Measuring impacts on species with models and metrics of varying ecological and computational complexity

Approaches to assess the impacts of landscape disturbance scenarios on species range from metrics based on patterns of occurrence or habitat to comprehensive models that explicitly include ecological processes. The choice of metrics and models affects how impacts are interpreted and conservation decisions. We explored the impacts of 3 realistic disturbance scenarios on 4 species with different ecological and taxonomic traits. We used progressively more complex models and metrics to evaluate relative impact and rank of scenarios on the species. Models ranged from species distribution models that relied on implicit assumptions about environmental factors and species presence to highly parameterized spatially explicit population models that explicitly included ecological processes and stochasticity. Metrics performed consistently in ranking different scenarios in order of severity primarily when variation in impact was driven by habitat amount. However, they differed in rank for cases where dispersal dynamics were critical in influencing metapopulation persistence. Impacts of scenarios on species with low dispersal ability were better characterized using models that explicitly captured these processes. Metapopulation capacity provided rank orders that most consistently correlated with those from highly parameterized and data-rich models and incorporated information about dispersal with little additional computational and data cost. Our results highlight the importance of explicitly considering species’ ecology, spatial configuration of habitat, and disturbance when choosing indicators of species persistence. We suggest using hybrid approaches that are a mixture of simple and complex models to improve multispecies assessments.     

Importance of individual and environmental variation for invasive species spread: a spatial integral projection model

Plant survival, growth, and flowering are size dependent in many plant populations but also vary among individuals of the same size. This individual variation, along with variation in dispersal caused by differences in, e.g., seed release height, seed characteristics, and wind speed, is a key determinant of the spread rate of species through homogeneous landscapes. Here we develop spatial integral projection models (SIPMs) that include both demography and dispersal with continuous state variables. The advantage of this novel approach over discrete-stage spread models is that the effect of variation in plant size and size-dependent vital rates can be studied at much higher resolution. Comparing Neubert-Caswell matrix models to SIPMs allowed us to assess the importance of including individual variation in the models. As a test case we parameterized a SIPM with previously published data on the invasive monocarpic thistle Carduus nutans in New Zealand. Spread rate (c*) estimates were 34% lower than for standard spatial matrix models and stabilized with as few as seven evenly distributed size classes. The SIPM allowed us to calculate spread rate elasticities over the range of plant sizes, showing the size range of seedlings that contributed most to c* through their survival, growth and reproduction. The annual transitions of these seedlings were also the most important ones for local population growth (lambda). However, seedlings that reproduced within a year contributed relatively more to c* than to lambda. In contrast, plants that grow over several years to reach a large size and produce many more seeds, contributed relatively more to lambda than to c*. We show that matrix models pick up some of these details, while other details disappear within wide size classes. Our results show that SIPMs integrate various sources of variation much better than discrete-stage matrix models. Simpler, heuristic models, however, remain very valuable in studies where the main goal is to investigate the general impact of a life history stage on population dynamics. We conclude with a discussion of future extensions of SIPMs, including incorporation of continuous time and environmental drivers.     

Seed terminal velocity, wind turbulence, and demography drive the spread of an invasive tree in an analytical model

Little is known about the relative importance of mechanistic drivers of plant spread, particularly when long-distance dispersal (LDD) events occur. Most methods to date approach LDD phenomenologically, and all mechanistic models, with one exception, have been implemented through simulation. Furthermore, the few recent mechanistically derived spread models have examined the relative role of different dispersal parameters using simulations, and a formal analytical approach has not yet been implemented. Here we incorporate an analytical mechanistic wind dispersal model (WALD) into a demographic matrix model within an analytical integrodifference equation spread model. We carry out analytical perturbation analysis on the combined model to determine the relative effects of dispersal and demographic traits and wind statistics on the spread of an invasive tree. Models are parameterized using data collected in situ and tested using independent data on historical spread. Predicted spread rates and direction match well the two historical phases of observed spread. Seed terminal velocity has the greatest potential influence on spread rate, and three wind properties (turbulence coefficient, mean horizontal wind speed, and standard deviation of vertical wind speed) are also important. Fecundity has marginal importance for spread rate, but juvenile survival and establishment are consistently important. This coupled empirical/theoretical framework enables prediction of plant spread rate and direction using fundamental dispersal and demographic parameters and identifies the traits and environmental conditions that facilitate spread. The development of an analytical perturbation analysis for a mechanistic spread model will enable multispecies comparative studies to be easily implemented in the future.     

