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Seedling establishment is a critical step that may ultimately govern tree species' distribution shifts under environmental change. Annual variation in the location of seed rain and microclimates results in transient "windows of opportunity" for tree seedling establishment across the landscape. These establishment windows vary at fine spatiotemporal scales that are not considered in most assessments of climate change impacts on tree species range dynamics and habitat displacement. We integrate field seedling establishment trials conducted in the southern Sierra Nevada and western Tehachapi Mountains of southern California with spatially downscaled grids of modeled water-year climatic water deficit (CWDwy) and mean August maximum daily temperature (Tmax) to map historical and projected future microclimates suitable for establishment windows of opportunity for Quercus douglasii, a dominant tree species of warm, dry foothill woodlands, and Q. kelloggii, a dominant of cooler, more mesic montane woodlands and forests. Based on quasi-binomial regression models, Q. douglasii seedling establishment is significantly associated with modeled CWDwy and to a lesser degree with modeled Tmax. Q. kelloggii seedling establishment is most strongly associated with Tmax and best predicted by a two-factor model including CWDwy and Tmax. Establishment niche models are applied to explore recruitment window dynamics in the western Tehachapi Mountains, where these species are currently widespread canopy dominants. Establishment windows are projected to decrease by 50-95%, shrinking locally to higher elevations and north-facing slopes by the end of this century depending on the species and climate scenario. These decreases in establishment windows suggest the potential for longer-term regional population declines of the species. While many additional processes regulate seedling establishment and growth, this study highlights the need to account for topoclimatic controls and interannual climatic variation when assessing how seedling establishment and colonization processes could be affected by climate change.

An increasing amount of California's landscape has burned in wildfires in recent decades, in conjunction with increasing temperatures and vapor pressure deficit due to climate change. As the wildland-urban interface expands, more people are exposed to and harmed by these extensive wildfires, which are also eroding the resilience of terrestrial ecosystems. With future wildfire activity expected to increase, there is an urgent demand for solutions that sustain healthy ecosystems and wildfire-resilient human communities. Those who manage disaster response, landscapes, and biodiversity rely on mapped projections of how fire activity may respond to climate change and other human factors. California wildfire is complex, however, and climate-fire relationships vary across the state. Given known geographical variability in drivers of fire activity, we asked whether the geographical extent of fire models used to create these projections may alter the interpretation of predictions. We compared models of fire occurrence spanning the entire state of California to models developed for individual ecoregions and then projected end-of-century future fire patterns under climate change scenarios. We trained a Maximum Entropy model with fire records and hydroclimatological variables from recent decades (1981 to 2010) as well as topographic and human infrastructure predictors. Results showed substantial variation in predictors of fire probability and mapped future projections of fire depending upon geographical extents of model boundaries. Only the ecoregion models, accounting for the unique patterns of vegetation, climate, and human infrastructure, projected an increase in fire in most forested regions of the state, congruent with predictions from other studies., Competing Interests: Competing interests statement:The authors declare no competing interest.

Natural ecosystems are fundamental to local water cycles and the water ecosystem services that humans enjoy, such as water provision, outdoor recreation, and flood protection. However, integrating ecosystem services into water resources management requires that they be acknowledged, quantified, and communicated to decision-makers. We present an indicator framework that incorporates the supply of, and demand for, water ecosystem services. This provides an initial diagnostic for water resource managers and a mechanism for evaluating tradeoffs through future scenarios. Building on a risk assessment framework, we present a three-tiered indicator for measuring where demand exceeds the supply of services, addressing the scope (spatial extent), frequency, and amplitude for which objectives (service delivery) are not met. The Ecosystem Service Indicator is measured on a 0-100 scale, which encompasses none to total service delivery. We demonstrate the framework and its applicability to a variety of services and data sources (e.g., monitoring stations, statistical yearbooks, modeled datasets) from case studies in China and Southeast Asia. We evaluate the sensitivity of the indicator scores to varying levels data and three methods of calculation using a simulated test dataset. Our indicator framework is conceptually simple, robust, and flexible enough to offer a starting point for decision-makers and to accommodate the evolution and expansion of tools, models and data sources used to measure and evaluate the value of water ecosystem services., (© 2021. The Author(s).)

