Utility of Dynamic-Landscape Metapopulation Models for Sustainable Forest Management

Abstract:  We evaluated the utility of combining metapopulation models with landscape-level forest-dynamics models to assess the sustainability of forest management practices. We used the Brown Creeper ( Certhia americana ) in the boreal forests of northern Ontario as a case study. We selected the Brown Creeper as a potential indicator of sustainability because it is relatively common in the region but is dependent on snags and old trees for nesting and foraging; hence, it may be sensitive to timber harvesting. For the modeling we used RAMAS Landscape, a software package that integrates RAMAS GIS, population-modeling software, and LANDIS, forest-dynamics modeling software. Predictions about the future floristic composition and structure of the landscape under a variety of management and natural disturbance scenarios were derived using LANDIS. We modeled eight alternative forest management scenarios, ranging in intensity from no timber harvesting and a natural fire regime to intensive timber harvesting with salvage logging after fire. We predicted the response of the Brown Creeper metapopulation over a 160-year period and used future population size and expected minimum population size to compare the sustainability of the various management scenarios. The modeling methods were easy to apply and model predictions were sensitive to the differences among management scenarios, indicating that these methods may be useful for assessing and ranking the sustainability of forest management options. Primary concerns about the method are the practical difficulties associated with incorporating fire stochasticity in prediction uncertainty and the number of model assumptions that must be made and tested with sensitivity analysis. We wrote new software to help quantify the contribution of landscape stochasticity to model prediction uncertainty.     

Integrating Landscape and Metapopulation Modeling Approaches: Viability of the Sharp-Tailed Grouse in a Dynamic Landscape

Abstract:  The lack of management experience at the landscape scale and the limited feasibility of experiments at this scale have increased the use of scenario modeling to analyze the effects of different management actions on focal species. However, current modeling approaches are poorly suited for the analysis of viability in dynamic landscapes. Demographic (e.g., metapopulation) models of species living in these landscapes do not incorporate the variability in spatial patterns of early successional habitats, and landscape models have not been linked to population viability models. We link a landscape model to a metapopulation model and demonstrate the use of this model by analyzing the effect of forest management options on the viability of the Sharp-tailed Grouse (  Tympanuchus phasianellus ) in the Pine Barrens region of northwestern Wisconsin (U.S.A.). This approach allows viability analysis based on landscape dynamics brought about by processes such as succession, disturbances, and silviculture. The landscape component of the model (LANDIS) predicts forest landscape dynamics in the form of a time series of raster maps. We combined these maps into a time series of patch structures, which formed the dynamic spatial structure of the metapopulation component (RAMAS). Our results showed that the viability of Sharp-tailed Grouse was sensitive to landscape dynamics and demographic variables such as fecundity and mortality. Ignoring the landscape dynamics gave overly optimistic results, and results based only on landscape dynamics (ignoring demography) lead to a different ranking of the management options than the ranking based on the more realistic model incorporating both landscape and demographic dynamics. Thus, models of species in dynamic landscapes must consider habitat and population dynamics simultaneously.     

Information processing in models for density-dependent emigration: A comparison

Density-dependent emigration has been recognized as a fitness enhancing strategy. Yet, especially in the modelling literature there is no consensus about how density-dependent emigration should quantitatively be incorporated into metapopulation models. In this paper we compare the performance of five different dispersal strategies (defined by the functional link between density and emigration probability). Four of these strategies are based on published functional relationships between local population density and emigration probability, one assumes density-independent dispersal. We use individual-based simulations of time-discrete metapopulation dynamics and conduct evolution experiments for a broad range of values for dispersal mortality and environmental stochasticity. For each set of these conditions we analyze the evolution of emigration rates in ‘monoculture experiments’ (with only one type of dispersal strategy used by all individuals in the metapopulation) as well as in selection experiments that allow a pair-wise comparison of the performance of each functional type. We find that a single-parameter ‘asymptotic threshold’ strategy - derived from the marginal value theorem - with a decelerating increase of emigration rate with increasing population density, out-competes any other strategy, i.e. density-independent emigration, a ‘linear threshold’ strategy and a flexible three-parameter strategy. Only when environmental conditions select for extremely high emigration probabilities (close to one), strategies may perform approximately equally. A simple threshold strategy derived for the case of continuous population growth performs even worse than the density-independent strategy. As the functional type of the dispersal function implemented in metapopulation models may severely affect predictions concerning the survival of populations, range expansion, or community changes we clearly recommend to carefully select adequate functions to model density-dependent dispersal.     