Unveiling human-assisted dispersal mechanisms in invasive alien insects: Integration of spatial stochastic simulation and phenology models

Capturing the spread of biological invasions in heterogeneous landscapes is a complex modelling task where information on both dispersal and population dynamics needs to be integrated. Spatial stochastic simulation and phenology models have rarely been combined to assist in the study of human-assisted long-distance dispersal events.Here we develop a process-based spatially explicit landscape-extent simulation model that considers the spread and detection of invasive insects. Natural and human-assisted dispersal mechanisms are modelled with an individual-based approach using negative exponential and negative power law dispersal kernels and gravity models. The model incorporates a phenology sub-model that uses daily temperature grids for the prediction and timing of the population dynamics in each habitat patch. The model was applied to the study of the invasion by the important maize pest western corn rootworm (WCR) Diabrotica virgifera ssp. virgifera in Europe. We parameterized and validated the model using maximum likelihood and simulation methods from the historical invasion of WCR in Austria.WCR was found to follow stratified dispersal where international transport networks in the Danube basin played a key role in the occurrence of long-distance dispersal events. Detection measures were found to be effective and altitude had a significant effect on limiting the spread of WCR. Spatial stochastic simulation combined with phenology models, maximum likelihood methods and predicted versus observed regression showed a high degree of flexibility that captured the salient features of WCR spread in Austria. This modelling approach is useful because it allows to fully exploit and the often limited and heterogeneous information available regarding the population dynamics and dispersal of alien invasive insects.     

Incorporating animal behavior into seed dispersal models: implications for seed shadows

Seed dispersal fundamentally influences plant population and community dynamics but is difficult to quantify directly. Consequently, models are frequently used to describe the seed shadow (the seed deposition pattern of a plant population). For vertebrate-dispersed plants, animal behavior is known to influence seed shadows but is poorly integrated in seed dispersal models. Here, we illustrate a modeling approach that incorporates animal behavior and develop a stochastic, spatially explicit simulation model that predicts the seed shadow for a primate-dispersed tree species (Virola calophylla, Myristicaceae) at the forest stand scale. The model was parameterized from field-collected data on fruit production and seed dispersal, behaviors and movement patterns of the key disperser, the spider monkey (Ateles paniscus), densities of dispersed and non-dispersed seeds, and direct estimates of seed dispersal distances. Our model demonstrated that the spatial scale of dispersal for this V. calophylla population was large, as spider monkeys routinely dispersed seeds >100 m, a commonly used threshold for long-distance dispersal. The simulated seed shadow was heterogeneous, with high spatial variance in seed density resulting largely from behaviors and movement patterns of spider monkeys that aggregated seeds (dispersal at their sleeping sites) and that scattered seeds (dispersal during diurnal foraging and resting). The single-distribution dispersal kernels frequently used to model dispersal substantially underestimated this variance and poorly fit the simulated seed-dispersal curve, primarily because of its multimodality, and a mixture distribution always fit the simulated dispersal curve better. Both seed shadow heterogeneity and dispersal curve multimodality arose directly from these different dispersal processes generated by spider monkeys. Compared to models that did not account for disperser behavior, our modeling approach improved prediction of the seed shadow of this V. calophylla population. An important function of seed dispersal models is to use the seed shadows they predict to estimate components of plant demography, particularly seedling population dynamics and distributions. Our model demonstrated that improved seed shadow prediction for animal-dispersed plants can be accomplished by incorporating spatially explicit information on disperser behavior and movements, using scales large enough to capture routine long-distance dispersal, and using dispersal kernels, such as mixture distributions, that account for spatially aggregated dispersal.     

