On Valuing Information in Adaptive‐Management Models

Abstract: Active adaptive management looks at the benefit of using strategies that may be suboptimal in the near term but may provide additional information that will facilitate better management in the future. In many adaptive‐management problems that have been studied, the optimal active and passive policies (accounting for learning when designing policies and designing policy on the basis of current best information, respectively) are very similar. This seems paradoxical; when faced with uncertainty about the best course of action, managers should spend very little effort on actively designing programs to learn about the system they are managing. We considered two possible reasons why active and passive adaptive solutions are often similar. First, the benefits of learning are often confined to the particular case study in the modeled scenario, whereas in reality information gained from local studies is often applied more broadly. Second, management objectives that incorporate the variance of an estimate may place greater emphasis on learning than more commonly used objectives that aim to maximize an expected value. We explored these issues in a case study of Merri Creek, Melbourne, Australia, in which the aim was to choose between two options for revegetation. We explicitly incorporated monitoring costs in the model. The value of the terminal rewards and the choice of objective both influenced the difference between active and passive adaptive solutions. Explicitly considering the cost of monitoring provided a different perspective on how the terminal reward and management objective affected learning. The states for which it was optimal to monitor did not always coincide with the states in which active and passive adaptive management differed. Our results emphasize that spending resources on monitoring is only optimal when the expected benefits of the options being considered are similar and when the pay‐off for learning about their benefits is large.     

Spatially Explicit Power Analyses for Occupancy‐Based Monitoring of Wolverine in the U.S. Rocky Mountains

Conservation scientists and resource managers often have to design monitoring programs for species that are rare or patchily distributed across large landscapes. Such programs are frequently expensive and seldom can be conducted by one entity. It is essential that a prospective power analysis be undertaken to ensure stated monitoring goals are feasible. We developed a spatially based simulation program that accounts for natural history, habitat use, and sampling scheme to investigate the power of monitoring protocols to detect trends in population abundance over time with occupancy‐based methods. We analyzed monitoring schemes with different sampling efforts for wolverine (Gulo gulo) populations in 2 areas of the U.S. Rocky Mountains. The relation between occupancy and abundance was nonlinear and depended on landscape, population size, and movement parameters. With current estimates for population size and detection probability in the northern U.S. Rockies, most sampling schemes were only able to detect large declines in abundance in the simulations (i.e., 50% decline over 10 years). For small populations reestablishing in the Southern Rockies, occupancy‐based methods had enough power to detect population trends only when populations were increasing dramatically (e.g., doubling or tripling in 10 years), regardless of sampling effort. In general, increasing the number of cells sampled or the per‐visit detection probability had a much greater effect on power than the number of visits conducted during a survey. Although our results are specific to wolverines, this approach could easily be adapted to other territorial species. Poder de Análisis Espacialmente Explícito para el Monitoreo Basado en Ocupación del Glotón (Gulo gulo) en las Montañas Rocallosas de Estados Unidos     

Fishing‐gear restrictions and biomass gains for coral reef fishes in marine protected areas

Considerable empirical evidence supports recovery of reef fish populations with fishery closures. In countries where full exclusion of people from fishing may be perceived as inequitable, fishing‐gear restrictions on nonselective and destructive gears may offer socially relevant management alternatives to build recovery of fish biomass. Even so, few researchers have statistically compared the responses of tropical reef fisheries to alternative management strategies. We tested for the effects of fishery closures and fishing gear restrictions on tropical reef fish biomass at the community and family level. We conducted 1,396 underwater surveys at 617 unique sites across a spatial hierarchy within 22 global marine ecoregions that represented 5 realms. We compared total biomass across local fish assemblages and among 20 families of reef fishes inside marine protected areas (MPAs) with different fishing restrictions: no‐take, hook‐and‐line fishing only, several fishing gears allowed, and sites open to all fishing gears. We included a further category representing remote sites, where fishing pressure is low. As expected, full fishery closures, (i.e., no‐take zones) most benefited community‐ and family‐level fish biomass in comparison with restrictions on fishing gears and openly fished sites. Although biomass responses to fishery closures were highly variable across families, some fishery targets (e.g., Carcharhinidae and Lutjanidae) responded positively to multiple restrictions on fishing gears (i.e., where gears other than hook and line were not permitted). Remoteness also positively affected the response of community‐level fish biomass and many fish families. Our findings provide strong support for the role of fishing restrictions in building recovery of fish biomass and indicate important interactions among fishing‐gear types that affect biomass of a diverse set of reef fish families.     