Dispersal in Spatially Explicit Population Models

Abstract: Ruckelshaus et al. (1997) outlined a simulation model of dispersal between patches in a fragmented landscape. They showed that dispersal success—the proportion of dispersers successfully locating a patch—was particularly sensitive to errors in dispersal mortality and concluded that this limits the utility of spatially explicit population models in conservation biology. I contend that, although they explored error propagation in a simple dispersal model, they did not explore how errors are propagated in spatially explicit population models, as no consideration of population processes was included. I developed a simple simulation model to investigate the effect of varying dispersal success on predictions of patch occupancy and population viability, the conventional outputs of spatially explicit population models. The model simulates births and deaths within habitat patches and dispersal as the transfer of individuals between them. Model predictions were sensitive to changes in dispersal success across a restricted range of within-patch growth rates, which depended on the dispersal initiation mechanism, patch carrying capacities, and number of generations simulated. Predictions of persistence and patch occupancy were generally more sensitive to changes in dispersal success (1) under presaturation rather than saturation dispersal; (2) at lower patch carrying capacities; and (3) over longer time periods. The framework I present provides a means of assessing, quantitatively, the regions of parameter space for which differences in dispersal success are likely to have a large effect on population model outputs. Investigating the effect of the representation of dispersal behavior within the demographic and landscape context provides a more useful assessment of whether our lack of knowledge is likely to cause unacceptable uncertainty in the predictions of spatially explicit population models.     

Conservation Corridors and Contagious Disease: A Cautionary Note

Recent conservation proposals frequently include the establishment of corridors to connect isolated patches of wildlife habitat. Much attention has been focused on the potential benefits of corridors with little note given to potentially adverse consequences. A simulation model is developed here to study the effect of corridors on the survival of a metapopulation in the presence of a fatal disease that is spread by direct contact between susceptible and infected individuals. For the disease modeled here, a landscape of patches connected by corridors generally suffers fewer metapopulation extinctions than a landscape of isolated patches. However, under a narrow range of conditions, results suggest that corridors may dramatically increase the probability of metapopulation extinction. This occurs when disease-induced mortality is low enough to allow infected individuals to spread the disease, but high enough to reduce population levels to the point that random demographic and environmental events cause frequent metapopulation extinctions. This has important implications for the design and management of conservation reserve networks. Although discussion focuses primarily on conservation corridors, the model results apply to any management techniques that increase the movement of individuals among populations.     

The control of emigration and its consequences for the survival of populations

Dispersal is the key process enhancing the long-term persistence of metapopulations in heterogeneous and dynamic landscapes. However, any individual emigrating from a occupied patch also increases the risk of local population extinction. The consequences of this increase for metapopulation persistence likely depend on the control of emigration. In this paper, we present results of individual-based simulations to compare the consequences of density-independent (DIE) and density-dependent (DDE) emigration on the extinction risk of local populations and a two-patch metapopulation. (1) For completely isolated patches extinction risk increases linearly with realised emigration rates in the DIE scenario. (2) For the DDE scenario extinction risk is nearly insensitive to emigration as longs as emigration probabilities remain below ≈0.2. Survival chances are up to half an order of magnitude larger than for populations with DIE. (3) For low dispersal mortality both modes of emigration increase survival of a metapopulation by ca. one order of magnitude. (4) For high dispersal mortality only DDE can improve the global survival chances of the metapopulation. (5) With DDE individuals are only removed from a population at high population density and the risk of extinction due to demographic stochasticity is thus much smaller compared to the DIE scenario.With density-dependent emigration prospects of metapopulations survival may thus be much higher compared to a system with density-independent emigration. Consequently, the knowledge about the factors driving emigration may significantly affect our conclusions concerning the conservation status of species.     