Evaluating the effect of corridors and landscape heterogeneity on dispersal probability: a comparison of three spatially explicit modelling approaches

Spatially explicit simulation models of varying degree of complexity are increasingly used in landscape and species management and conservation. The choice as to which type of model to employ in a particular situation, is however, far too often governed by logistic constraints and the personal preferences of the modeller, rather than by a critical evaluation of model performance. We present a comparison of three common spatial simulation approaches (patch-based incidence-function model (IFM), individual-based movement model (IBMM), individual-based population model including detailed behaviour and demographics (IBPM)). The IBPM was analysed in two versions (IBPM_st and IBPM_dyn). Both assumed spatial heterogeneity of the matrix, but the IBPM_dyn in addition included temporal matrix dynamics. The models were developed with a shared minimum objective, namely to predict dynamics of individuals or populations in space given a specific configuration of habitat patches. We evaluated how the choice of model influenced predictions regarding the effect of patch and corridor configuration on dispersal probabilities and the number of successful immigrants of a simulated small mammal. Model results were analysed both at the level of the entire habitat network and at the level of individual patches.All models produced similar rankings of alternative habitat networks, but large discrepancies existed between absolute estimates of dispersal probabilities and the number of successful immigrants predicted by the different models. Generally, predicted dispersal probabilities were highest in the IBMM, intermediate in the IFM and the IBPM_st and lowest in the IBPM_dyn. Observed differences were due both to differences in implementation (e.g. raster versus vector-based movement algorithms), the chosen level of detail in landscape representation (e.g. matrix complexity) and the degree of behavioural realism included in the models (e.g. demography, differentiated mortality).The advantages and disadvantages of the three modelling approaches are discussed, as are the implications of the results for the recommended use of the three types of models in practical management.     

Using demography and movement behavior to predict range expansion of the southern sea otter

In addition to forecasting population growth, basic demographic data combined with movement data provide a means for predicting rates of range expansion. Quantitative models of range expansion have rarely been applied to large vertebrates, although such tools could be useful for restoration and management of many threatened but recovering populations. Using the southern sea otter (Enhydra lutris nereis) as a case study, we utilized integro-difference equations in combination with a stage-structured projection matrix that incorporated spatial variation in dispersal and demography to make forecasts of population recovery and range recolonization. In addition to these basic predictions, we emphasize how to make these modeling predictions useful in a management context through the inclusion of parameter uncertainty and sensitivity analysis. Our models resulted in hind-cast (1989-2003) predictions of net population growth and range expansion that closely matched observed patterns. We next made projections of future range expansion and population growth, incorporating uncertainty in all model parameters, and explored the sensitivity of model predictions to variation in spatially explicit survival and dispersal rates. The predicted rate of southward range expansion (median = 5.2 km/yr) was sensitive to both dispersal and survival rates; elasticity analysis indicated that changes in adult survival would have the greatest potential effect on the rate of range expansion, while perturbation analysis showed that variation in subadult dispersal contributed most to variance in model predictions. Variation in survival and dispersal of females at the south end of the range contributed most of the variance in predicted southward range expansion. Our approach provides guidance for the acquisition of further data and a means of forecasting the consequence of specific management actions. Similar methods could aid in the management of other recovering populations.     