From Adaptive Management to Adjustive Management: A Pragmatic Account of Biodiversity Values

Abstract: The conservation of biodiversity poses an exceptionally difficult problem in that it needs to be effective in a context of double uncertainty: scientific (i.e., how to conserve biodiversity) and normative (i.e., which biodiversity to conserve and why). Although adaptive management offers a promising approach to overcome scientific uncertainty, normative uncertainty is seldom tackled by conservation science. We expanded on the approach proposed by adaptive‐management theorists by devising an integrative and iterative approach to conservation that encompasses both types of uncertainty. Inspired by environmental pragmatism, we suggest that moral values at stake in biodiversity conservation are plastic and that a plurality of individual normative positions can coexist and evolve. Moral values should thus be explored through an experimental process as additional parameters to be incorporated in the traditional adaptive‐management approach. As such, moral values should also be monitored by environmental ethicists working side by side with scientists and managers on conservation projects. Acknowledging the diversity of moral values and integrating them in a process of collective deliberation will help overcome the normative uncertainty. We used Dewey's distinction between adaptation and adjustment to offer a new paradigm built around what we call adjustive management, which reflects both the uncertainty and the likely evolution of the moral values humans attribute to biodiversity. We illustrate how this paradigm relates to practical conservation decisions by exploring the case of the Sacred Ibis (Threskiornis aethiopicus), an alien species in France that is the target of an eradication plan undertaken with little regard for moral issues. We propose that a more satisfying result of efforts to control Sacred Ibis could have been reached by rerouting the traditional feedback loop of adaptive management to include a normative inquiry. This adjustive management approach now needs to be tested in real‐case conservation programs.     

Adaptive Comanagement of a Marine Protected Area Network in Fiji

Adaptive management of natural resources is an iterative process of decision making whereby management strategies are progressively changed or adjusted in response to new information. Despite an increasing focus on the need for adaptive conservation strategies, there remain few applied examples. We describe the 9‐year process of adaptive comanagement of a marine protected area network in Kubulau District, Fiji. In 2011, a review of protected area boundaries and management rules was motivated by the need to enhance management effectiveness and the desire to improve resilience to climate change. Through a series of consultations, with the Wildlife Conservation Society providing scientific input to community decision making, the network of marine protected areas was reconfigured so as to maximize resilience and compliance. Factors identified as contributing to this outcome include well‐defined resource‐access rights; community respect for a flexible system of customary governance; long‐term commitment and presence of comanagement partners; supportive policy environment for comanagement; synthesis of traditional management approaches with systematic monitoring; and district‐wide coordination, which provided a broader spatial context for adaptive‐management decision making. Co‐Manejo Adaptativo de una Red de Áreas Marinas Protegidas en Fiyi     

Setting population targets for mammals using body mass as a predictor of population persistence

Conservation planning and biodiversity assessments need quantitative targets to optimize planning options and assess the adequacy of current species protection. However, targets aiming at persistence require population‐specific data, which limit their use in favor of fixed and nonspecific targets, likely leading to unequal distribution of conservation efforts among species. We devised a method to derive equitable population targets; that is, quantitative targets of population size that ensure equal probabilities of persistence across a set of species and that can be easily inferred from species‐specific traits. In our method, we used models of population dynamics across a range of life‐history traits related to species’ body mass to estimate minimum viable population targets. We applied our method to a range of body masses of mammals, from 2 g to 3825 kg. The minimum viable population targets decreased asymptotically with increasing body mass and were on the same order of magnitude as minimum viable population estimates from species‐ and context‐specific studies. Our approach provides a compromise between pragmatic, nonspecific population targets and detailed context‐specific estimates of population viability for which only limited data are available. It enables a first estimation of species‐specific population targets based on a readily available trait and thus allows setting equitable targets for population persistence in large‐scale and multispecies conservation assessments and planning.     

