Jessica Stanton and H. Reşit Akçakaya Introduction

Conservation Planning to Ensure Viability of Populations and Metapopulations

Jessica Stanton and H. Reşit Akçakaya

Introduction

Current Best Practices

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  probability of extinction within the next 100 years;
  
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  probability of a 50% decline by the year 2060;
  
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  probability that there will be 50 or fewer mature individuals left in the population sometime in the next 40 years;
  
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  probability that the population will increase to 1000 or more individuals by the year 2050.
  

To predict these types of viability measures, PVA uses a variety of models (discussed below), which are parameterized with habitat and distribution data (e.g., occurrence locations and map layers describing habitat requirements) and demographic data (such as censuses, mark-recapture studies, surveys and observations of reproduction and dispersal events).

(i) Assessing vulnerability: estimating the risk of extinction (an absolute measure of vulnerability), or the relative vulnerability of species and populations to extinction. These predictions can be used in ranking species (such as in the IUCN Red List of Threatened Species). When combined with other considerations such as cost effectiveness, social and cultural priorities, and taxonomic uniqueness, these results can also be used to set policies and priorities for allocating scarce conservation resources.

(ii) Evaluating management options: predicting the likely responses of species to conservation and management actions, such as reintroduction, captive breeding, prescribed burning, weed control, habitat rehabilitation, or different designs for nature reserves or corridor networks.

(iii) Impact assessment: quantifying the impact of human activities (such as urban sprawl and other types of land-use change; harvest, poaching, and other types of direct exploitation; pollution, and introduction of exotic species) by comparing results of models with and without the population-level consequences of the human activity. These assessments may involve past or current impacts (to assign blame) or projected future impacts (e.g., to evaluate proposed development plans).

(iv) Planning research: determining priorities for further data collection based on the sensitivity of model results to data uncertainties in model parameters.

(v) Improving understanding: organizing the information and assumptions about a species or a population. PVA allows a structured process that makes the assumptions explicit, and highlights the data deficiencies and uncertainties.

Guidelines for application

Before you begin a PVA it is important to clearly identify the question you wish to answer with your analysis. Clearly outlining a realistic and useful question or set of questions is not a trivial point and new questions may arise along the way as you begin to gather the data needed to construct your models. Likewise, you may find that the available data are not sufficient to address your original questions and you may need to gather more data and the process of constructing a PVA can make your data needs clearer. However, PVA can help clarify where the critical areas of uncertainty or data deficiency lie. Examples of the types of questions that are typically addressed by a PVA include predictions about future abundances or assessing vulnerabilities such as ‘What is the risk of local extinction?’ or ‘What is the chance for recovery?’; questions related to assessing the impacts or current or future threats such as ‘What is the population-level impact of harvesting?’; questions to evaluate competing management options such as ‘Which management option will reduce the extinction risk the most for the least cost?’; and questions to guide future research or data collection ‘Is the model more sensitive to uncertainty in adult survival or fecundity estimates?’.

Although other chapters in this volume cover predicting habitat, we briefly discuss it here, because it is an important aspect of viability analysis. In addition, habitat maps or models constructed for other conservation purposes may or may not be at the most appropriate spatial extent or resolution, or capture the characteristics of the landscape to be most useful in a PVA. A habitat map is the spatial backbone of a PVA and directly relates to many of the demographic parameters such as the number and carrying capacity of subpopulations, dispersal, and survival among others.

1. 
   Use a spatial scale that is relevant to the dispersal and patch size of the species (see below for more details on issues of scale).
   
2. 
   Verify occurrence locations when constructing a correlation based habitat model. Occurrence locations from publicly available biodiversity databases can at times be problematic if the coordinates given have an unknown radius of uncertainty. For instance, coordinates given may be the location of an actual sighting, or the center of a county or township where the species is reported to occur. Species misidentification is another issue that can occur and will impact the accuracy of habitat models (Lozier, Aniello, & Hickerson 2009).
   
3. 
   Select variables for the model that are biologically relevant to the species, and directly determine its occurrence through known eco-physiological mechanisms. Thus, indirect variables (e.g. altitude, topographic heterogeneity) should be avoided (Guisan & Zimmermann 2000).
   
4. 
   Consider including climatic variables (derived from long-term weather data), land-use or land-cover variables (often derived from remote-sensing products such as satellite images). When the model includes climatic variables that are predicted to change in the future (see below), it may be better to include the static maps (such as land-cover) as binary masks, rather than including them in the model fitting.
   
5. 
   When using categorical variables (such as land-cover), consider creating distance maps (e.g., distance to water) or percentage cover maps (e.g., percent of forest in a 120-meter cell, based on 30-meter cells of the original map), if such derived variables are considered to more directly affect the suitability of an area for the focal species.
   
6. 
   Validate your model by using some form of data partitioning this will help to avoid the risk of overfitting your model (see Model parameters and assumptions).
   

