Butterfly populations are naturally patchy and undergo extinctions and recolonizations. Analyses based on more than 2 decades of data on California's Central Valley butterfly fauna show a net loss in species richness through time. We analyzed 22 years of phenological and faunistic data for butterflies to investigate patterns of species richness over time. We then used 18-22 years of data on changes in regional land use and 37 years of seasonal climate data to develop an explanatory model. The model related the effects of changes in land-use patterns, from working landscapes (farm and ranchland) to urban and suburban landscapes, and of a changing climate on butterfly species richness. Additionally, we investigated local trends in land use and climate. A decline in the area of farmland and ranchland, an increase in minimum temperatures during the summer and maximum temperatures in the fall negatively affected net species richness, whereas increased minimum temperatures in the spring and greater precipitation in the previous summer positively affected species richness. According to the model, there was a threshold between 30% and 40% working-landscape area below which further loss of working-landscape area had a proportionally greater effect on butterfly richness. Some of the isolated effects of a warming climate acted in opposition to affect butterfly richness. Three of the 4 climate variables that most affected richness showed systematic trends (spring and summer mean minimum and fall mean maximum temperatures). Higher spring minimum temperatures were associated with greater species richness, whereas higher summer temperatures in the previous year and lower rainfall were linked to lower richness. Patterns of land use contributed to declines in species richness (although the pattern was not linear), but the net effect of a changing climate on butterfly richness was more difficult to discern.

Species phenology is increasingly being used to explore the effects of climate change and other environmental stressors. Long-term monitoring data sets are essential for understanding both patterns manifest by individual species and more complex patterns evident at the community level. This study used records of 78 butterfly species observed on 626 days across 27 years at a site in northern California, USA, to build quadratic logistic regression models of the observation probability of each species for each day of the year. Daily species probabilities were summed to develop a potential aggregate species richness (PASR) model, indicating expected daily species richness. Daily positive and negative contributions to PASR were calculated, which can be used to target optimum sampling time frames. Residuals to PASR indicate a rate of decline of 0.12 species per year over the course of the study. When PASR was calculated for wet and dry years, wet years were found to delay group phenology by up to 17 days and reduce the maximum annual expected species from 32.36 to 30. Three tests to determine how well the PASR model reflected the butterfly fauna dynamics were all positive: We correlated probabilities developed with species presence/absence data to observed abundance by species, tested species' predicted phenological patterns against known biological characteristics, and compared the PASR curve to a spline-fitted curve calculated from the original species richness observations. Modeling individual species' flight windows was possible from presence/absence data, an approach that could be used on other similar records for butterfly communities with seasonal phenologies, and for common species with far fewer dates than used here. It also provided a method to assess sample frequency guidelines for other butterfly monitoring programs.

To evaluate a suite of species distribution models for their utility as predictors of suitable habitat and as tools for new population discovery of six rare plant species that have both narrow geographical ranges and specialized habitat requirements. Location The Rattlesnake Creek Terrane (RCT) of the Shasta-Trinity National Forest in the northern California Coast Range of the United States. Methods We used occurrence records from 25 years of US Forest Service botanical surveys, environmental and remotely sensed climate data to model the distributions of the target species across the RCT. The models included generalized linear models (GLM), artificial neural networks (ANN), random forests (RF) and maximum entropy (ME). From the results we generated predictive maps that were used to identify areas of high probability occurrence. We made field visits to the top-ranked sites to search for new populations of the target species. Results Random forests gave the best results according to area under the curve and Kappa statistics, although ME was in close agreement. While GLM and ANN also gave good results, they were less restrictive and more varied than RF and ME. Cross-model correlations were the highest for species with the most records and declined with record numbers. Model assessment using a separate dataset confirmed that RF provided the best predictions of appropriate habitat. Use of RF output to prioritize search areas resulted in the discovery of 16 new populations of the target species. Main conclusions Species distribution models, such as RF and ME, which use presence data and information about the background matrix where species do not occur, may be an effective tool for new population discovery of rare plant species, but there does appear to be a lower threshold in the number of occurrences required to build a good model. © 2009 Blackwell Publishing Ltd.

Climate change and habitat destruction have been linked to global declines in vertebrate biodiversity, including mammals, amphibians, birds, and fishes. However, invertebrates make up the vast majority of global species richness, and the combined effects of climate change and land use on invertebrates remain poorly understood. Here we present 35 years of data on 159 species of butterflies from 10 sites along an elevational gradient spanning 0-2,775 m in a biodiversity hotspot, the Sierra Nevada Mountains of Northern California. Species richness has declined at half of the sites, with the most severe reductions at the lowest elevations, where habitat destruction is greatest. At higher elevations, we observed clear upward shifts in the elevational ranges of species, consistent with the influence of global warming. Taken together, these long-term data reveal the interacting negative effects of human-induced changes on both the climate and habitat available to butterfly species in California. Furthermore, the decline of ruderal, disturbance-associated species indicates that the traditional focus of conservation efforts on more specialized and less dispersive species should be broadened to include entire faunas when estimating and predicting the effects of pervasive stressors.