Investment and the Policy Process in Conservation Monitoring

Despite decades of discussion and implementation, conservation monitoring remains a challenge. Many current solutions in the literature focus on improving the science or making more structured decisions. These insights are important but incomplete in accounting for the politics and economics of the conservation decisions informed by monitoring. Our novel depiction of the monitoring enterprise unifies insights from multiple disciplines (conservation, operations research, economics, and policy) and highlights many underappreciated factors that affect the expected benefits of monitoring. For example, there must be a strong link between the specific needs of decision makers and information gathering. Furthermore, the involvement of stakeholders other than scientists and research managers means that new information may not be interpreted and acted upon as expected. While answering calls for sharply delineated objectives will clearly add focus to monitoring efforts, for practical reasons, high‐level goals may purposefully be left vague, to facilitate other necessary steps in the policy process. We use the expanded depiction of the monitoring process to highlight problems of cooperation and conflict. We critique calls to invest in monitoring for the greater good by arguing that incentives are typically lacking. Although the benefits of learning accrued within a project (e.g., improving management) provide incentives for investing in some monitoring, it is unrealistic, in general, to expect managers to add potentially costly measures to generate shared benefits. In the traditional linear model of the role of science in policy decisions, monitoring reduces uncertainty and decision makers are rational, unbiased consumers of the science. However, conservation actions increasingly involve social conflict. Drawing insights from political science, we argue that in high‐conflict situations, it is necessary to address the conflict prior to monitoring. Las Inversiones y el Proceso de Políticas en el Monitoreo de la Conservación Sanchirico et al.     

Adaptive social impact management for conservation and environmental management

Concerns about the social consequences of conservation have spurred increased attention the monitoring and evaluation of the social impacts of conservation projects. This has resulted in a growing body of research that demonstrates how conservation can produce both positive and negative social, economic, cultural, health, and governance consequences for local communities. Yet, the results of social monitoring efforts are seldom applied to adaptively manage conservation projects. Greater attention is needed to incorporating the results of social impact assessments in long‐term conservation management to minimize negative social consequences and maximize social benefits. We bring together insights from social impact assessment, adaptive management, social learning, knowledge coproduction, cross‐scale governance, and environmental planning to propose a definition and framework for adaptive social impact management (ASIM). We define ASIM as the cyclical process of monitoring and adaptively managing social impacts over the life‐span of an initiative through the 4 stages of profiling, learning, planning, and implementing. We outline 14 steps associated with the 4 stages of the ASIM cycle and provide guidance and potential methods for social‐indicator development, predictive assessments of social impacts, monitoring and evaluation, communication of results, and identification and prioritization of management responses. Successful ASIM will be aided by engaging with best practices – including local engagement and collaboration in the process, transparent communication of results to stakeholders, collective deliberation on and choice of interventions, documentation of shared learning at the site level, and the scaling up of insights to inform higher‐level conservation policies‐to increase accountability, trust, and perceived legitimacy among stakeholders. The ASIM process is broadly applicable to conservation, environmental management, and development initiatives at various scales and in different contexts.     