Demographic stochasticity reduces the synchronizing effect of dispersal in predator-prey metapopulations

Dispersal may affect predator-prey metapopulations by rescuing local sink populations from extinction or by synchronizing population dynamics across the metapopulation, increasing the risk of regional extinction. Dispersal is likely influenced by demographic stochasticity, however, particularly because dispersal rates are often very low in metapopulations. Yet the effects of demographic stochasticity on predator-prey metapopulations are not well known. To that end, I constructed three models of a two-patch predator-prey system. The models constitute a hierarchy of complexity, allowing direct comparisons. Two models included demographic stochasticity (pure jump process [PJP] and stochastic differential equations [SDE]), and the third was deterministic (ordinary differential equations [ODE]). One stochastic model (PJP) treated population sizes as discrete, while the other (SDE) allowed population sizes to change continuously. Both stochastic models only produced synchronized predator-prey dynamics when dispersal was high for both trophic levels. Frequent dispersal by only predators or prey in the PJP and SDE spatially decoupled the trophic interaction, reducing synchrony of the non-dispersive species. Conversely, the ODE generated synchronized predator-prey dynamics across all dispersal rates, except when initial conditions produced anti-phase transients. These results indicate that demographic stochasticity strongly reduces the synchronizing effect of dispersal, which is ironic because demographic stochasticity is often invoked post hoc as a driver of extinctions in synchronized metapopulations.     

A model for behavioral regulation of metapopulation dynamics

Habitat fragmentation can decrease local population persistence by reducing connectivity, which is a function of dispersal of individuals among habitat fragments. Dispersal is often treated as diffusion in population models, even though for many species it is a result of a series of behavioral decisions. We developed a metapopulation model to explore the potential importance of dispersal behaviors in driving metapopulation dynamics. We incorporated types of behavior that affect dispersal—colonization inhibiting, colonization enhancing, extinction inhibiting, extinction enhancing, rescue enhancing, rescue inhibiting—into Levins’ (1969) metapopulation model and projected occupancy rates for a variety of parameter values. Examples from the literature of behaviors associated with each of these parameters are provided. Our model simplifies into previously published metapopulation models that incorporate only a single behavior, and we present a density-dependent rescue function that leads to multiple non-zero equilibria. We found a variety of behavioral effects on metapopulations. Rescue enhancement fills patches faster than does colonization enhancement or extinction inhibition, and declines in patch occupancy are moderate with extinction enhancement, but colonization inhibition causes metapopulation extinction. We also found that with colonization and extinction inhibitions, equilibrium patch occupancy is inversely related to patch turnover rate. With density-dependent rescue, persistence depends not only on the strength of the strong rescue effect, but also on having a sufficient initial fraction of patches occupied; the stronger the rescue effect, the lower this fraction can be. This study suggests that dispersal behavior can have strong influences on metapopulation dynamics. It confirms the importance of understanding the relationship between landscape structure and dispersal behavior in understanding population persistence.     

Comparison of Two Types of Metapopulation Models in Real and Artificial Landscapes

Abstract: Application of metapopulation models is becoming increasingly widespread in the conservation of species in fragmented landscapes. We provide one of the first detailed comparisons of two of the most common modeling techniques, incidence function models and stage-based matrix models, and test their accuracy in predicting patch occupancy for a real metapopulation. We measured patch occupancies and demographic rates for regional populations of the Florida scrub lizard (   Sceloporus woodi ) and compared the observed occupancies with those predicted by each model. Both modeling strategies predicted patch occupancies with good accuracy ( 77–80%) and gave similar results when we compared hypothetical management scenarios involving removal of key habitat patches and degradation of habitat quality. To compare the two modeling approaches over a broader set of conditions, we simulated metapopulation dynamics for 150 artificial landscapes composed of equal-sized patches (2–1024 ha) spaced at equal distances (50–750 m). Differences in predicted patch occupancy were small to moderate (<20%) for about 74% of all simulations, but 22% of the landscapes had differences openface> 50%. Incidence function models and stage-based matrix models differ in their approaches, assumptions, and requirements for empirical data, and our findings provide evidence that the two models can produce different results. We encourage researchers to use both techniques and further examine potential differences in model output. The feasibility of obtaining data for population modeling varies widely among species and limits the modeling approaches appropriate for each species. Understanding different modeling approaches will become increasingly important as conservation programs undertake the challenge of managing for multiple species in a landscape context.     

Minimum viable metapopulation size, extinction debt, and the conservation of a declining species.