The Value of Using Feasibility Models in Systematic Conservation Planning to Predict Landholder Management Uptake

Understanding the social dimensions of conservation opportunity is crucial for conservation planning in multiple‐use landscapes. However, factors that influence the feasibility of implementing conservation actions, such as the history of landscape management, and landholders’ willingness to engage are often difficult or time consuming to quantify and rarely incorporated into planning. We examined how conservation agencies could reduce costs of acquiring such data by developing predictive models of management feasibility parameterized with social and biophysical factors likely to influence landholders’ decisions to engage in management. To test the utility of our best‐supported model, we developed 4 alternative investment scenarios based on different input data for conservation planning: social data only; biological data only; potential conservation opportunity derived from modeled feasibility that incurs no social data collection costs; and existing conservation opportunity derived from feasibility data that incurred collection costs. Using spatially explicit information on biodiversity values, feasibility, and management costs, we prioritized locations in southwest Australia to control an invasive predator that is detrimental to both agriculture and natural ecosystems: the red fox (Vulpes vulpes). When social data collection costs were moderate to high, the most cost‐effective investment scenario resulted from a predictive model of feasibility. Combining empirical feasibility data with biological data was more cost‐effective for prioritizing management when social data collection costs were low (<4% of the total budget). Calls for more data to inform conservation planning should take into account the costs and benefits of collecting and using social data to ensure that limited funding for conservation is spent in the most cost‐efficient and effective manner.     

Predicting regional spread of non-native species using oceanographic models: validation and identification of gaps

Predicting spread is a central goal of invasion ecology. Within marine systems, researchers have increasingly made use of oceanographic circulation models to estimate currents and track species dispersal. However, the accuracy of these models for predicting biological patterns, particularly for non-native species, has generally not been validated. Particularly, we wished to examine the ability of models to predict physical and biological processes, which jointly determine the spread of marine larval organisms. We conducted two empirical studies—a recruitment study and a drift card study—along the coast of New England, USA, focusing on two invaders of concern—the European green crab (Carcinus maenas) and the Asian shore crab (Hemigrapsus sanguineus), to explicitly evaluate the ability of oceanographic models to predict patterns of spread. We used data from the large-scale drift card study to validate our ability to capture dispersal patterns driven purely by physical processes. Next, we conducted a recruitment study to evaluate our ability to reproduce patterns of biological dispersal. We were generally capable of reproducing drift cards patterns—suggesting that the physical mechanics in the model were predictive. However, predicted biological patterns were inconsistent—we were able to predict dispersal patterns for H. sanguineus but not for C. maenas. Our results highlight the importance of validating models and suggest that more work is necessary before we can reliably use oceanographic models to predict biological spread of intertidal organisms.     

Spatially mixed crops to control the stratified dispersal of airborne fungal diseases

Intraspecific crop diversification is thought to be a possible solution to the disease susceptibility of monocultured crops. We modelled the stratified dispersal of an airborne pathogen population in order to identify the spatial patterns of cultivar mixtures that could slow epidemic spread driven by dual dispersal mechanisms acting over both short and long distances. We developed a model to simulate the propagation of a fungal disease in a 2D field, including a reaction-diffusion model for short-distance disease dispersal, and a stochastic model for long-distance dispersal. The model was fitted to data for the spatio-temporal spread of faba bean rust (caused by Uromyces viciae-fabae) through a discontinuous field. The model was used to compare the effectiveness of eight different planting patterns of cultivar mixtures against a disease spread by short-distance and stratified dispersal. Our combined modelling approach provides a reasonably good fit with the observed data for the spread of faba bean rust. Similar predictive power could be expected for the management of resource-mediated invasions by other airborne fungi. If a disease spreads by short-distance dispersal, random mixtures can be used to slow the epidemic spread, since their spatial irregularity creates a natural barrier to the progression of a smooth epidemic wave. In the context of stratified dispersal, heterogeneous patterns should be used that include a minimum distance between susceptible units, which decreases the probability of infection by long-distance spore dispersal. We provide a simple framework for modelling the stratified dispersal of disease in a diversified crop. The model suggests that the spatial arrangement of components in cultivar mixtures has to accord with the dispersal characteristics of the pathogen in order to increase the efficiency of diversification strategies in agro-ecosystems and forestry. It can be applied in low input agriculture to manage pathogen invasion by intercropping and cultivar mixtures, and to design sustainable systems of land use.     