Adaptive introgression as a resource for management and genetic conservation in a changing climate

Current rates of climate change require organisms to respond through migration, phenotypic plasticity, or genetic changes via adaptation. We focused on questions regarding species’ and populations’ ability to respond to climate change through adaptation. Specifically, the role adaptive introgression, movement of genetic material from the genome of 1 species into the genome of another through repeated interbreeding, may play in increasing species’ ability to respond to a changing climate. Such interspecific gene flow may mediate extinction risk or consequences of limited adaptive potential that result from standing genetic variation and mutation alone, enabling a quicker demographic recovery in response to changing environments. Despite the near dismissal of the potential benefits of hybridization by conservation practitioners, we examined a number of case studies across different taxa that suggest gene flow between sympatric or parapatric sister species or within species that exhibit strong ecotypic differentiation may represent an underutilized management option to conserve evolutionary potential in a changing environment. This will be particularly true where advanced‐generation hybrids exhibit adaptive traits outside the parental phenotypic range, a phenomenon known as transgressive segregation. The ideas presented in this essay are meant to provoke discussion regarding how we maintain evolutionary potential, the conservation value of natural hybrid zones, and consideration of their important role in adaptation to climate.     

Complementarity of No‐Take Marine Reserves and Individual Transferable Catch Quotas for Managing the Line Fishery of the Great Barrier Reef

Abstract: Changes in the management of the fin fish fishery of the Great Barrier Reef motivated us to investigate the combined effects on economic returns and fish biomass of no‐take areas and regulated total allowable catch allocated in the form of individual transferable quotas (such quotas apportion the total allowable catch as fishing rights and permits the buying and selling of these rights among fishers). We built a spatially explicit biological and economic model of the fishery to analyze the trade‐offs between maintaining given levels of fish biomass and the net financial returns from fishing under different management regimes. Results of the scenarios we modeled suggested that a decrease in total allowable catch at high levels of harvest either increased net returns or lowered them only slightly, but increased biomass by up to 10% for a wide range of reserve sizes and an increase in the reserve area from none to 16% did not greatly change net returns at any catch level. Thus, catch shares and no‐take reserves can be complementary and when these methods are used jointly they promote lower total allowable catches when harvest is relatively high and encourage larger no‐take areas when they are small.     

Use of monitoring data to support conservation management and policy decisions in Micronesia

Adaptive management implies a continuous knowledge‐based decision‐making process in conservation. Yet, the coupling of scientific monitoring and management frameworks remains rare in practice because formal and informal communication pathways are lacking. We examined 4 cases in Micronesia where conservation practitioners are using new knowledge in the form of monitoring data to advance marine conservation. These cases were drawn from projects in Micronesia Challenge jurisdictions that received funding for coupled monitoring‐to‐management frameworks and encompassed all segments of adaptive management. Monitoring in Helen Reef, Republic of Palau, was catalyzed by coral bleaching and revealed evidence of overfishing that led to increased enforcement and outreach. In Nimpal Channel, Yap, Federated States of Micronesia (FSM), monitoring the recovery of marine food resources after customary restrictions were put in place led to new, more effective enforcement approaches. Monitoring in Laolao Bay, Saipan, Commonwealth of the Northern Mariana Islands, was catalyzed by observable sediment loads from poor land‐use practices and resulted in actions that reduced land‐based threats, particularly littering and illegal burning, and revealed additional threats from overfishing. Pohnpei (FSM) began monitoring after observed declines in grouper spawning aggregations. This data led to adjusting marine conservation area boundaries and implementing market‐based size class restrictions. Two themes emerged from these cases. First, in each case monitoring was conducted in a manner relevant to the social and ecological systems and integrated into the decision‐making process. Second, conservation practitioners and scientists in these cases integrated culturally appropriate stakeholder engagement throughout all phases of the adaptive management cycle. More broadly, our study suggests, when describing adaptive management, providing more details on how monitoring and management activities are linked at similar spatial scales and across similar time frames can enhance the application of knowledge.     

Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation

Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management. Social learning, in contrast to individual‐based learning, stresses collaborative and generative insight making and is well‐suited for adaptive management. Adaptive‐management integrates feedback loops that are informed by what is learned and is guided by iterative decision making. Participants engaged in citizen science are able to add to what they are learning through primary data collection, which can result in the real‐time information that is often necessary for conservation. Our work is particularly timely because research publications consistently report a lack of established frameworks and evaluation plans to address the extent of conservation outcomes in citizen science. To illustrate how our framework supports conservation through citizen science, we examined how 2 programs enacted our collaborative science framework. Further, we inspected preliminary conservation outcomes of our case‐study programs. These programs, despite their recent implementation, are demonstrating promise with regard to positive conservation outcomes. To date, they are independently earning funds to support research, earning buy‐in from local partners to engage in experimentation, and, in the absence of leading scientists, are collecting data to test ideas. We argue that this success is due to citizen scientists being organized around local issues and engaging in iterative, collaborative, and adaptive learning.     

Incorporating geodiversity into conservation decisions

In a rapidly changing climate, conservation practitioners could better use geodiversity in a broad range of conservation decisions. We explored selected avenues through which this integration might improve decision making and organized them within the adaptive management cycle of assessment, planning, implementation, and monitoring. Geodiversity is seldom referenced in predominant environmental law and policy. With most natural resource agencies mandated to conserve certain categories of species, agency personnel are challenged to find ways to practically implement new directives aimed at coping with climate change while retaining their species‐centered mandate. Ecoregions and ecological classifications provide clear mechanisms to consider geodiversity in plans or decisions, the inclusion of which will help foster the resilience of conservation to climate change. Methods for biodiversity assessment, such as gap analysis, climate change vulnerability analysis, and ecological process modeling, can readily accommodate inclusion of a geophysical component. We adapted others’ approaches for characterizing landscapes along a continuum of climate change vulnerability for the biota they support from resistant, to resilient, to susceptible, and to sensitive and then summarized options for integrating geodiversity into planning in each landscape type. In landscapes that are relatively resistant to climate change, options exist to fully represent geodiversity while ensuring that dynamic ecological processes can change over time. In more susceptible landscapes, strategies aiming to maintain or restore ecosystem resilience and connectivity are paramount. Implementing actions on the ground requires understanding of geophysical constraints on species and an increasingly nimble approach to establishing management and restoration goals. Because decisions that are implemented today will be revisited and amended into the future, increasingly sophisticated forms of monitoring and adaptation will be required to ensure that conservation efforts fully consider the value of geodiversity for supporting biodiversity in the face of a changing climate.     

Using soundscapes to detect variable degrees of human influence on tropical forests in Papua New Guinea

There is global concern about tropical forest degradation, in part, because of the associated loss of biodiversity. Communities and indigenous people play a fundamental role in tropical forest management and are often efficient at preventing forest degradation. However, monitoring changes in biodiversity due to degradation, especially at a scale appropriate to local tropical forest management, is plagued by difficulties, including the need for expert training, inconsistencies across observers, and lack of baseline or reference data. We used a new biodiversity remote‐sensing technology, the recording of soundscapes, to test whether the acoustic saturation of a tropical forest in Papua New Guinea decreases as land‐use intensity by the communities that manage the forest increases. We sampled soundscapes continuously for 24 hours at 34 sites in different land‐use zones of 3 communities. Land‐use zones where forest cover was fully retained had significantly higher soundscape saturation during peak acoustic activity times (i.e., dawn and dusk chorus) compared with land‐use types with fragmented forest cover. We conclude that, in Papua New Guinea, the relatively simple measure of soundscape saturation may provide a cheap, objective, reproducible, and effective tool for monitoring tropical forest deviation from an intact state, particularly if it is used to detect the presence of intact dawn and dusk choruses.     