The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories., (© 2018 Society for Conservation Biology.)

Degradation of freshwater ecosystems and the services they provide is a primary cause of increasing water insecurity, raising the need for integrated solutions to freshwater management. While methods for characterizing the multi-faceted challenges of managing freshwater ecosystems abound, they tend to emphasize either social or ecological dimensions and fall short of being truly integrative. This paper suggests that management for sustainability of freshwater systems needs to consider the linkages between human water uses, freshwater ecosystems and governance. We present a conceptualization of freshwater resources as part of an integrated social-ecological system and propose a set of corresponding indicators to monitor freshwater ecosystem health and to highlight priorities for management. We demonstrate an application of this new framework -the Freshwater Health Index (FHI) - in the Dongjiang River Basin in southern China, where stakeholders are addressing multiple and conflicting freshwater demands. By combining empirical and modeled datasets with surveys to gauge stakeholders' preferences and elicit expert information about governance mechanisms, the FHI helps stakeholders understand the status of freshwater ecosystems in their basin, how ecosystems are being manipulated to enhance or decrease water-related services, and how well the existing water resource management regime is equipped to govern these dynamics over time. This framework helps to operationalize a truly integrated approach to water resource management by recognizing the interplay between governance, stakeholders, freshwater ecosystems and the services they provide., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

Population viability analysis (PVA) is a reliable tool for ranking management options for a range of species despite parameter uncertainty. No one has yet investigated whether this holds true for model uncertainty for species with complex life histories and for responses to multiple threats. We tested whether a range of model structures yielded similar rankings of management and threat scenarios for 2 plant species with complex postfire responses. We examined 2 contrasting species from different plant functional types: an obligate seeding shrub and a facultative resprouting shrub. We exposed each to altered fire regimes and an additional, species-specific threat. Long-term demographic data sets were used to construct an individual-based model (IBM), a complex stage-based model, and a simple matrix model that subsumes all life stages into 2 or 3 stages. Agreement across models was good under some scenarios and poor under others. Results from the simple and complex matrix models were more similar to each other than to the IBM. Results were robust across models when dominant threats are considered but were less so for smaller effects. Robustness also broke down as the scenarios deviated from baseline conditions, likely the result of a number of factors related to the complexity of the species' life history and how it was represented in a model. Although PVA can be an invaluable tool for integrating data and understanding species' responses to threats and management strategies, this is best achieved in the context of decision support for adaptive management alongside multiple lines of evidence and expert critique of model construction and output., (© 2016 Society for Conservation Biology.)

Quantitative indicators are a common means of assessing the complex dimensions of a sustainable freshwater system, and framing scientific knowledge for policy and decision makers. There is an abundance of indicators in use, but considerable variation in terms of what is being measured and how indicators are applied, making it difficult for end-users to identify suitable assessment methods. We review 95 water-related indices and analyze them along their normative, procedural, and systemic dimensions to better understand how problems are being defined, highlight overlaps and differences, and identify the context(s) in which a particular index is useful. We also analyze the intended use, end-users, and geographic scale of application for each index. We find that risk assessment is the most common application (n = 25), with indices in this group typically focusing either on hazard identification (biophysical assessments) or vulnerability of human populations. Indices that measure freshwater ecological health are not explicitly linking these indicators to ecosystem services, and in fact the concept of ecosystem services is rarely (n = 3) used for indicator selection. Resource managers are the most common group of intended end-users (n = 25), but while 28 indices involved consultation with potential end-users, 11 did not specify an intended use. We conclude that indices can be applied as solution-oriented tools, evaluating scenarios and identifying tradeoffs among services and beneficiaries, rather than only assessing and monitoring existing conditions. Finally, earlier engagement of end-users is recommended to help researchers find the right balance among indices' salience, legitimacy, and credibility and thus improve their decision relevance.