Estimating dispersal potential for marine larvae: dynamic models applied to scleractinian corals

Dispersal influences ecological dynamics, evolution, biogeography, and biodiversity conservation, but models of larval dispersal in marine organisms make simplifying assumptions that are likely to approximate poorly the temporal dynamics of larval survival and capacity for settlement. In particular, larval mortality rates are typically assumed to be constant throughout larval life; and all larvae are frequently assumed to acquire and lose competence at the same time. To improve upon these assumptions, we here develop simple models of dispersal potential that incorporate rates of mortality, and acquisition and loss of settlement competence. We fit these models to empirical competence and survival data for five scleractinian coral species, to test the models' ability to characterize empirical survival and competence patterns, and to estimate the dispersal potential implied by those patterns. The models fit the data well, incorporating qualitative features of competence and survival that traditional approaches to modeling dispersal do not, with important implications for dispersal potential. Most notably, there was high within-cohort variation in the duration of the competent period in all species, and this variation increases both self-recruitment and long-distance dispersal compared with models assuming a fixed competent period. These findings help to explain the seeming paradox of high genetic population structure, coupled with large geographic range size, observed in many coral species. More broadly, our approach offers a way to parsimoniously account for variation in competence dynamics in dispersal models, a phenomenon that our results suggest has important effects on patterns of connectivity in marine metapopulations.     

Patch Size and Connectivity Thresholds for Butterfly Habitat Restoration

Abstract:  Recovery of endangered species in highly fragmented habitats often requires habitat restoration. Selection of restoration sites typically involves too many options and too much uncertainty to reach a decision based on existing reserve design methods. The Fender's blue butterfly (  Icaricia icarioides fenderi ) survives in small, isolated patches of remnant prairie in Oregon's Willamette Valley—a habitat for which <0.5% of the original remains. Recovery of this species will require considerable habitat restoration. We investigated the potential of biologically based rules of thumb and more complex models to serve as tools in making land acquisitions. Based on Fender's blue dispersal behavior and demography, we have estimated that restored patches should be <1 km from existing habitat and at least 2 ha. We compared these rules to the results of two modeling approaches: an incidence function model and a spatially explicit simulation of demography and dispersal behavior. Not surprisingly, the simple rules and complex models all conclude that large (>2 ha) connected (<1 km) patches have the highest restoration value. The dispersal model, however, suggests that small, connected patches have more restoration value than large, isolated patches, whereas the incidence function model suggests that size and connectivity are equally important. These differences stem from model assumptions. We used incidence functions to predict long-term, stochastic, steady-state conditions and dispersal simulations to predict short-term (25-year) colonization dynamics. To apply our results in the context of selecting restoration sites on the ground, we recommend selecting nearby sites when short-term colonization dynamics are expected to be an important aspect of a species' biology.     

Negative relationship between dispersal distance and demography in butterfly metapopulations

Little is known about the connection between demography and dispersal in metapopulations. The meta-analysis of the population time series of five butterfly species indicated that (meta)population dynamics are driven by density-dependent factors. Inter-specific comparison reveals a significant inverse relationship between population growth rate and the magnitude of dispersal distance. As the range of dispersal distances is constrained by the patch system, dispersing individuals moving too far away would (probably) get lost. This generates selective pressures on individuals with a high dispersal propensity, but favors individuals investing more in reproduction and results in a higher (meta)population growth rate. From a conservation perspective, individuals from (meta)populations and species sacrificing dispersal for the sake of reproductive performances are most vulnerable because of their higher sensitivity to stochastic events: the temporal variation of growth rate was much higher in the two metapopulations where dispersal was limited.     