The role of digital data entry in participatory environmental monitoring

Many argue that monitoring conducted exclusively by scientists is insufficient to address ongoing environmental challenges. One solution entails the use of mobile digital devices in participatory monitoring (PM) programs. But how digital data entry affects programs with varying levels of stakeholder participation, from nonscientists collecting field data to nonscientists administering every step of a monitoring program, remains unclear. We reviewed the successes, in terms of management interventions and sustainability, of 107 monitoring programs described in the literature (hereafter programs) and compared these with case studies from our PM experiences in Australia, Canada, Ethiopia, Ghana, Greenland, and Vietnam (hereafter cases). Our literature review showed that participatory programs were less likely to use digital devices, and 2 of our 3 more participatory cases were also slow to adopt digital data entry. Programs that were participatory and used digital devices were more likely to report management actions, which was consistent with cases in Ethiopia, Greenland, and Australia. Programs engaging volunteers were more frequently reported as ongoing, but those involving digital data entry were less often sustained when data collectors were volunteers. For the Vietnamese and Canadian cases, sustainability was undermined by a mismatch in stakeholder objectives. In the Ghanaian case, complex field protocols diminished monitoring sustainability. Innovative technologies attract interest, but the foundation of effective participatory adaptive monitoring depends more on collaboratively defined questions, objectives, conceptual models, and monitoring approaches. When this foundation is built through effective partnerships, digital data entry can enable the collection of more data of higher quality. Without this foundation, or when implemented ineffectively or unnecessarily, digital data entry can be an additional expense that distracts from core monitoring objectives and undermines project sustainability. The appropriate role of digital data entry in PM likely depends more on the context in which it is used and less on the technology itself.     

Incorporating Uncertainty of Management Costs in Sensitivity Analyses of Matrix Population Models

The importance of accounting for economic costs when making environmental‐management decisions subject to resource constraints has been increasingly recognized in recent years. In contrast, uncertainty associated with such costs has often been ignored. We developed a method, on the basis of economic theory, that accounts for the uncertainty in population‐management decisions. We considered the case where, rather than taking fixed values, model parameters are random variables that represent the situation when parameters are not precisely known. Hence, the outcome is not precisely known either. Instead of maximizing the expected outcome, we maximized the probability of obtaining an outcome above a threshold of acceptability. We derived explicit analytical expressions for the optimal allocation and its associated probability, as a function of the threshold of acceptability, where the model parameters were distributed according to normal and uniform distributions. To illustrate our approach we revisited a previous study that incorporated cost‐efficiency analyses in management decisions that were based on perturbation analyses of matrix population models. Incorporating derivations from this study into our framework, we extended the model to address potential uncertainties. We then applied these results to 2 case studies: management of a Koala (Phascolarctos cinereus) population and conservation of an olive ridley sea turtle (Lepidochelys olivacea) population. For low aspirations, that is, when the threshold of acceptability is relatively low, the optimal strategy was obtained by diversifying the allocation of funds. Conversely, for high aspirations, the budget was directed toward management actions with the highest potential effect on the population. The exact optimal allocation was sensitive to the choice of uncertainty model. Our results highlight the importance of accounting for uncertainty when making decisions and suggest that more effort should be placed on understanding the distributional characteristics of such uncertainty. Our approach provides a tool to improve decision making.     

Need for multiscale planning for conservation of urban bats

For over a century there have been continual efforts to incorporate nature into urban planning. These efforts (i.e., urban reconciliation) aim to manage and create habitats that support biodiversity within cities. Given that species select habitat at different spatial scales, understanding the scale at which urban species respond to their environment is critical to the success of urban reconciliation efforts. We assessed species–habitat relationships for common bat species at 50‐m, 500‐m, and 1 km spatial scales in the Chicago (U.S.A.) metropolitan area and predicted bat activity across the greater Chicago region. Habitat characteristics across all measured scales were important predictors of silver‐haired bat (Lasionycteris noctivagans) and eastern red bat (Lasiurus borealis) activity, and big brown bat (Eptesicus fuscus) activity was significantly lower at urban sites relative to rural sites. Open vegetation had a negative effect on silver‐haired bat activity at the 50‐m scale but a positive effect at the 500‐m scale, indicating potential shifts in the relative importance of some habitat characteristics at different scales. These results demonstrate that localized effects may be constrained by broader spatial patterns. Our findings highlight the importance of considering scale in urban reconciliation efforts and our landscape predictions provide information that can help prioritize urban conservation work.     