Anthropogenic drivers of global change include rising atmospheric concentrations of carbon dioxide and other greenhouse gasses and resulting changes in the climate, as well as nitrogen deposition, biotic invasions, altered disturbance regimes, and land-use change. Predicting the effects of global change on terrestrial plant communities is crucial because of the ecosystem services vegetation provides, from climate regulation to forest products. In this paper, we present a framework for detecting vegetation changes and attributing them to global change drivers that incorporates multiple lines of evidence from spatially extensive monitoring networks, distributed experiments, remotely sensed data, and historical records. Based on a literature review, we summarize observed changes and then describe modeling tools that can forecast the impacts of multiple drivers on plant communities in an era of rapid change. Observed responses to changes in temperature, water, nutrients, land use, and disturbance show strong sensitivity of ecosystem productivity and plant population dynamics to water balance and long-lasting effects of disturbance on plant community dynamics. Persistent effects of land-use change and human-altered fire regimes on vegetation can overshadow or interact with climate change impacts. Models forecasting plant community responses to global change incorporate shifting ecological niches, population dynamics, species interactions, spatially explicit disturbance, ecosystem processes, and plant functional responses. Monitoring, experiments, and models evaluating multiple change drivers are needed to detect and predict vegetation changes in response to 21st century global change.

The identification of species at risk of extinction is a central goal of conservation. As the use of data compiled for IUCN Red List assessments expands, a number of misconceptions regarding the purpose, application and use of the IUCN Red List categories and criteria have arisen. We outline five such classes of misconception; the most consequential drive proposals for adapted versions of the criteria, rendering assessments among species incomparable. A key challenge for the future will be to recognize the point where understanding has developed so markedly that it is time for the next generation of the Red List criteria. We do not believe we are there yet but, recognizing the need for scrutiny and continued development of Red Listing, conclude by suggesting areas where additional research could be valuable in improving the understanding of extinction risk among species., (© 2016 The Author(s).)

Estimating and projecting population trends using population viability analysis (PVA) are central to identifying species at risk of extinction and for informing conservation management strategies. Models for PVA generally fall within two categories, scalar (count-based) or matrix (demographic). Model structure, process error, measurement error, and time series length all have known impacts in population risk assessments, but their combined impact has not been thoroughly investigated. We tested the ability of scalar and matrix PVA models to predict percent decline over a ten-year interval, selected to coincide with the IUCN Red List criterion A.3, using data simulated for a hypothetical, short-lived organism with a simple life-history and for a threatened snail, Tasmaphena lamproides. PVA performance was assessed across different time series lengths, population growth rates, and levels of process and measurement error. We found that the magnitude of effects of measurement error, process error, and time series length, and interactions between these, depended on context. We found that high process and measurement error reduced the reliability of both models in predicted percent decline. Both sources of error contributed strongly to biased predictions, with process error tending to contribute to the spread of predictions more than measurement error. Increasing time series length improved precision and reduced bias of predicted population trends, but gains substantially diminished for time series lengths greater than 10-15 years. The simple parameterization scheme we employed contributed strongly to bias in matrix model predictions when both process and measurement error were high, causing scalar models to exhibit similar or greater precision and lower bias than matrix models. Our study provides evidence that, for short-lived species with structured but simple life histories, short time series and simple models can be sufficient for reasonably reliable conservation decision-making, and may be preferable for population projections when unbiased estimates of vital rates cannot be obtained.

Most species face multiple anthropogenic disruptions. Few studies have quantified the cumulative influence of multiple threats on species of conservation concern, and far fewer have quantified the potential relative value of multiple conservation interventions in light of these threats. We linked spatial distribution and population viability models to explore conservation interventions under projected climate change, urbanization, and changes in fire regime on a long-lived obligate seeding plant species sensitive to high fire frequencies, a dominant plant functional type in many fire-prone ecosystems, including the biodiversity hotspots of Mediterranean-type ecosystems. First, we investigated the relative risk of population decline for plant populations in landscapes with and without land protection under an existing habitat conservation plan. Second, we modeled the effectiveness of relocating both seedlings and seeds from a large patch with predicted declines in habitat area to 2 unoccupied recipient patches with increasing habitat area under 2 projected climate change scenarios. Finally, we modeled 8 fire return intervals (FRIs) approximating the outcomes of different management strategies that effectively control fire frequency. Invariably, long-lived obligate seeding populations remained viable only when FRIs were maintained at or above a minimum level. Land conservation and seedling relocation efforts lessened the impact of climate change and land-use change on obligate seeding populations to differing degrees depending on the climate change scenario, but neither of these efforts was as generally effective as frequent translocation of seeds. While none of the modeled strategies fully compensated for the effects of land-use and climate change, an integrative approach managing multiple threats may diminish population declines for species in complex landscapes. Conservation plans designed to mitigate the impacts of a single threat are likely to fail if additional threats are ignored., (© 2014 Society for Conservation Biology.)

