Species extinctions have defined the global biodiversity crisis, but extinction begins with loss in abundance of individuals that can result in compositional and functional changes of ecosystems. Using multiple and independent monitoring networks, we report population losses across much of the North American avifauna over 48 years, including once-common species and from most biomes. Integration of range-wide population trajectories and size estimates indicates a net loss approaching 3 billion birds, or 29% of 1970 abundance. A continent-wide weather radar network also reveals a similarly steep decline in biomass passage of migrating birds over a recent 10-year period. This loss of bird abundance signals an urgent need to address threats to avert future avifaunal collapse and associated loss of ecosystem integrity, function, and services.

There is an urgent need to develop e ective vulnerability assessments for evaluating the conservation status of species in a changing climate 1 . Several new assessment approaches have been proposed for evaluating the vulnerability of species to climate change 2-5 based on the expectation that established assessments such as the IUCN Red List 6 need revising or superseding in light of the threat that climate change brings. However, although previous studies have identified ecological and life history attributes that characterize declining species or those listed as threatened 7-9 , no study so far has undertaken a quantitative analysis of the attributes that cause species to be at high risk of extinction specifically due to climate change. We developed a simulation approach based on generic life history types to show here that extinction risk due to climate change can be predicted using a mixture of spatial and demographic variables that can be measured in the present day without the need for complex forecasting models. Most of the variables we found to be important for predicting extinction risk, including occupied area and population size, are already used in species conservation assessments, indicating that present systems may be better able to identify species vulnerable to climate change than previously thought. Therefore, although climate change brings many new conservation challenges, we find that it may not be fundamentally di erent from other threats in terms of assessing extinction risks.Attempts to quantify the threat that climate change poses to species' survival commonly infer extinction risk from changes in the area of climatically suitable habitat (the bioclimate envelope) 10,11 , but this approach ignores important aspects of species' biology such as population dynamics, vital rates and dispersal 12-16 , leading to high uncertainty 1,17 . To address this challenge, we coupled ecological niche models (ENMs) with demographic models [13][14][15][18][19][20] and expanded this approach by developing a generic life history (GLH) method. The coupled modelling approach estimates extinction risk as the probability of abundance falling to zero by the year 2100, rather than as the proportion of species committed to extinction due to contraction of bioclimate envelopes 10 (Methods).By matching ENMs for 36 amphibian and reptile species endemic to the US with corresponding GLH models (Supplementary Table 1), we estimate mean extinction risk by 2100 to be 28 ± 7% under a high CO 2 concentration Reference climate scenario 21 and 23 ± 7% under a Policy climate scenario that assumes substantive intervention 22 (Methods). In contrast, extinction risk is estimated by the same models to be <1% without climate change, showing that the methods are not biased towards predicting high risks. The contrast between predicted extinction risk with and without climate change suggests that climate change will cause a pronounced increase in extinction risk for these taxonomic groups over the coming century. Contrary to other stud...

Summary1. Methods used to predict shifts in species' ranges because of climate change commonly involve species distribution (niche) modelling using climatic variables, future values of which are predicted for the next several decades by general circulation models. However, species' distributions also depend on factors other than climate, such as land cover, land use and soil type. Changes in some of these factors, such as soil type, occur over geologic time and are thus imperceptible over the timescale of these types of projections. Other factors, such as land use and land cover, are expected to change over shorter timescales, but reliable projections are not available. Some important predictor variables, therefore, must be treated as unchanging, or static, whether because of the properties of the variable or out of necessity. The question of how best to combine dynamic variables predicted by climate models with static variables is not trivial and has been dealt with differently in studies to date. Alternative methods include using the static variables as masks, including them as independent explanatory variables in the model, or excluding them altogether. 2. Using a set of simulated species, we tested various methods for combining static variables with future climate scenarios. Our results showed that including static variables in the model with the dynamic variables performed better or no worse than either masking or excluding the static variables. 3. The difference in predictive ability was most pronounced when there is an interaction between the static and dynamic variables. 4. For variables such as land use, our results indicate that if such variables affect species distributions, including them in the model is better than excluding them, even though this may mean making the unrealistic assumption that the variable will not change in the future. 5. These results demonstrate the importance of including static and dynamic non-climate variables in addition to climate variables in species distribution models designed to predict future change in a species' habitat or distribution as a result of climate change.