Importance of Habitat Quality and Landscape Connectivity for the Persistence of Endangered Natterjack Toads

Abstract: The natterjack toad (Bufo calamita) is endangered in several parts of its distribution, including Belgium, where it occurs mainly in artificial habitats. We parameterized a general model for natterjack population viability analysis (PVA) and tested its sensitivity to changes in the values of basic parameters. Then we assessed the relative efficiency of various conservation measures in 2 situations: a small isolated population and a system of 4 populations connected by rare dispersal movements. We based the population viability analysis on a stage‐structured model of natterjack population dynamics. We parameterized the model in the RAMAS GIS platform with vital rates obtained from our own field experience and from published studies. Simulated natterjack populations were highly sensitive to habitat quality (particularly pond drying), to dispersal from surrounding local populations, and to a lesser extent to values of fecundity and survival of terrestrial stages. Population trajectories were nearly insensitive to initial abundances, carrying capacities, and the frequency of extreme climatic conditions. The simulations showed that in habitats with highly ephemeral ponds, where premetamorphosis mortality was high, natterjack populations nearly always had a very high extinction risk. We also illustrated how low dispersal rates (<1 dispersing individual/generation) efficiently rescued declining local populations. Such source‐sink dynamics demonstrate that the identification and management of source populations should be a high priority.     

Increasing the Accuracy of Productivity and Survival Estimates in Assessing Landbird Population Status

Abstract:  The conservation of species with declining populations requires information on population demography and identification of factors that limit population growth. For landbird species, an understanding of large-scale population declines often requires assessment of local population processes, including the production of offspring, the survival of those offspring, and adult survival. Population growth has been modeled for several species of landbirds to date, and these studies have provided important information on relationships between population status and population-limiting factors. Several recent studies have illuminated field methods and analytical techniques that can aid in increasing the accuracy of productivity and survival estimates for population models. We reviewed these methods and recommend their implementation, including quantification of the season-long productivity of individuals, collection of empirical data on juvenile survival during the postfledging and overwintering periods, and incorporation of adult breeding dispersal into annual adult survival estimates. Such methods will allow for more accurate assessment of population status and provide a better understanding of the factors on which to focus our conservation efforts.     

How complex do models need to be to predict dispersal of threatened species through matrix habitats?

Persistence of species in fragmented landscapes depends on dispersal among suitable breeding sites, and dispersal is often influenced by the "matrix" habitats that lie between breeding sites. However, measuring effects of different matrix habitats on movement and incorporating those differences into spatially explicit models to predict dispersal is costly in terms of time and financial resources. Hence a key question for conservation managers is: Do more costly, complex movement models yield more accurate dispersal predictions? We compared the abilities of a range of movement models, from simple to complex, to predict the dispersal of an endangered butterfly, the Saint Francis' satyr (Neonympha mitchellii francisci). The value of more complex models differed depending on how value was assessed. Although the most complex model, based on detailed movement behaviors, best predicted observed dispersal rates, it was only slightly better than the simplest model, which was based solely on distance between sites. Consequently, a parsimony approach using information criteria favors the simplest model we examined. However, when we applied the models to a larger landscape that included proposed habitat restoration sites, in which the composition of the matrix was different than the matrix surrounding extant breeding sites, the simplest model failed to identify a potentially important dispersal barrier, open habitat that butterflies rarely enter, which may completely isolate some of the proposed restoration sites from other breeding sites. Finally, we found that, although the gain in predicting dispersal with increasing model complexity was small, so was the increase in financial cost. Furthermore, a greater fit continued to accrue with greater financial cost, and more complex models made substantially different predictions than simple models when applied to a novel landscape in which butterflies are to be reintroduced to bolster their populations. This suggests that more complex models might be justifiable on financial grounds. Our results caution against a pure parsimony approach to deciding how complex movement models need to be to accurately predict dispersal through the matrix, especially if the models are to be applied to novel or modified landscapes.     