Voting systems aggregate preferences efficiently and are often used for deciding conservation priorities. Desirable characteristics of voting systems include transitivity, completeness, and Pareto optimality, among others. Voting systems that are common and potentially useful for environmental decision making include simple majority, approval, and preferential voting. Unfortunately, no voting system can guarantee an outcome, while also satisfying a range of very reasonable performance criteria. Furthermore, voting methods may be manipulated by decision makers and strategic voters if they have knowledge of the voting patterns and alliances of others in the voting populations. The difficult properties of voting systems arise in routine decision making when there are multiple criteria and management alternatives. Because each method has flaws, we do not endorse one method. Instead, we urge organizers to be transparent about the properties of proposed voting systems and to offer participants the opportunity to approve the voting system as part of the ground rules for operation of a group., (© 2014 The Authors. Conservation Biology published by Wiley Periodicals, Inc., on behalf of the Society for Conservation Biology.)

A species' response to climate change depends on the interaction of biotic and abiotic factors that define future habitat suitability and species' ability to migrate or adapt. The interactive effects of processes such as fire, dispersal, and predation have not been thoroughly addressed in the climate change literature. Our objective was to examine how life history traits, short-term global change perturbations, and long-term climate change interact to affect the likely persistence of an oak species--Quercus engelmannii (Engelmann oak). Specifically, we combined dynamic species distribution models, which predict suitable habitat, with stochastic, stage-based metapopulation models, which project population trajectories, to evaluate the effects of three global change factors--climate change, land use change, and altered fire frequency--emphasizing the roles of dispersal and seed predation. Our model predicted dramatic reduction in Q. engelmannii abundance, especially under drier climates and increased fire frequency. When masting lowers seed predation rates, decreased masting frequency leads to large abundance decreases. Current rates of dispersal are not likely to prevent these effects, although increased dispersal could mitigate population declines. The results suggest that habitat suitability predictions by themselves may under-estimate the impact of climate change for other species and locations.

As the number and intensity of threats to biodiversity increase, there is a critical need to investigate interactions between threats and manage populations accordingly. We ask whether it is possible to reduce the effects of one threat by mitigating another. We used long-term data for the long-lived resprouter, Xanthorrhoea resinosa Pers., to parameterise an individual-based population model. This plant is currently threatened by adverse fire regimes and the pathogen Phytophthora cinnamomi. We tested a range of fire and disease scenarios over various time horizons relevant to the population dynamics of the species and the practicalities of management. While fire does not kill the disease, it does trigger plant demographic responses that may promote population persistence when disease is present. Population decline is reduced with frequent fires because they promote the greatest number of germination events, but frequent fires reduce adult stages, which is detrimental in the long term. Fire suppression is the best action for the non-seedling stages but does not promote recruitment. With disease, frequent fire produced the highest total population sizes for shorter durations, but for longer durations fire suppression gave the highest population sizes. When seedlings were excluded, fire suppression was the best action. We conclude that fire management can play an important role in mitigating threats posed by this disease. The best approach to reducing declines may be to manage populations across a spatial mosaic in which the sequence of frequent fires and suppression are staggered across patches depending on the level of disease at the site.