An important consideration when constructing a model for a PVA is the spatial scale of your data, both in terms of the resolution and the extent. The scale should be relevant to the biology of the focal species, meaning it should relate to the patch size and dispersal distance. For example, too fine a resolution is needlessly data intensive, too coarse and you may not get an adequate representation of the patch structure. An approximate rule-of-thumb for vertebrate species is that the cell size of the raster map underlying the model should be about the same size or a bit smaller (but not much smaller) than the home range or territory size of the species.

The issues of scale mentioned above illustrate just a few of the common mistakes that can be made when constructing a population model. This is not meant as a discouragement; the process of constructing a model is an opportunity to think carefully about important dynamics of your focal species and all the factors which may be influencing the population in critical ways. It is important to be mindful of what explicit assumptions you are making in your model as well as the assumptions which are implied through the model parameters. For instance, mapping the available habitat on the present landscape as a static variable is making the implicit assumption that the landscape will not change. Sometimes, because of lack of data (or lack of a crystal ball), we are forced to make assumptions which we know are not entirely accurate. By being aware of the assumptions you are making with your model you can evaluate whether or not they seem reasonable or supportable within the context of the questions you are trying to address.

A good way to test the fit of a model is to use separate (ideally independent) data for model building and for model validation. This separate training data and testing data can come from partitioning a single data set spatially or temporally (while controlling for spatial or temporal autocorrelation). So for example, a model of habitat suitability might be constructed using one half of a landscape and tested for accuracy by seeing how well it can predict the occurrences in the other half. This can be done with time series data of population trends as well where some portion of the time series is retained for evaluation. Using this approach, Brook et al. (2000) validated probabilistic predictions of PVAs—they found that the predicted risk of population decline closely matched observed outcomes, there was no significant bias, population size projections did not differ significantly or importantly from reality, and the predictions of five software packages they tested were highly concordant.

Demographic structure refers to the categorizations of individuals within a population according to their age, size, sex, or physiological state (such as immature, breeding, etc.). Models should have an age or stage structure if the demographic characteristics (fecundity, survival, growth, dispersal) depend on the age, size, or other characteristics of the individual. These models, also referred to as "matrix models" because of the way the demographic rates can be organized in a matrix equation, are commonly used in conservation biology; and many techniques have been developed to parameterize them (Caswell 2001). The data that can be used for estimating the parameters of these models (e.g., age-specific survival rates and fecundities) include mark-recapture data (White & Burnham 1999), and population censuses in which individuals of different ages or stages are counted separately.

Density dependence refers to how the population growth depends on population size. In many species, both survival and fecundity decrease as a function of population size (i.e., more crowded populations have lower population growth). The mechanism for this is usually intraspecific competition for limited resources, such as food, light, and space (i.e., nesting or hiding sites). However, at very low populations, the reverse of this can also happen: because of factors collectively known as Allee effects, survival or reproduction may be lower when population size is very low (because of, for instance, difficulty in finding mates, or breakdown of social structures).

Most PVA models represent the landscape as discrete patches of suitable habitat, surrounded by a "matrix" which does not support populations, but may allow dispersal. Each discrete habitable patch is assumed to support one biological population (sometimes called a subpopulation of a metapopulation). A biological population can be defined as a group of regularly interbreeding (i.e. panmictic) individuals. How these populations are delineated (i.e., the number, size, shape, and location of these populations in the landscape) determines the model's spatial structure.

Dispersal is the movement of individuals in the landscape. Dispersal between populations allows extinct populations (unoccupied patches) to become recolonized, and decreases the probability of local extinction of extant but small populations ("rescue effect"). Thus, in many cases increased dispersal increases viability. However, this is not always true (see Stacey et al. 1997; Beier & Noss 1998; Lecomte et al. 2004); in some cases, increasing dispersal, e.g., by building or maintaining habitat corridors, may not be the best option. Whether or not such conservation actions are useful depends on many factors, including the behavior of the species in corridors, risk of spreading diseases or attracting predators, and cost of, and alternatives to, corridors (Akçakaya et al. 2007). Thus, defining connectivity as a conservation goal by itself may be counterproductive; instead the role of dispersal should be studied in the context of the whole metapopulation, and in terms of its effect on the overall viability of the species in the landscape.

Variability is a pervasive characteristic of natural populations, and an important determinant of risk of extinction or decline in population models. Some variability in population sizes arises from the internal dynamics of the population; these include deterministic changes such as fluctuations in age composition due to departure from the stable age distribution, and random changes caused by demographic stochasticity. Demographic stochasticity is the sampling variation in the number of survivors and the number of offspring that occurs (even if survival rates and fecundities were constant) because a population is made up of a finite, integer number of individuals. Demographic stochasticity is often modeled by sampling the number of survivors from a binomial distribution and the number of offspring from a Poisson distribution (Akçakaya 1991). Relative to other factors, demographic stochasticity becomes more important at small population sizes.

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  If survival rates are based on a mark-recapture analysis, see Gould & Nichols (1998), White et al. (2002), or the help file of Program MARK on how to remove demographic/sampling variance.
  
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  If survival or fecundity estimates are based on age-structured census data, subtract the expected demographic variance from the observed variance (Akçakaya 2002).
  
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  If the data are from a census of the total (or part of the) population, use methods discussed by Holmes (2001, 2004).
  
