Strains of insect-pathogenic fungi with high virulence toward certain pest insects have great potential for commercial biological control applications. Identifying such strains has been a central theme in using fungi for biological control. This theme is supported by a persistent paradigm in insect pathology which suggests that the host insect is the predominant influence on the population genetics of insect-pathogenic fungi. In this study, a population genetics analysis of the insect-pathogenic fungus Metarhizium anisopliae from forested and agricultural habitats in Ontario, Canada, showed a nonrandom association of alleles between two distinct, reproductively isolated groups (index of multilocus association ‫؍‬ 1.2). Analyses of the mitochondrial DNA showed no differences between the groups. The two groups were associated with different habitat types, and associations with insect hosts were not found. The group from forested areas showed an ability for cold-active growth (i.e., 8°C), while the group from the agricultural area showed an ability for growth at high temperatures (i.e., 37°C) and resilience to UV exposure. These results represent a significant paradigm shift; habitat selection, not host insect selection, drives the population structure of these insect-pathogenic deuteromycetous fungi. With each group we observed recombining population structures as well as clonally reproducing lineages. We discuss whether these groups may represent cryptic species. Worldwide, M. anisopliae may be an assembly of cryptic species, each adapted to certain environmental conditions. The association of fungal genotypes with habitat but not with host insects has implications on the criteria for utility of this, and perhaps other, fungal biocontrol agents.Insect-pathogenic fungi have genetic features related to insect infection (17), and the population genetics of these fungi are also assumed to be influenced primarily by host insect taxa (5,6,14,19,20,24,28,33,35). Metarhizium anisopliae is an insect-pathogenic, haploid, deuteromycetous fungus that is assumed to reproduce clonally, and it is also assumed that certain genotypes are related to an insect host (5,6,14,19,20,24,28,33,35). It also has the potential for parasexual reproduction (36), although an analysis of clonality versus recombination has not been undertaken. One of the distinctive features of a clonal population is the widespread occurrence of identical genotypes (23). Here we have undertaken to determine the population structure of M. anisopliae and to test the paradigm that certain genotypes are related to insect hosts.M. anisopliae is a recognized pathogen of more than 200 insect species, including several major pests (29). It is a recurrent paradigm in the literature that the insect host drives the population structure, i.e., that there are fungal isolates or genotypes adapted for pathogenesis toward certain species or taxa of insects (5,6,14,19,20,24,28,32,35). Because this fungus offers an environmentally safe alternative to chemical pesticides, it is of...

Simple hydrological models, such as the Seasonal Water Yield Model developed by the Natural Capital Project (InVEST SWYM), are attractive as data requirements are relatively easy to satisfy. However, simple models may produce unrealistic results when the underlying hydrological processes are inadequately described. We used the variation in performance of the InVEST SWYM across watersheds to identify correlates of poorly modeled outcomes of InVEST SWYM. We grouped 749 watersheds from across North America into five bioclimatic regions using nine environmental variables. For each region, we compared the predicted flow patterns to actual flow conditions over a 15-year period. The correlation between the modeled and actual flows was highly dispersed and relatively poor, with 92% of r2 values less than 0.5 and 42% less than 0.1. We linked cryospheric variables to model performance in the bioclimatic region with the poorest model performance (the Low elevation Boreal Sub-humid region—LeBSh). After incorporating cryospheric conditions into the InVEST SWYM, predictions improved significantly in 30% of the LeBSh watersheds. We provide a relatively straightforward approach for identifying processes that simple hydrological models may not consider or which need further attention or refinement.

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Identifying the spatial scale at which particular mechanisms influence plant community assembly is crucial to understanding the mechanisms structuring communities. It has long been recognized that many elements of community structure are sensitive to area; however the majority of studies examining patterns of community structure use a single relatively small sampling area. As different assembly mechanisms likely cause patterns at different scales we investigate how plant species co‐occurrence patterns change with sampling unit scale. We use the checkerboard score as an index of species segregation, and examine species C‐score1–sampling area patterns in two ways. First, we show via numerical simulation that the C‐score–area relationship is necessarily hump shaped with respect to sample plot area. Second we examine empirical C‐score–area relationships in arctic tundra, grassland, boreal forest and tropical forest communities. The minimum sampling scale where species co‐occurrence patterns were significantly different from the null model expectation was at 0.1 m2 in the tundra, 0.2 m2 in grassland, and 0.2 ha in both the boreal and tropical forests. Species were most segregated in their co‐occurrence (maximum C‐score) at 0.3 m2 in the tundra (0.54 3 0.54 m quadrats), 1.5 m2 in the grassland (1.2 3 1.2 m quadrats), 0.26 ha in the tropical forest (71 3 71 m quadrats), and a maximum was not reached at the largest sampling scale of 1.4 ha in the boreal forest. The most important finding is that the dominant scales of community structure in these systems are large relative to plant body size, and hence we infer that the dominant mechanisms structuring these communities must be at similarly large scales. This provides a method for identifying the spatial scales at which communities are maximally structured; ecologists can use this information to develop hypotheses and experiments to test scale‐specific mechanisms that structure communities.