Reintroduction of captive-reared animals has become increasingly popular in recent decades as a conservation technique, but little is known of how demographic factors affect the success of reintroductions. We believe whether the increase in population persistence associated with reintroduction is sufficient to warrant the cost of rearing and relocating individuals should be considered as well. We examined the trade-off between population persistence and financial cost of a reintroduction program for Crested Coots (Fulica cristata). This species was nearly extirpated from southern Europe due to unsustainable levels of hunting and reduction in amount and quality of habitat. We used a stochastic, stage-based, single-sex, metapopulation model with site-specific parameters to examine the demographic effects of releasing juveniles or adults in each population for a range of durations. We parameterized the model with data from an unsuccessful reintroduction program in which juvenile captive-bred Crested Coots were released between 2000 and 2009. Using economic data from the captive-breeding program, we also determined whether the strategy that maximized abundance coincided with the least expensive strategy. Releasing adults resulted in slightly larger final abundance than the release of nonreproductive juveniles. Both strategies were equally poor in achieving a viable metapopulation, but releasing adults was 2-4 times more expensive than releasing juveniles. To obtain a metapopulation that would be viable for 30 years, fecundity in the wild would need to increase to the values observed in captivity and juvenile survival would need to increase to almost unity. We suggest that the most likely way to increase these vital rates is by increasing habitat quality at release sites., (© 2011 Society for Conservation Biology.)

The conversion of natural habitat to urban settlements is a primary driver of biodiversity loss, and species' persistence is threatened by the extent, location, and spatial pattern of development. Urban growth models are widely used to anticipate future development and to inform conservation management, but the source of spatial input to these models may contribute to uncertainty in their predictions. We compared two sources of historic urban maps, used as input for model calibration, to determine how differences in definition and scale of urban extent affect the resulting spatial predictions from a widely used urban growth model for San Diego County, CA under three conservation scenarios. The results showed that rate, extent, and spatial pattern of predicted urban development, and associated habitat loss, may vary substantially depending on the source of input data, regardless of how much land is excluded from development. Although the datasets we compared both represented urban land, different types of land use/land cover included in the definition of urban land and different minimum mapping units contributed to the discrepancies. Varying temporal resolution of the input datasets also contributed to differences in projected rates of development. Differential predicted impacts to vegetation types illustrate how the choice of spatial input data may lead to different conclusions relative to conservation. Although the study cannot reveal whether one dataset is better than another, modelers should carefully consider that geographical reality can be represented differently, and should carefully choose the definition and scale of their data to fit their research objectives., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Premise of the Study: Despite numerous recommendations for various aspects of the design and monitoring of habitat conservation plans, there remains a need to synthesize existing guidelines into a comprehensive scheme and apply it to real-world conservation programs., Methods: We review tools for systematic conservation planning and elements for designing and implementing ecological monitoring in an adaptive management context. We apply principles of monitoring design to the San Diego Multiple Species Conservation Program (MSCP) in California, USA--one of the first multispecies habitat conservation plans, located in a landscape where high biodiversity and urban development converge., Key Results: Tools for spatial conservation planning are aimed to conserve biodiversity, often in the context of a limited budget. In practice, these methods may not accommodate legislative mandates, budgetary uncertainties, and the range of implementation mechanisms available across consortia of stakeholders. Once a reserve is implemented, the question becomes whether it is effective at conserving biodiversity, and if not, what actions are required to make it effective. In monitoring plan development, status and threats should be used to prioritize species and communities that require management action to ensure their persistence. Conceptual models documenting the state of knowledge of the system should highlight the main drivers affecting status and trends of species or communities. Monitoring strategies require scientifically justified decisions based on sampling, response, and data design., Conclusions: Because the framework illustrated here tackles multiple species, communities, and threats at the urban-wildland interface, it will have utility for ecosystem managers struggling to design monitoring programs.