Stochastic gravity models for modeling lake invasions

Freshwater aquatic systems in North America are being invaded by many different species, ranging from fish, mollusks, cladocerans to various bacteria and viruses. These invasions have serious ecological and economic impacts. Human activities such as recreational boating are an important pathway for dispersal. Gravity models are used to quantify the dispersal effect of human activity. Gravity models currently used in ecology are deterministic. This paper proposes the use of stochastic gravity models in ecology, which provides new capabilities both in model building and in potential model applications. These models allow us to use standard statistical inference tools such as maximum likelihood estimation and model selection based on information criteria. To facilitate prediction, we use only those covariates that are easily available from common data sources and can be forecasted in future. This is important for forecasting the spread of invasive species in geographical and temporal domain. The proposed model is portable, that is it can be used for estimating relative boater traffic and hence relative propagule pressure for the lakes not covered by current boater surveys. This makes our results broadly applicable to various invasion prediction and management models.     

Dispersal ecology of deadwood organisms and connectivity conservation

Limited knowledge of dispersal for most organisms hampers effective connectivity conservation in fragmented landscapes. In forest ecosystems, deadwood‐dependent organisms (i.e., saproxylics) are negatively affected by forest management and degradation globally. We reviewed empirically established dispersal ecology of saproxylic insects and fungi. We focused on direct studies (e.g., mark‐recapture, radiotelemetry), field experiments, and population genetic analyses. We found 2 somewhat opposite results. Based on direct methods and experiments, dispersal is limited to within a few kilometers, whereas genetic studies showed little genetic structure over tens of kilometers, which indicates long‐distance dispersal. The extent of direct dispersal studies and field experiments was small and thus these studies could not have detected long‐distance dispersal. Particularly for fungi, more studies at management‐relevant scales (1–10 km) are needed. Genetic researchers used outdated markers, investigated few loci, and faced the inherent difficulties of inferring dispersal from genetic population structure. Although there were systematic and species‐specific differences in dispersal ability (fungi are better dispersers than insects), it seems that for both groups colonization and establishment, not dispersal per se, are limiting their occurrence at management‐relevant scales. Because most studies were on forest landscapes in Europe, particularly the boreal region, more data are needed from nonforested landscapes in which fragmentation effects are likely to be more pronounced. Given the potential for long‐distance dispersal and the logical necessity of habitat area being a more fundamental landscape attribute than the spatial arrangement of habitat patches (i.e., connectivity sensu strict), retaining high‐quality deadwood habitat is more important for saproxylic insects and fungi than explicit connectivity conservation in many cases.     

Demographic Forecasting in Koala Conservation

Abstract: The koala currently needs conservation intervention. There is clear evidence of decline in many populations, but the existence of other stable or expanding populations offers the possibility of a variety of creative solutions to their conservation problems. The 1998 National Koala Conservation Strategy emphasizes the need to obtain demographic information and to use this information to assess management options for koalas. We need accurate diagnoses of the status of koala populations and forecasts of their demographic future with and without particular management actions. In a qualitative fashion, this process has been undertaken many times on a local and national scale. Quantitative demographic forecasting tools are increasingly available, and koala management could benefit from their application both at the scale of individual populations and more broadly. There is already a considerable body of suitable data on the dispersal, effects of normal and catastrophic environmental variation on reproduction and survival, and on the effects of habitat change. Demographic forecasting, however, is hampered because the full suite of information is rarely available from a single population. In two Queensland populations, retrospective population viability analyses provided forecasts that were in agreement with observed population trends. Work is needed to determine whether data from one population can be applied to other populations. Models can then be developed to make projections at a multipopulation level on the basis of local population dynamics and dispersal. Certain koala populations, because of their long history of study, offer the opportunity to test demographic models retrospectively. These tests will not only aid in fine-tuning the models for koala biology and data but will also assist with the more general process of validating the models.