Optimal allocation of conservation effort among subpopulations of a threatened species: How important is patch quality?

Money is often a limiting factor in conservation, and attempting to conserve endangered species can be costly. Consequently, a framework for optimizing fiscally constrained conservation decisions for a single species is needed. In this paper we find the optimal budget allocation among isolated subpopulations of a threatened species to minimize local extinction probability. We solve the problem using stochastic dynamic programming, derive a useful and simple alternative guideline for allocating funds, and test its performance using forward simulation. The model considers subpopulations that persist in habitat patches of differing quality, which in our model is reflected in different relationships between money invested and extinction risk. We discover that, in most eases, subpopulations that are less efficient to manage should receive more money than those that are more efficient to manage, due to higher investment needed to reduce extinction risk. Our simple investment guideline performs almost as well as the exact optimal strategy. We illustrate our approach with a case study of the management of the Sumatran tiger, Panthera tigris sumatrae, in Kerinei Seblat National Park (KSNP), Indonesia. We find that different budgets should be allocated to the separate tiger subpopulations in KSNP. The subpopulation that is not at risk of extinction does not require any management investment. Based on the combination of risks of extinction and habitat quality, the optimal allocation for these particular tiger subpopulations is an unusual case: subpopulations that occur in higher-quality habitat (more efficient to manage) should receive more funds than the remaining subpopulation that is in lower-quality habitat. Because the yearly budget allocated to the KSNP for tiger conservation is small, to guarantee the persistence of all the subpopulations that are currently under threat we need to prioritize those that are easier to save. When allocating resources among subpopulations of a threatened species, the combined effects of differences in habitat quality, cost of action, and current subpopulation probability of extinction need to be integrated. We provide a useful guideline for allocating resources among isolated subpopulations of any threatened species

Unintended Consequences of Conservation Actions: Managing Disease in Complex Ecosystems

Infectious diseases are increasingly recognised to be a major threat to biodiversity. Disease management tools such as control of animal movements and vaccination can be used to mitigate the impact and spread of diseases in targeted species. They can reduce the risk of epidemics and in turn the risks of population decline and extinction. However, all species are embedded in communities and interactions between species can be complex, hence increasing the chance of survival of one species can have repercussions on the whole community structure. In this study, we use an example from the Serengeti ecosystem in Tanzania to explore how a vaccination campaign against Canine Distemper Virus (CDV) targeted at conserving the African lion (Panthera leo), could affect the viability of a coexisting threatened species, the cheetah (Acinonyx jubatus). Assuming that CDV plays a role in lion regulation, our results suggest that a vaccination programme, if successful, risks destabilising the simple two-species system considered, as simulations show that vaccination interventions could almost double the probability of extinction of an isolated cheetah population over the next 60 years. This work uses a simple example to illustrate how predictive modelling can be a useful tool in examining the consequence of vaccination interventions on non-target species. It also highlights the importance of carefully considering linkages between human-intervention, species viability and community structure when planning species-based conservation actions

Patterns of mammalian population decline inform conservation action

Evaluations of wildlife population dynamics have the potential to convey valuable information on the type of pressure affecting a population and could help predict future changes in the population's trajectory. Greater understanding of different patterns of population declines could provide a useful mechanism for assessing decline severity in the wild and identifying those populations that are more likely to exhibit severe declines. We identified 93 incidences of decline within 75 populations of mammalian species using a time-series analysis method. These included linear, quadratic convex (accelerating) declines, exponential concave (decelerating) declines and quadratic concave declines (representing recovering populations). Excluding linear declines left a data set of 85 declines to model the relationship between each decline-curve type and a range of biological, anthropogenic and time-series descriptor explanatory variables. None of the decline-curve types were spatially or phylogenetically clustered. The only characteristic that could be consistently associated with any curve type was the time at which they were more likely to occur within a time series. Quadratic convex declines were more likely to occur at the start of the time series, while recovering curve shapes (quadratic concave declines) were more likely at the end of the time series. Synthesis and applications. The ability to link certain factors with specific decline dynamics across a number of mammalian populations is useful for management purposes as it provides decision-makers with potential triggers upon which to base their conservation actions. We propose that the identification of quadratic convex declines could be used as an early-warning signal of potentially severe decline dynamics. For such a population, increased population monitoring effort should be deployed to diagnose the cause of its decline and avert possible extinctions. Conversely, the presence of a quadratic concave decline suggests that the population has already undergone a period of serious decline but is now in the process of recovery. Such populations will require different types of conservation actions, focussing on enhancing their chances of recovery

R-squared values (and respective standard deviations) between observed and modelled abundance for all populations of sedentary grazer species.

<p>R-squared values (and respective standard deviations) between observed and modelled abundance for all populations of sedentary grazer species.</p

Number of consecutive months of the preceding year in which <i>q<</i><i>θ</i> (<b><i>C</i></b>) as a predictor of growth rates (<i>r</i>) for all sedentary, grazing or mixed feeding species (4 species, n = 148).

<p>Number of consecutive months of the preceding year in which <i>q<</i><i>θ</i> (<b><i>C</i></b>) as a predictor of growth rates (<i>r</i>) for all sedentary, grazing or mixed feeding species (4 species, n = 148).</p

Modelled average growth rate (<i>λ</i>) and mean extinction probability (<i>E;</i> in %) to 2099 across all populations of each sedentary grazer species under model scenarios 20C, B1 and A2. SD stands for standard deviation.

<p>Modelled average growth rate (<i>λ</i>) and mean extinction probability (<i>E;</i> in %) to 2099 across all populations of each sedentary grazer species under model scenarios 20C, B1 and A2. SD stands for standard deviation.</p

Distribution of the 70 ungulate populations used in the analysis.

<p>Distribution of the 70 ungulate populations used in the analysis.</p

Models considered while modeling birth season length.

<p>Models are ranked according to their associated Akaike Information Criterion corrected for small sample sizes (AICc). “*” Indicates the presence of an interaction between the variables on both sides of the sign (e.g., “Contingency*Constancy” should be read as “Contingency + Constancy + Contingency x Constancy”). “Diet” refers to diet type, “Gregariousness” to the level of gregariousness.</p

The variables hypothesized to influence birth season length in ungulate populations, with the rationale behind their inclusion.

<p>The variables hypothesized to influence birth season length in ungulate populations, with the rationale behind their inclusion.</p