Climate change is likely to become an increasingly major obstacle to slowing the rate of species extinctions. Several new assessment approaches have been proposed for identifying climate-vulnerable species, based on the assumption that established systems such as the IUCN Red List need revising or replacing because they were not developed to explicitly consider climate change. However, no assessment approach has been tested to determine its ability to provide advanced warning time for conservation action for species that might go extinct due to climate change. To test the performance of the Red List system in this capacity, we used linked niche-demographic models with habitat dynamics driven by a 'business-as-usual' climate change scenario. We generated replicate 100-year trajectories for range-restricted reptiles and amphibians endemic to the United States. For each replicate, we categorized the simulated species according to IUCN Red List criteria at annual, 5-year, and 10-year intervals (the latter representing current practice). For replicates that went extinct, we calculated warning time as the number of years the simulated species was continuously listed in a threatened category prior to extinction. To simulate data limitations, we repeated the analysis using a single criterion at a time (disregarding other listing criteria). Results show that when all criteria can be used, the Red List system would provide several decades of warning time (median = 62 years; >20 years for 99% of replicates), but suggest that conservation actions should begin as soon as a species is listed as Vulnerable, because 50% of replicates went extinct within 20 years of becoming uplisted to Critically Endangered. When only one criterion was used, warning times were substantially shorter, but more frequent assessments increased the warning time by about a decade. Overall, we found that the Red List criteria reliably provide a sensitive and precautionary way to assess extinction risk under climate change.

Both the Breeding Bird Survey (BBS) and the Christmas Bird Count (CBC) have been used for several decades to determine the status and trends of birds in North America. We propose new methods for assessing and comparing the reliability of these two surveys for estimating continental bird trends. In addition, we propose a new method for combining BBS and CBC continental trends to produce a combined continental trend for any species with useful trend information from both surveys. METHODS Breeding Bird Survey (BBS)The BBS, administered by the U.S. Geological Survey; http://www.pwrc.usgs.gov/BBS/, is the primary source of status and trend information for North American birds during the breeding season. The BBS is a roadside survey that includes 50 3-minute stops one-half mile apart, at which experienced individuals count all birds seen and heard. Surveys are done between late May and early July beginning 30 minutes before dawn. Surveys have been done on more than 4,000 routes; about 3,000 routes are done each year. Data are aggregated by BCR and by state. The survey began in 1965, so our analyses begin with that year. Audubon Christmas Bird Count (CBC)The CBC (http://www.audubon.org/bird/cbc/) is the primary source of status and trend information for North American birds in early winter. Each individual CBC occurs within a 15-mile diameter circle on a single day within two weeks of Christmas. Participants join groups that survey subunits of the circle during the course of the day using a variety of transportation methods (mostly on foot, in a car, or watching at a feeder). Just over 2,000 circles are surveyed each year. Like the BBS, data are aggregated by BCR and by state. The first CBC was done in 1900. We begin our analysis of CBC trends with the winter of 1965-66 for comparison with the BBS (which began in 1965) and because earlier CBC data are less comparable to current CBC data due to changes in methods and intensity of effort. Combining CBC and BBS for Continental Bird Population TrendsTrend analysis methods BBS trends and annual indices are estimated using the route-regression methods described by Geissler and Sauer (1990). In this analysis, trend is estimated first, and annual indices of abundance are used to assess higher levels of pattern in the context of the trend.Route trends are estimated as multiplicative trends using the estimating equations estimator described by Link and Sauer (1994). Observer effects are incorporated in the model to prevent bias associated with increases in observer quality over time . Regional trends are found as weighted averages of route trends. Regardless of variability in the counts on the route, missing counts (from years when the route was not surveyed) and observer changes (that modify the quality of the data) both tend to make route data less reliable. Consequently, it is necessary to weight the route trends by a measure of the consistency of counting on the route. This weight is proportional to number of years run and number of observer changes, but because it does not ...