Avian Survival Rates and the Extinction Process on Barro Colorado Island, Panama

Abstract: Selective extinction following isolation of habitat patches may be due to biogeographical (e.g., island size or isolation) and ecological (species natural histories, interspecifc interactions) factors, or their interactions. Among the demographic and life history attributes commonly associated with high extinction probability are small populations, large size of individuals, and population variability. Long-term capture-recapture data from forest habitat in central Panama permit an examination of the association between mainland survival rates and extinction on a nearby land-bridge island Species of birds that no longer occur on Barro Colorado Island (BCI), Panama, have, on average, lower survival rates on the adjacent mainland than species that have persisted on BCI. Moreover, of the species that no longer occur on BCI, those with lower mainland survival rates generally disappeared earlier from the island. My analysis provides little evidence of a relationship between extinction and population size. Recolonization of BCI from the adjacent mainland by the forest undergrowth species studied here is unlikely. Reduced reproductive success on BCI combined with naturally low adult survival rates seems to be responsible for these BCI extinctions. High nest predation and/or altered landscape dynamics are probable agents in the low reproductive success. The methods used here could be employed in other circumstances to identify fragmentation-sensitive species.     

Extinction Rates of North American Freshwater Fauna

Abstract: Since 1900, 123 freshwater animal species have been recorded as extinct in North America. Hundreds of additional species of fishes, mollusks, crayfishes, and amphibians are considered imperiled. Using an exponential decay model, we derived recent and future extinction rates for North American freshwater fauna that are five times higher than those for terrestrial fauna. Assuming that imperiled freshwater species will not survive throughout the next century, our model projects a future extinction rate of 4% per decade, which suggests that North America's temperate freshwater ecosystems are being depleted of species as rapidly as tropical forests.     

Double Allee Effects and Extinction in the Island Fox

Abstract:  An Allee effect (AE) occurs in populations when individuals suffer a decrease in fitness at low densities. If a fitness component is reduced (component AE), per capita population growth rates may decline as a consequence (demographic AE) and extinction risk is increased. The island fox ( Urocyon littoralis ) is endemic to six of the eight California Channel Islands. Population crashes have coincided with an increase in predation by Golden Eagles ( Aquila chrysaetos ). We propose that AEs could render fox populations more sensitive and may be a likely explanation for their sharp decline. We analyzed demographic data collected between 1988 and 2000 to test whether fox density (1) influences survival and reproductive rates; (2) interacts with eagle presence and affects fox fitness parameters; and (3) influences per capita fox population trends. A double component AE simultaneously influenced survival (of adults and pups) and proportion of breeding adult females. The adult survival AE was driven by predation by eagles. These component AEs led to a demographic AE. Multiple-component AEs, a predation-driven AE, and the simultaneous occurrence of both component and demographic AEs in a mammal are all previously unreported processes. Populations below 7 foxes/km² could have suboptimal population growth rates due to the demographic AE, and AEs may have contributed to the dramatic declines in three fox populations. Because fox densities in critically endangered populations are well below this level, removing Golden Eagles appears necessary to prevent a predation-driven AE. Conservationists should also be aware of AEs when planning the release of captive foxes. More generally, our findings highlight the danger of overlooking AEs in the conservation of populations of rare or threatened species.     

Extinction Rates in Archipelagoes: Implications for Populations in Fragmented Habitats

To study the effect of habitat fragmentation on population viability, I used extinction rates on islands in archipelagoes and estimated the relative probability of extinction per species on single large islands and sets of smaller islands with the same total area. Data on lizards, birds, and mammals on oceanic islands and mammals on mountaintops and in nature reserves yield similar results. Species are likely to go extinct on all the small islands before they go extinct on the single, large island. In the short term, the analysis indicates that extinction probabilities may be lower on a set of small islands. This is perhaps an artifact due to underestimation of extinction rates on small islands and/or the necessity of pooling species in a focal taxon to obtain estimates of extinction rates (which may obscure area thresholds and underestimate the slope and curvature of extinction rates as a function of area). Ultimately, cumulative extinction probabilities are higher for a set of small islands than for single large islands. Mean and median times to extinction tend to be shorter in the fragmented systems, in some cases much shorter. Thus, to minimize extinction rates in isolated habitat remnants and nature reserve systems, the degree of fragmentation should be minimized     

Rates of Movement of Threatened Bird Species between IUCN Red List Categories and toward Extinction