A key question facing conservation biologists is whether declines in species' distributions are keeping pace with landscape change, or whether current distributions overestimate probabilities of future persistence. We use metapopulations of the marsh fritillary butterfly Euphydryas aurinia in the United Kingdom as a model system to test for extinction debt in a declining species. We derive parameters for a metapopulation model (incidence function model, IFM) using information from a 625-km2 landscape where habitat patch occupancy, colonization, and extinction rates for E. aurinia depend on patch connectivity, area, and quality. We then show that habitat networks in six extant metapopulations in 16-km2 squares were larger, had longer modeled persistence times (using IFM), and higher metapopulation capacity (lambdaM) than six extinct metapopulations. However, there was a > 99% chance that one or more of the six extant metapopulations would go extinct in 100 years in the absence of further habitat loss. For 11 out of 12 networks, minimum areas of habitat needed for 95% persistence of metapopulation simulations after 100 years ranged from 80 to 142 ha (approximately 5-9% of land area), depending on the spatial location of habitat. The area of habitat exceeded the estimated minimum viable metapopulation size (MVM) in only two of the six extant metapopulations, and even then by only 20%. The remaining four extant networks were expected to suffer extinction in 15-126 years. MVM was consistently estimated as approximately 5% of land area based on a sensitivity analysis of IFM parameters and was reduced only marginally (to approximately 4%) by modeling the potential impact of long-distance colonization over wider landscapes. The results suggest a widespread extinction debt among extant metapopulations of a declining species, necessitating conservation management or reserve designation even in apparent strongholds. For threatened species, metapopulation modeling is a potential means to identify landscapes near to extinction thresholds, to which conservation measures can be targeted for the best chance of success.     

A stochastic model for estimating sustainable limits to wildlife mortality in a changing world

Human-caused mortality of wildlife is a pervasive threat to biodiversity. Assessing the population-level impact of fisheries bycatch and other human-caused mortality of wildlife has typically relied upon deterministic methods. However, population declines are often accelerated by stochastic factors that are not accounted for in such conventional methods. Building on the widely applied potential biological removal (PBR) equation, we devised a new population modeling approach for estimating sustainable limits to human-caused mortality and applied it in a case study of bottlenose dolphins affected by capture in an Australian demersal otter trawl fishery. Our approach, termed sustainable anthropogenic mortality in stochastic environments (SAMSE), incorporates environmental and demographic stochasticity, including the dependency of offspring on their mothers. The SAMSE limit is the maximum number of individuals that can be removed without causing negative stochastic population growth. We calculated a PBR of 16.2 dolphins per year based on the best abundance estimate available. In contrast, the SAMSE model indicated that only 2.3–8.0 dolphins could be removed annually without causing a population decline in a stochastic environment. These results suggest that reported bycatch rates are unsustainable in the long term, unless reproductive rates are consistently higher than average. The difference between the deterministic PBR calculation and the SAMSE limits showed that deterministic approaches may underestimate the true impact of human-caused mortality of wildlife. This highlights the importance of integrating stochasticity when evaluating the impact of bycatch or other human-caused mortality on wildlife, such as hunting, lethal control measures, and wind turbine collisions. Although population viability analysis (PVA) has been used to evaluate the impact of human-caused mortality, SAMSE represents a novel PVA framework that incorporates stochasticity for estimating acceptable levels of human-caused mortality. It offers a broadly applicable, stochastic addition to the demographic toolbox to evaluate the impact of human-caused mortality on wildlife.     

The role of habitat disturbance and recovery in metapopulation persistence

Classical metapopulation theory assumes a static landscape. However, empirical evidence indicates many metapopulations are driven by habitat succession and disturbance. We develop a stochastic metapopulation model, incorporating habitat disturbance and recovery, coupled with patch colonization and extinction, to investigate the effect of habitat dynamics on persistence. We discover that habitat dynamics play a fundamental role in metapopulation dynamics. The mean number of suitable habitat patches is not adequate for characterizing the dynamics of the metapopulation. For a fixed mean number of suitable patches, we discover that the details of how disturbance affects patches and how patches recover influences metapopulation dynamics in a fundamental way. Moreover, metapopulation persistence is dependent not only on the average lifetime of a patch, but also on the variance in patch lifetime and the synchrony in patch dynamics that results from disturbance. Finally, there is an interaction between the habitat and metapopulation dynamics, for instance declining metapopulations react differently to habitat dynamics than expanding metapopulations. We close, emphasizing the importance of using performance measures appropriate to stochastic systems when evaluating their behavior, such as the probability distribution of the state of the metapopulation, conditional on it being extant (i.e., the quasistationary distribution).     

Parameterizing,evaluating and comparing metapopulation models with data from individual-based simulations

Due to the lack of sufficient data and appropriate ecological information parameterizing predictive population dynamical models usually is a difficult task. The approach proposed in this study is meant to overcome this problem by using detailed individual-based simulations to generate artificial data. With short-term data samples, the models to be investigated can be parameterized and their predictions be compared. The flexibility of individual-based simulations as experimental tools also facilitates the evaluation and comparison of different (aggregated) model types. The presented approach is a step towards unifying models of different complexity. As an example we applied it to two metapopulation models of insect species in a highly fragmented landscape: the well-known incidence function model with a patch-based representation of space and a grid-based analogue. The models are tested with respect to their data requirement and recommendations for a better data sampling are derived.     

