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.biodiversity | elevational gradient | global change | Lepidoptera | phenology

Aim  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.

Abstract. The epidemic and economic impacts of Foot-and-mouth disease virus (FMDV) spread and control were examined by using epidemic simulation and economic (epinomic) optimization models. The simulated index herd was a $2,000 cow dairy located in California. Simulated disease spread was limited to California; however, economic impact was assessed throughout the United States and included international trade effects. Five index case detection delays were examined, which ranged from 7 to 22 days. The simulated median number of infected premises (IP) ranged from approximately 15 to 745, increasing as the detection delay increased from 7 to 22 days. Similarly, the median number of herds under quarantine increased from approximately 680 to 6,200, whereas animals slaughtered went from approximately 8,700 to 260,400 for detection delays of 7-22 days, respectively. The median economic impact of an FMD outbreak in California was estimated to result in national agriculture welfare losses of $2.3-$69.0 billion as detection delay increased from 7 to 22 days, respectively. If assuming a detection delay of 21 days, it was estimated that, for every additional hr of delay, the impact would be an additional approximately 2,000 animals slaughtered and an additional economic loss of $565 million. These findings underline the critical importance that the United States has an effective early detection system in place before an introduction of FMDV if it hopes to avoid dramatic losses to both livestock and the economy.

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