Predict Extinction Risk Research Articles

Biological determinants of extinction risk: why are smaller species less vulnerable?

AbstractIt is becoming increasingly clear that species of smaller body size tend to be less vulnerable to contemporary extinction threats than larger species, but few studies have examined the mechanisms underlying this pattern. In this paper, data for the Australian terrestrial mammal fauna are used to ask whether higher reproductive output or smaller home ranges can explain the reduced extinction risk of smaller species. Extinct and endangered species do indeed have smaller litters and larger home ranges for their body size than expected under a null model. In multiple regressions, however, only litter size is a significant predictor of extinction risk once body size and phylogeny are controlled for. Larger litters contribute to fast population growth, and are probably part of the reason that smaller species are less extinction‐prone. The effect of litter size varies between the mesic coastal regions and the arid interior of Australia, indicating that the environment a species inhabits mediates the effect of biology on extinction risk. These results suggest that predicting extinction risk from biological traits is likely to be a complex task which must consider explicitly interactions between biology and environment.

How accurate are population models? Lessons from landscape‐scale tests in a fragmented system

AbstractThere is a growing debate about the ability of Population Viability Analysis (PVA) to predict the risk of extinction. Previously, the debate has focused largely on models where spatial variation and species movement are ignored. We present a synthesis of the key results for an array of different species for which detailed tests of the accuracy of PVA models were completed. These models included spatial variation in habitat quality and the movement of individuals across a landscape. The models were good approximations for some species, but poor for others. Predictive ability was limited by complex processes typically overlooked in spatial population models, these being interactions between landscape structure and life history attributes. Accuracy of models could not be determined a priori, although model tests indicated how they might be improved. Importantly, model predictions were poor for some species that are among the best‐studied vertebrates in Australia. This indicated that although the availability of good life history data is a key part of PVA other factors also influence model accuracy. We were also able to draw broad conclusions about the sorts of populations and life history characteristics where model predictions are likely to be less accurate. Predictions of extinction risk are often essential for real‐world population management. Therefore, we believe that although PVA has been shown to be less than perfect, it remains a useful tool particularly in the absence of alternative approaches. Hence, tests of PVA models should be motivated by the cycle of testing and improvement.

Predicting extinction risk

Predicting extinction risk

Food web structure and habitat loss

In this paper we explore simple food web models to study how metacommunity structure affects species response to habitat loss. We find that patch abundances and extinction thresholds vary according to the kind of food web. Second, for intermediate species, a slight decrease in the exploration cost of the better competitor has a strong effect on the extinction threshold of the poorer competitor. When predicting extinction risk one should consider not only the amount of habitat destroyed, but also the structure of the food web in which species are embedded. Both direct and indirect interactions are critical for predicting the consequences of habitat destruction.

Variation among Individuals and Reduced Demographic Stochasticity.

Population viability analysis ( PVA) is a technique that employs stochastic demographic models to predict extinction risk. All else being equal, higher variance in a demographic rate leads to a greater extinction risk. Demographic stochasticity represents variance due to differences among individuals. Current implementations of PVAs, however, assume that the expected fates of all individuals are identical. For example, demographic stochasticity in survival is modeled as a random draw from a binomial distribution. We developed a simple conceptual model showing that if there is variation among individuals in expected survival, then existing PVA models overestimate the variance due to demographic stochasticity in survival. This is a consequence of Jensen's inequality and the fact that the binomial demographic variance is a concave function of mean survival. The effect of variation among individuals on demographic stochasticity in fecundity depends on the mean-variance relationship for individual reproductive success, which is not presently known. If fecundity patterns mirror those of survival, then variation among individuals will reduce the extinction risk of small populations.

A Role for Natural Resource Social Science in Biodiversity Risk Assessment

Biologists have made considerable progress in developing realistic simulation models to predict extinction risks for threatened species. Social scientists have to date had a more limited role in these efforts. This limited involvement comes despite the growing acknowledgment by population biologists and simulation modelers thatthis additional input is necessary for these models to accurately reflect the impact of humans and human-dominated landscapes on wildlife populations. We argue that collaborations among social and biological scientists can provide unparalleled opportunities to develop new conceptual and simulation tools for biodiversity risk assessment. One challenge is that while the value of interdisciplinary research is widely recognized, interdisciplinary teamwork is difficult to achieve. We suggest strategies to strengthen such cross-disciplinary collaboration, including efforts to link diverse models and to build networks of researchers who have not historically collaborated.We conclude with questions intended to guide further discussions about how to integrate social science information into biodiversity risk assessments in the future.

Australian ecologists are world's best predictors of extinction risk

Australian ecologists are world's best predictors of extinction risk

Structural uncertainty in stochastic population models: delayed development in the eastern barred bandicoot, Perameles gunnii

Uncertainty about which model structure best describes the life history of a species may be a problem for the development of some population viability analysis (PVA). This paper describes the development and exploration of two structurally different stochastic population models when there is uncertainty about the life history of a species. Delayed reproduction was observed in a protected population of the small marsupial Perameles gunnii (eastern barred bandicoot) at Woodlands Historic Park, Victoria, Australia. This previously undocumented feature of P. gunnii may be considered to be either a component of the seasonal breeding cycle or it may be delayed development to sexual maturity. A delayed development model is compared to a standard development model where the parameter estimates of each model were obtained from a long-term mark-recapture study at Woodlands Historic Park. While the growth rate of the delayed development model is less than that of the standard model, the predicted risks of extinction/quasiextinction were higher for the standard model. This discrepancy is the result of different interpretations placed upon the available data underpinning the two models, the most important of which is the difference between the estimated variance in survivorship of the sub-adult stage. The results highlight the need for conservation assessments based on stochastic modelling to explore the degree to which the predicted extinction risk is affected by the incomplete knowledge of the species’ basic biology and parameter values.