Abstract:  In recent centuries bird species have been deteriorating in status and becoming extinct at a rate that may be 2–3 orders of magnitude higher than in prehuman times. We examined extinction rates of bird species designated critically endangered in 1994 and the rate at which species have moved through the IUCN (World Conservation Union) Red List categories of extinction risk globally for the period 1988–2004 and regionally in Australia from 1750 to 2000. For Australia we drew on historical accounts of the extent and condition of species habitats, spread of invasive species, and changes in sighting frequencies. These data sets permitted comparison of observed rates of movement through the IUCN Red List categories with novel predictions based on the IUCN Red List criterion E, which relates to explicit extinction probabilities determined, for example, by population viability analysis. The comparison also tested whether species listed on the basis of other criteria face a similar probability of moving to a higher threat category as those listed under criterion E. For the rate at which species moved from vulnerable to endangered, there was a good match between observations and predictions, both worldwide and in Australia. Nevertheless, species have become extinct at a rate that, although historically high, is 2 (Australia) to 10 (globally) times lower than predicted. Although the extinction probability associated with the critically endangered category may be too high, the shortfall in realized extinctions can also be attributed to the beneficial impact of conservation intervention. These efforts may have reduced the number of global extinctions from 19 to 3 and substantially slowed the extinction trajectory of 33 additional critically endangered species. Our results suggest that current conservation action benefits species on the brink of extinction, but is less targeted at or has less effect on moderately threatened species.     

Predicting Extinction Times from Environmental Stochasticity and Carrying Capacity

Managers of small populations often need to estimate the expected time to extinction T_e of their charges. Useful models for extinction times must be ecologically realistic and depend on measurable parameters. Many populations become extinct due to environmental stochasticity, even when the carrying capacity K is stable and the expected growth rate is positive. A model is proposed that gives T_e by diffusion analysis of the log population size n_t (= log_e N_t). The model population grows according to the equation N_t+1 = R_tN_t, with K as a ceiling. Application of the model requires estimation of the parameters k = logK, r_d = the expected change in n, v_r = Variance(log R), and ϱ the autocorrelation of the r_t. These are readily calculable from annual census data (r_d is trickiest to estimate). General formulas for T_e are derived. As a special case, when environmental fluctuations overwhelm expected growth (that is r_d 0), T_e = 2n_o(k - n_o/2)/v_r. If the r_t are autocorrelated, then the effective variance is v_re v_r (1 + ϱ)/(1 - ϱ). The theory is applied to populations of checkerspot butterfly, grizzly bear, wolf, and mountain lion.     

Armageddon Versus Extinction

Note from the Editor : Some readers may feel that topics such as politics and religion are inappropriate for a scientific journal. Our "Conservation in Context" column, however, is intended to cover the entire spectrum of the context within which the science of conservation biology must operate. Thus, we freely and openly explore topics in this column that ordinarily are not addressed in scientific journals, to better understand the real-world constraints and opportunities that define how conservation science is applied in a complex world.     

Detecting Extinction Risk from Climate Change by IUCN Red List Criteria

Anthropogenic climate change is a key threat to global biodiversity. To inform strategic actions aimed at conserving biodiversity as climate changes, conservation planners need early warning of the risks faced by different species. The IUCN Red List criteria for threatened species are widely acknowledged as useful risk assessment tools for informing conservation under constraints imposed by limited data. However, doubts have been expressed about the ability of the criteria to detect risks imposed by potentially slow‐acting threats such as climate change, particularly because criteria addressing rates of population decline are assessed over time scales as short as 10 years. We used spatially explicit stochastic population models and dynamic species distribution models projected to future climates to determine how long before extinction a species would become eligible for listing as threatened based on the IUCN Red List criteria. We focused on a short‐lived frog species (Assa darlingtoni) chosen specifically to represent potential weaknesses in the criteria to allow detailed consideration of the analytical issues and to develop an approach for wider application. The criteria were more sensitive to climate change than previously anticipated; lead times between initial listing in a threatened category and predicted extinction varied from 40 to 80 years, depending on data availability. We attributed this sensitivity primarily to the ensemble properties of the criteria that assess contrasting symptoms of extinction risk. Nevertheless, we recommend the robustness of the criteria warrants further investigation across species with contrasting life histories and patterns of decline. The adequacy of these lead times for early warning depends on practicalities of environmental policy and management, bureaucratic or political inertia, and the anticipated species response times to management actions. Detección del Riesgo de Extinción a partir del Cambio Climático por medio del Criterio de la Lista Roja de la UICNKeith et al.