Ecosystem energy is now recognized as a primary correlate and potential driver of global patterns of species richness. The increasingly well-tested species-energy relationship is now ripe for application to conservation, and recent advances in satellite technology make this more feasible. While the correlates for the species-energy relationship have been addressed many times previously, this study is among the first to apply species-energy theory to conservation. Our objectives were to: (1) determine the strongest model of bird richness across North America; (2) determine whether the slope of the best species-energy model varied with varying energy levels; and (3) identify the spatial patterns with similar or dissimilar slopes to draw inference for conservation. Model selection techniques were used to evaluate relationships between Moderate Resolution Imaging Spectroradiometer (MODIS) measures of ecosystem energy and species richness of native land birds using the USGS Breeding Bird Survey (BBS) data. Linear, polynomial, and break point regression techniques were used to evaluate the shape of the relationships with correction for spatial autocorrelation. Spatial analyses were used to determine regions where slopes of the relationship differed. We found that annual gross primary production (GPP) was the strongest correlate of richness (adjusted R-2 = 0.55), with a quadratic model being the strongest model. The negative slope of the model was confirmed significantly negative at the highest energy levels. This finding demonstrates that there are three different slopes to the species-energy relationship across the energy gradient of North America: positive, flat, and negative. If energy has a causal relationship with richness, then species-energy theory implies that energy causes richness to increase in low-energy areas, energy has little effect in intermediate-energy areas, and energy depresses richness in the highest-energy areas. This information provides a basis for potential applications for more effective conservation. For example, in low-energy areas, increased nutrients could improve vegetation productivity and increase species richness. In high-energy areas where competitive dominance of vegetation might reduce species richness, vegetation manipulation could increase species richness. These strategies will likely be most effective if tailored to the local energy gradient.