Why are metapopulations so rare?

Roughly 40 years after its introduction, the metapopulation concept is central to population ecology. The notion that local populations and their dynamics may be coupled by dispersal is without any doubt of great importance for our understanding of population-level processes. A metapopulation describes a set of subpopulations linked by (rare) dispersal events in a dynamic equilibrium of extinctions and recolonizations. In the large body of literature that has accumulated, the term "metapopulation" is often used in a very broad sense; most of the time it simply implies spatial heterogeneity. A number of reviews have recently addressed this problem and have pointed out that, despite the large and still growing popularity of the metapopulation concept, there are only very few empirical examples that conform with the strict classical metapopulation (CM) definition. In order to understand this discrepancy between theory and observation, we use an individual-based modeling approach that allows us to pinpoint the environmental conditions and the life-history attributes required for the emergence of a CM structure. We find that CM dynamics are restricted to a specific parameter range at the border between spatially structured but completely occupied and globally extinct populations. Considering general life-history attributes, our simulations suggest that CMs are more likely to occur in arthropod species than in (large) vertebrates. Since the specific type of spatial population structure determines conservation concepts, our findings have important implications for conservation biology. Our model suggests that most spatially structured populations are panmictic, patchy, or of mainland-island type, which makes efforts spent on increasing connectivity (e.g., corridors) questionable. If one does observe a true CM structure, this means that the focal metapopulation is on the brink of extinction and that drastic conservation measures are needed.     

The Application of Neutral Landscape Models in Conservation Biology

Neutral landscape models, derived from percolation theory in the field of landscape ecology, are grid-based maps in which complex habitat distributions are generated by random or fractal algorithms. This grid-based representation of landscape structure is compatible with the raster-based format of geographical information systems (GIS), which facilitates comparisons between theoretical and real landscapes. Neutral landscape models permit the identification of critical thresholds in connectivity, which can be used to predict when landscapes will become fragmented. The coupling of neutral landscape models with generalized population models, such as metapopulation theory, provides a null model for generating predictions about population dynamics in fragmented landscapes. Neutral landscape models can contribute to the following applications in conservation: (1) incorporation of complex spatial patterns in (meta)population models; (2) identification of species' perceptions of landscape structure; (3) determination of landscape connectivity; (4) evaluation of the consequences of habitat fragmentation for population subdivision; (5) identification of the domain of metapopulation dynamics; (6) prediction of the occurrence of extinction thresholds; ( 7) determination of the genetic consequences of habitat fragmentation; and (8) reserve design and ecosystem management. This generalized, spatially explicit framework bridges the gap between spatially implicit, patch-based models and spatially realistic GIS applications which are usually parameterized for a single species in a specific landscape. Development of a generalized, spatially explicit framework is essential in conservation biology because we will not be able to develop individual models for every species of management concern.     

Application of multiple-population viability analysis to evaluate species recovery alternatives

Population viability analysis (PVA) is a powerful conservation tool, but it remains impractical for many species, particularly species with multiple, broadly distributed populations for which collecting suitable data can be challenging. A recently developed method of multiple-population viability analysis (MPVA), however, addresses many limitations of traditional PVA. We built on previous development of MPVA for Lahontan cutthroat trout (LCT) (Oncorhynchus clarkii henshawi), a species listed under the U.S. Endangered Species Act, that is distributed broadly across habitat fragments in the Great Basin (U.S.A.). We simulated potential management scenarios and assessed their effects on population sizes and extinction risks in 211 streams, where LCT exist or may be reintroduced. Conservation populations (those managed for recovery) tended to have lower extinction risks than nonconservation populations (mean = 19.8% vs. 52.7%), but not always. Active management or reprioritization may be warranted in some cases. Eliminating non-native trout had a strong positive effect on overall carrying capacities for LCT populations but often did not translate into lower extinction risks unless simulations also reduced associated stochasticity (to the mean for populations without non-native trout). Sixty fish or 5–10 fish/km was the minimum reintroduction number and density, respectively, that provided near-maximum reintroduction success. This modeling framework provided crucial insights and empirical justification for conservation planning and specific adaptive management actions for this threatened species. More broadly, MPVA is applicable to a wide range of species exhibiting geographic rarity and limited availability of abundance data and greatly extends the potential use of empirical PVA for conservation assessment and planning.     

