larger landscape that included proposed habitat restoration sites, in which the composition of the matrix was different than the matrix surrounding extant breeding sites, the simplest model failed to identify a potentially important dispersal barrier, open habitat that butterflies rarely enter, which may completely isolate some of the proposed restoration sites from other breeding sites. Finally, we found that, although the gain in predicting dispersal with increasing model complexity was small, so was the increase in financial cost. Furthermore, a greater fit continued to accrue with greater financial cost, and more complex models made substantially different predictions than simple models when applied to a novel landscape in which butterflies are to be reintroduced to bolster their populations. This suggests that more complex models might be justifiable on financial grounds. Our results caution against a pure parsimony approach to deciding how complex movement models need to be to accurately predict dispersal through the matrix, especially if the models are to be applied to novel or modified landscapes.