Predicting extinction risk in declining species.

What biological attributes predispose species to the risk of extinction? There are many hypotheses but so far there has been no systematic analysis for discriminating between them. Using complete phylogenies of contemporary carnivores and primates, we present, to our knowledge, the first comparative test showing that high trophic level, low population density slow life history and, in particular, small geographical range size are all significantly and independently associated with a high extinction risk in declining species. These traits together explain nearly 50% of the total between-species variation in extinction risk. Much of the remaining variation can be accounted for by external anthropogenic factors that affect species irrespective of their biology.

Predictive accuracy of population viability analysis in conservation biology.

Population viability analysis (PVA) is widely applied in conservation biology to predict extinction risks for threatened species and to compare alternative options for their management. It can also be used as a basis for listing species as endangered under World Conservation Union criteria. However, there is considerable scepticism regarding the predictive accuracy of PVA, mainly because of a lack of validation in real systems. Here we conducted a retrospective test of PVA based on 21 long-term ecological studies--the first comprehensive and replicated evaluation of the predictive powers of PVA. Parameters were estimated from the first half of each data set and the second half was used to evaluate the performance of the model. Contrary to recent criticisms, we found that PVA predictions were surprisingly accurate. The risk of population decline closely matched observed outcomes, there was no significant bias, and population size projections did not differ significantly from reality. Furthermore, the predictions of the five PVA software packages were highly concordant. We conclude that PVA is a valid and sufficiently accurate tool for categorizing and managing endangered species.

Extinction Risk and the 1/f Family of Noise Models

In order to predict extinction risk in the presence of reddened, or correlated, environmental variability, fluctuating parameters may be represented by the family of 1/f noises, a series of stochastic models with different levels of variation acting on different timescales. We compare the process of parameter estimation for three 1/f models (white, pink and brown noise) with each other, and with autoregressive noise models (which are not 1/f noises), using data from a model time-series (length, T) of population. We then calculate the expected increase in variance and the expected extinction risk for each model, and we use these to explore the implication of assuming an incorrect noise model. When parameterising these models, it is necessary to do so in terms of the measured (“sample”) parameters rather than fundamental (“population”) parameters. This is because these models are non-stationary: their parameters need not stabilize on measurement over long periods of time and are uniquely defined only over a specified “window” of timescales defined by a measurement process. We find that extinction forecasts can differ greatly between models, depending on the length, T, and the coefficient of variability, CV, of the time series used to parameterise the models, and on the length of time into the future which is to be projected. For the simplest possible models, ones with population itself the 1/f noise process, it is possible to predict the extinction risk based on CV of the observed time series. Our predictions, based on explicit formulae and on simulations, indicate that (a) for very short projection times relative to T, brown and pink noise models are usually optimistic relative to equivalent white noise model; (b) for projection timescales equal to and substantially greater than T, an equivalent brown or pink noise model usually predicts a greater extinction risk, unless CV is very large; and (c) except for very small values of CV, for timescales very much greater than T, the brown and pink models present a more optimistic picture than the white noise model. In most cases, a pink noise is intermediate between white and brown models. Thus, while reddening of environmental noise may increase the long-term extinction probability for stationary processes, this is not generally true for non-stationary processes, such as pink or brown noises.

Male sexual ornament size but not asymmetry reflects condition in stalk–eyed flies

Models of sexual selection predict that females use ornament size to evaluate male condition. It has also been suggested that ornament asymmetry provides females with accurate information about condition. To test these ideas we experimentally manipulated condition in the stalk-eyed fly, Cyrtodiopsis dalmanni, by varying the amount of food available to developing larvae. Males of this species have greatly exaggerated eyestalk length and females prefer to mate with males with wider eyespans. Our experiments show that male ornaments (eyestalks) display a disproportionate sensitivity to condition compared with the homologous character in females, and to non-sexual traits (wing dimensions). In contrast, in neither sex did asymmetry reflect condition either in sexual ornaments or in non-sexual traits. We conclude that ornament size is likely to play a far greater role in sexual selection as an indicator of individual condition than does asymmetry.

Equatorial Undercurrent of the Indian Ocean : Knauss J. A. and B. A. Taft, 1964. Science, 143 (3604): 354–356

Uncertainty about which model structure best describes the life history of a species may be a problem for the development of some population viability analysis (PVA). This paper describes the development and exploration of two structurally different stochastic population models when there is uncertainty about the life history of a species. Delayed reproduction was observed in a protected population of the small marsupial Perameles gunnii (eastern barred bandicoot) at Woodlands Historic Park, Victoria, Australia. This previously undocumented feature of P. gunnii may be considered to be either a component of the seasonal breeding cycle or it may be delayed development to sexual maturity. A delayed development model is compared to a standard development model where the parameter estimates of each model were obtained from a long-term mark-recapture study at Woodlands Historic Park. While the growth rate of the delayed development model is less than that of the standard model, the predicted risks of extinction/quasiextinction were higher for the standard model. This discrepancy is the result of different interpretations placed upon the available data underpinning the two models, the most important of which is the difference between the estimated variance in survivorship of the sub-adult stage. The results highlight the need for conservation assessments based on stochastic modelling to explore the degree to which the predicted extinction risk is affected by the incomplete knowledge of the species’ basic biology and parameter values.