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  If you have repeated or parallel estimates of the same population, the covariance of the parallel measurements or the within-year variance estimates can be used to subtract observation error from total observed variance (see Dunning et al. 2002; and Morris & Doak 2002, Chapter 5).
  

An important consideration is the correlation among the demographic rates over time. This can be either different demographic rates in the same population (e.g., the correlation between survival and fecundity), or in different populations (e.g., the spatial correlation of population growth rate). Correlation increases the variability of the population size, and hence increases the risks of decline or increase (LaHaye et al. 1994). Thus, assuming no (or zero) correlation often gives results that are not precautionary.

Landscapes change according to seasons, climatic fluctuations (e.g., droughts, El Niño events), disturbances (e.g., fire and windthrow) and succession, as well as human impacts (e.g., urban sprawl, global climate change, and agricultural expansion). For a species in such a dynamic landscape, these changes cause changes in the spatial structure of the population (e.g., populations may become more fragmented or more isolated), as well as in the characteristics of populations (e.g., habitat loss may mean a declining carrying capacity). These changes have important effects on the viability of the species, which depends on the interaction between landscape change (the pattern, scale, rate and direction of habitat changes in size, structure and quality) and the species' ecology (its ability to disperse between and function within the habitat patches).

Model parameters used in a PVA always contain some level of uncertainty. This uncertainty may be a consequence of measurement error, not knowing how different model parameters interact with one another, natural variability in the environment, and/or not knowing how parameters may change in the future. The relative effects of uncertainty in model parameters on model results can be evaluated by conducting a sensitivity analysis. A sensitivity analysis can help to identify important parameters in which small changes in estimated values or assumptions have relatively large effects on conclusions drawn from the model. Sensitivity analysis can also help to prioritize resource allocation to collecting field data in the future as it will allows for identifying parameters whose more precise estimation will have the largest effects on improving model confidence (Akçakaya & Sjögren-Gulve 2000). PVAs, particularly when constructed as spatially explicit demographic or individual based models, can include complicated interactions between multiple variables. Ideally, sensitivity analysis should include tests of multiple parameters at once rather than one-at-a-time parameter tests especially if spatial and non-spatial parameters may be interacting (Naujokaitis-Lewis et al. 2009).

Guidelines for implementing conservation using this approach

Population viability models naturally lend themselves to address questions related to available conservation management options. It may be that you wish to evaluate a proposed plan in terms of how effective it may be over time or how the focal species might be expected to respond. You may have multiple management options on the table and you wish rank to them in terms of expected effectiveness. For example, reserve design often involves selecting the network of reserve areas that will represent the greatest number of species. This approach does not examine whether the arrangement and connectivity of the selected reserve areas will be sufficient to support the long term survival of any those species. By looking at the total species pool and selecting a subset of focal species that are representative of the range of dispersal abilities, life history traits, and habitat preferences and conducting a PVA for those focal species you would be able to evaluate the proposed reserve designs with an eye toward long-term viability of the species represented. Within a PVA framework each reserve can be evaluated as one or more patch or subpopulation for each focal species. Other examples of how PVA can inform conservation planning include simulating habitat corridor design by modeling increased dispersal between patches and simulating translocation or reintroduction by modeling a set number of individuals being moved or reintroduced to a patch.

Climate change has the potential to pose a threat to a great number of species, particularly those already being impacted by habitat loss and fragmentation, hunting, pollution, disease, and competition from invasive species. PVA provides a method to examine the impact of climate change that goes beyond predicting net changes in amount and location of suitable habitat available under future climate scenarios. The linkage between species demography and the landscape available with PVA models allows for examining how a species’ dispersal and colonization abilities will be able to keep pace with the rate of change. Recent work by Brook et al. (2009) and Keith et al. (2008) demonstrates how a species risk of extinction under climate change depends on complex interactions between life history characteristics and the landscape.

An important advantage of PVA compared to alternative methods of assessment is that it can incorporate the effects of multiple impacts and conservation measures. Thus, a model can simulate effects of habitat loss (through decrease in carrying capacity of affected populations), harvest or poaching (through reduced survival rates in populations targeted), climate change (by changes in several model parameters, as discussed above), as well as conservation actions that are designed to mitigate against these threats, such as reintroductions, translocations, protected areas, habitat corridors and habitat restoration and improvement. In some cases, different threats may exacerbate each other’s impact. For example, habitat loss may result in smaller populations, which may make them more vulnerable to affects of hunting (e.g., due to Allee effects) or climate change. Thus, it is important to build PVAs that incorporate all known threats to the populations of the species, rather than focusing on one threat or one conservation measure.

Acknowledgments

This material is based in part upon work supported by the National Aeronautics and Space Administration under Grant No. NNX09AK19G issued through the NASA Biodiversity Program.

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Wintle, B. A., S.A. Bekessy, L.A. Venier, J. L. Pearce, and R. A. Chisholm. 2005. “Utility of Dynamic-Landscape Metapopulation Models for Sustainable Forest Management.” Conservation Biology 19:1930-1943.

Table 1: Types of PVA models (modified from Akçakaya and Brook 2008)