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.     

Extinction times in experimental populations

Predicting population extinctions is a key element of quantitative conservation biology and population ecology. Although stochastic population theories have long been used to obtain theoretical distributions of population extinction times, model-based predictions have rarely been tested. Here I report results from a quantitative analysis of extinction time in 281 experimental populations of water fleas (Daphnia magna) in variable environments. To my knowledge, this is the first quantitative estimate of the shape of the distribution of population extinction times based on extinction data for any species. The finding that the distribution of population extinction times was extraordinarily peaked is consistent with theoretical predictions for density-independent populations, but inconsistent with predictions for density-dependent populations. The tail of the extinction time distribution was not exponential. These results imply that our current theories of extinction are inadequate. Future work should focus on how demographic stochasticity scales with population size and effects of nonrandom variable environments on population growth and decline.     

Population Variability and Extinction Risk

Abstract: Population models generally predict increased extinction risk (ER) with increased population variability (  PV  ), yet some empirical tests have provided contradictory findings. We resolve this conflict by attributing negative measured relationships to a statistical artifact that arises because PV tends to be underestimated for populations with short persistence. Such populations do not go extinct quickly as a consequence of low intrinsic variability; instead, the measured variability is low because they go extinct so quickly. Consequently, any underlying positive relationship between PV and ER tends to be obscured. We conducted a series of analyses to evaluate this claim. Simulations showed that negative measured relationships are to be expected, despite an underlying positive relationship. Simulations also identified properties of data, minimizing this bias and thereby permitting meaningful analysis. Experimental data on laboratory populations of a bruchid beetle (Callosobruchus maculatus) supported the simulation results. Likewise, with an appropriate statistical approach (Cox regression on untransformed data), reanalysis of a controversial data set on British island bird populations revealed a significant positive association between PV and ER (p = 0.03). Finally, a similar analysis of time series for naturally regulated animal populations revealed a positive association between PV and quasiextinction risk (p < 0.01). Without exception, our simulation results, experimental findings, reanalysis of published data, and analysis of quasiextinction risk all contradict previous reports of negative or equivocal relationships. Valid analysis of meaningful data provides strong evidence that increased population variability leads to increased extinction risk.     

Attitudes toward Sustainability and Extinction

Abstract: Conservation biologists and natural resource managers are both working to maintain species, but their approaches and priorities differ. The contrast was highlighted when the World Conservation Union (IUCN) listed some commercial fish species, such as the Atlantic cod ( Gadus morhua ), in the 1996 Red List of Threatened Animals. These species qualified under IUCN's criteria because they had undergone a marked decline in abundance. Disagreements over these listings revealed fundamental differences between resource managers and conservation biologists. Resource managers aiming to maximize continuing yields using specific, explicit, and data-rich models, generally have not considered risk assessment and sometimes face the necessity for political compromises. Conservation biologists generally consider a wide diversity of species and operate in a data-poor and precautionary context with an overall aim of minimizing extinction risk. The IUCN Red List is an extreme case in point and uses simple criteria for evaluating the conservation status of all species. Under these circumstances, it can do little more than indicate a species' status in order to prompt further investigation by the appropriate body. We suggest that productive collaboration between conservation biologists and resource managers will start with an understanding of these different perspectives and will benefit from common interests in precautionary approaches, ecosystem approaches, and adaptive management studies.     

Island Extinction Rates from Regular Censuses

Regular censuses conducted over a long time allow the calculation of both extinction and immigration rates. We present formulae for estimation of those rates. We use them on bird censuses of three British islands. These formulae improve on previous estimators of extinction but reaffirm that smaller populations have a higher probability of becoming extinct. On the other hand, they suggest no correlation between extinction rate and either body size or migratory status among birds.     

Metapopulation Extinction Risk under Spatially Autocorrelated Disturbance

Abstract:  Recent extinction models generally show that spatial aggregation of habitat reduces overall extinction risk because sites emptied by local extinction are more rapidly recolonized. We extended such an investigation to include spatial structure in the disturbance regime. A spatially explicit metapopulation model was developed with a wide range of dispersal distances. The degree of aggregation of both habitat and disturbance pattern could be varied from a random distribution, through the intermediate case of a fractal distribution, all the way to complete aggregation (single block). Increasing spatial aggregation of disturbance generally increased extinction risk. The relative risk faced by populations in different landscapes varied greatly, depending on the disturbance regime. With random disturbance, the spatial aggregation of habitat reduced extinction risk, as in earlier studies. Where disturbance was spatially autocorrelated, however, this advantage was eliminated or reversed because populations in aggregated habitats are at risk of mass extinction from coarse-scale disturbance events. The effects of spatial patterns on extinction risk tended to be reduced by long-distance dispersal. Given the high levels of spatial correlation in natural and anthropogenic disturbance processes, population vulnerability may be greatly underestimated both by classical (nonspatial) models and by those that consider spatial structure in habitat alone.     

On Uncertain Sightings and Inference about Extinction

The extinction of many species can only be inferred from the record of sightings of individuals. Solow et al. (2012, Uncertain sightings and the extinction of the Ivory‐billed Woodpecker. Conservation Biology 26: 180–184) describe a Bayesian approach to such inference and apply it to a sighting record of the Ivory‐billed Woodpecker (Campephilus principalis). A feature of this sighting record is that all uncertain sightings occurred after the most recent certain sighting. However, this appears to be an artifact. We extended this earlier work in 2 ways. First, we allowed for overlap in time between certain and uncertain sightings. Second, we considered 2 plausible statistical models of a sighting record. In one of these models, certain and uncertain sightings that are valid arise from the same process whereas in the other they arise from independent processes. We applied both models to the case of the Ivory‐billed Woodpecker. The result from the first model did not favor extinction, whereas the result for the second model did. This underscores the importance, in applying tests for extinction, of understanding what could be called the natural history of the sighting record. Sobre Avistamientos Inciertos e Inferencia de la Extinción