Past,present, and future of a freshwater fish metapopulation in a threatened landscape

It is well documented that hydropower plants can affect the dynamics of fish populations through landscape alterations and the creation of new barriers. Less emphasis has been placed on the examination of the genetic consequences for fish populations of the construction of dams. The relatively few studies that focus on genetics often do not consider colonization history and even fewer tend to use this information for conservation purposes. As a case study, we used a 3‐pronged approach to study the influence of historical processes, contemporary landscape features, and potential future anthropogenic changes in landscape on the genetic diversity of a fish metapopulation. Our goal was to identify the metapopulation's main attributes, detect priority areas for conservation, and assess the consequences of the construction of hydropower plants for the persistence of the metapopulation. We used microsatellite markers and coalescent approaches to examine historical colonization processes, traditional population genetics, and simulations of future populations under alternate scenarios of population size reduction and gene flow. Historical gene flow appeared to have declined relatively recently and contemporary populations appeared highly susceptible to changes in landscape. Gene flow is critical for population persistence. We found that hydropower plants could lead to a rapid reduction in number of alleles and to population extirpation 50–80 years after their construction. More generally, our 3‐pronged approach for the analyses of empirical genetic data can provide policy makers with information on the potential impacts of landscape changes and thus lead to more robust conservation efforts.     

Developing Metapopulation Connectivity Criteria from Genetic and Habitat Data to Recover the Endangered Mexican Wolf

Restoring connectivity between fragmented populations is an important tool for alleviating genetic threats to endangered species. Yet recovery plans typically lack quantitative criteria for ensuring such population connectivity. We demonstrate how models that integrate habitat, genetic, and demographic data can be used to develop connectivity criteria for the endangered Mexican wolf (Canis lupus baileyi), which is currently being restored to the wild from a captive population descended from 7 founders. We used population viability analysis that incorporated pedigree data to evaluate the relation between connectivity and persistence for a restored Mexican wolf metapopulation of 3 populations of equal size. Decreasing dispersal rates greatly increased extinction risk for small populations (<150–200), especially as dispersal rates dropped below 0.5 genetically effective migrants per generation. We compared observed migration rates in the Northern Rocky Mountains (NRM) wolf metapopulation to 2 habitat‐based effective distance metrics, least‐cost and resistance distance. We then used effective distance between potential primary core populations in a restored Mexican wolf metapopulation to evaluate potential dispersal rates. Although potential connectivity was lower in the Mexican wolf versus the NRM wolf metapopulation, a connectivity rate of >0.5 genetically effective migrants per generation may be achievable via natural dispersal under current landscape conditions. When sufficient data are available, these methods allow planners to move beyond general aspirational connectivity goals or rules of thumb to develop objective and measurable connectivity criteria that more effectively support species recovery. The shift from simple connectivity rules of thumb to species‐specific analyses parallels the previous shift from general minimum‐viable‐population thresholds to detailed viability modeling in endangered species recovery planning. Desarrollo de Criterios de Conectividad Metapoblacional a Partir de Datos Genéticos y de Hábitat para Recuperar al Lobo Mexicano en Peligro de Extinción     

Extinction-effective population index: incorporating life-history variations in population viability analysis

Viability status of populations is a commonly used measure for decision-making in the management of populations. One of the challenges faced by managers is the need to consistently allocate management effort among populations. This allocation should in part be based on comparison of extinction risks among populations. Unfortunately, common criteria that use minimum viable population size or count-based population viability analysis (PVA) often do not provide results that are comparable among populations, primarily because they lack consistency in determining population size measures and threshold levels of population size (e.g., minimum viable population size and quasi-extinction threshold). Here I introduce a new index called the "extinction-effective population index," which accounts for differential effects of demographic stochasticity among organisms with different life-history strategies and among individuals in different life stages. This index is expected to become a new way of determining minimum viable population size criteria and also complement the count-based PVA. The index accounts for the difference in life-history strategies of organisms, which are modeled using matrix population models. The extinction-effective population index, sensitivity, and elasticity are demonstrated in three species of Pacific salmonids. The interpretation of the index is also provided by comparing them with existing demographic indices. Finally, a measure of life-history-specific effect of demographic stochasticity is derived.