|dc.description.abstract||Biodiversity can be defined in many ways. One of the more common ways of indicating an ecosystem’s biodiversity is by measuring species richness; the number of different species that inhabit it. However, species richness follows a distinctive global pattern (generally decreasing from the tropics towards the poles), and gives only an absolute value for biodiversity. As such, while species richness may be useful for studying biogeographical patterns, it is not a particularly useful indicator to assess the health of ecosystems, let alone comparing the health of dissimilar ecosystems. For conservationists, indicators that measure how species-rich an ecosystem is relative to how species-rich it could be may be more useful. In this study, biodiversity patterns of a network of mammal assemblages (hexagonal cells of 7500 km2) across extratropical North America were analyzed. The distribution patterns of species amongst the cells were used to calculate various biodiversity indicators.
The method of reflections was used to quantify the potential species richness of each cell, which was then compared to the observed species richness, yielding an indicator called the anomaly. Beal’s probability index, a method that can be used to quantify with what probability a species might occur in a particular cell, was used to calculate each cell’s dark diversity; the amount of species that are unexpectedly absent. The relationship between dark diversity and species richness yields the indicator completeness. Both anomaly and completeness relate to how species-rich a cell is relative to the potential that the network co-occurrence patterns of the species within the cell suggest; cells with a high anomaly or low completeness are far from their potential species richness. Unlike completeness, the anomaly does not require an explicit qualification of a cell’s dark diversity and therefore does not need to make assumptions. Here we assess whether the indicator anomaly is a suitable substitute for the more established indicator completeness.
All biodiversity indicators were regressed against one another, and against models containing a variety of environmental factors commonly understood to affect biodiversity: precipitation, temperature, net primary productivity, elevation range, habitat homogeneity and human influence. We found that a combination of elevation range, mean annual precipitation and mean annual temperature was generally the most accurate predictor for these biodiversity indicators. Also, unlike species richness, which increased from the northeast to the southwest of the continent, anomaly and completeness were more closely tied to topographical features such as mountains and islands.||