Inferring the nature of anthropogenic threats from long‐term abundance records

Diagnosing the processes that threaten species persistence is critical for recovery planning and risk forecasting. Dominant threats are typically inferred by experts on the basis of a patchwork of informal methods. Transparent, quantitative diagnostic tools would contribute much‐needed consistency, objectivity, and rigor to the process of diagnosing anthropogenic threats. Long‐term census records, available for an increasingly large and diverse set of taxa, may exhibit characteristic signatures of specific threatening processes and thereby provide information for threat diagnosis. We developed a flexible Bayesian framework for diagnosing threats on the basis of long‐term census records and diverse ancillary sources of information. We tested this framework with simulated data from artificial populations subjected to varying degrees of exploitation and habitat loss and several real‐world abundance time series for which threatening processes are relatively well understood: bluefin tuna (Thunnus maccoyii) and Atlantic cod (Gadus morhua) (exploitation) and Red Grouse (Lagopus lagopus scotica) and Eurasian Skylark (Alauda arvensis) (habitat loss). Our method correctly identified the process driving population decline for over 90% of time series simulated under moderate to severe threat scenarios. Successful identification of threats approached 100% for severe exploitation and habitat loss scenarios. Our method identified threats less successfully when threatening processes were weak and when populations were simultaneously affected by multiple threats. Our method selected the presumed true threat model for all real‐world case studies, although results were somewhat ambiguous in the case of the Eurasian Skylark. In the latter case, incorporation of an ancillary source of information (records of land‐use change) increased the weight assigned to the presumed true model from 70% to 92%, illustrating the value of the proposed framework in bringing diverse sources of information into a common rigorous framework. Ultimately, our framework may greatly assist conservation organizations in documenting threatening processes and planning species recovery. Inferencia la Naturaleza de las Amenazas Antropogénicas para los Registros de Abundancia a Largo Plazo     

Actual and Potential Use of Population Viability Analyses in Recovery of Plant Species Listed under the U.S. Endangered Species Act

Use of population viability analyses (PVAs) in endangered species recovery planning has been met with both support and criticism. Previous reviews promote use of PVA for setting scientifically based, measurable, and objective recovery criteria and recommend improvements to increase the framework's utility. However, others have questioned the value of PVA models for setting recovery criteria and assert that PVAs are more appropriate for understanding relative trade‐offs between alternative management actions. We reviewed 258 final recovery plans for 642 plants listed under the U.S. Endangered Species Act to determine the number of plans that used or recommended PVA in recovery planning. We also reviewed 223 publications that describe plant PVAs to assess how these models were designed and whether those designs reflected previous recommendations for improvement of PVAs. Twenty‐four percent of listed species had recovery plans that used or recommended PVA. In publications, the typical model was a matrix population model parameterized with ≤5 years of demographic data that did not consider stochasticity, genetics, density dependence, seed banks, vegetative reproduction, dormancy, threats, or management strategies. Population growth rates for different populations of the same species or for the same population at different points in time were often statistically different or varied by >10%. Therefore, PVAs parameterized with underlying vital rates that vary to this degree may not accurately predict recovery objectives across a species’ entire distribution or over longer time scales. We assert that PVA, although an important tool as part of an adaptive‐management program, can help to determine quantitative recovery criteria only if more long‐term data sets that capture spatiotemporal variability in vital rates become available. Lacking this, there is a strong need for viable and comprehensive methods for determining quantitative, science‐based recovery criteria for endangered species with minimal data availability. Uso Actual y Potencial del Análisis de Viabilidad Poblacional para la Recuperación de Especies de Plantas Enlistadas en el Acta de Especies En Peligro de E.U.A