Habitat loss is widely considered the greatest threat to biodiversity. However, habitat loss brings with it myriad other threats that exacerbate impacts to biodiversity. For instance, altered fire regime is associated with habitat loss and fragmentation with unknown consequences to biodiversity. Plant functional groups that rely on fire to complete their life cycle may be adversely affected by disruptions to the natural fire regime, particularly when coupled with population declines due to habitat loss. We used a spatially explicit stochastic population model linked with fire hazard functions to investigate the cumulative effects of habitat loss, fragmentation, and altered fire regime on the expected minimum abundance of a long-lived obligate-seeding shrub, Ceanothus greggii var. perplexans. This species is endemic to the California Floristic Province, a biodiversity hotspot, and is representative of a functional group of plants found in many fire-prone ecosystems. We tested the impact of a range of different fire frequencies under three different combinations of fuel accumulation and weather. The best average fire return interval for population abundance was consistently in the range of 30-50 years. However, observed average fire return intervals in highly fragmented areas can be approximately 20 years or less, and model results show this to be detrimental to C. greggii populations. Results also show that if fires are uncorrelated across habitat fragments then the impact of altered fire regime on populations is worse than the impact of habitat fragmentation because of spatial and temporal decoupling of fire events across the landscape. However, the negative impacts of altered fire regime are outweighed by habitat loss as fragmentation increases. Our results show that large unplanned fires, operating under an altered fire regime, are ultimately detrimental to perennial obligate-seeding shrubs in fragmented landscapes.

Species responses to climate change may be influenced by changes in available habitat, as well as population processes, species interactions and interactions between demographic and landscape dynamics. Current methods for assessing these responses fail to provide an integrated view of these influences because they deal with habitat change or population dynamics, but rarely both. In this study, we linked a time series of habitat suitability models with spatially explicit stochastic population models to explore factors that influence the viability of plant species populations under stable and changing climate scenarios in South African fynbos, a global biodiversity hot spot. Results indicate that complex interactions between life history, disturbance regime and distribution pattern mediate species extinction risks under climate change. Our novel mechanistic approach allows more complete and direct appraisal of future biotic responses than do static bioclimatic habitat modelling approaches, and will ultimately support development of more effective conservation strategies to mitigate biodiversity losses due to climate change.

In nature reserves and habitat conservation areas, monitoring is required to determine if reserves are meeting their goals for preserving species, ecological communities, and ecosystems. Increasingly, reserves are established to protect multiple species and communities, each with their own conservation goals and objectives. As resources are always inadequate to monitor all components, criteria must be applied to prioritize both species and communities for monitoring and management. While methods for prioritizing species based on endangerment or risk have been established, approaches to prioritizing ecological communities for monitoring are not well developed, despite a long-standing emphasis on communities as target elements in reserve design. We established guidelines based on four criteria derived from basic principles of conservation and landscape ecology--extent, representativeness, fragmentation, and endangerment--to prioritize communities in the San Diego Multiple Species Conservation Plan (MSCP). The MSCP was one of the first multiple-species habitat conservation areas established in California, USA, and it has a complex spatial configuration because of the patterns of surrounding land use, which are largely urbanized. In this case study, high priority communities for monitoring include coastal sage scrub (high endangerment, underrepresented within the reserve relative to the region, and moderately fragmented), freshwater wetlands, and coastal habitats (both have high fragmentation, moderate endangerment and representativeness, and low areal extent). This framework may be useful to other conservation planners and land managers for prioritizing the most significant and at-risk communities for monitoring.

Successful conservation plans are not solely achieved by acquiring optimally designed reserves. Ongoing monitoring and management of the biodiversity in those reserves is an equally important, but often neglected or poorly executed, part of the conservation process. In this paper we address one of the first and most important steps in designing a monitoring program - deciding what to monitor. We present a strategy for prioritizing species for monitoring and management in multispecies conservation plans. We use existing assessments of threatened status, and the degree and spatial and temporal extent of known threats to link the prioritization of species to the overarching goals and objectives of the conservation plan. We consider both broad and localized spatial scales to capture the regional conservation context and the practicalities of local management and monitoring constraints. Spatial scales that are commensurate with available data are selected. We demonstrate the utility of this strategy through application to a set of 85 plants and animals in an established multispecies conservation plan in San Diego County, California, USA. We use the prioritization to identify the most prominent risk factors and the habitats associated with the most threats to species. The protocol highlighted priorities that had not previously been identified and were not necessarily intuitive without systematic application of the criteria; many high-priority species have received no monitoring attention to date, and lower-priority species have. We recommend that in the absence of clear focal species, monitoring threats in highly impacted habitats may be a way to circumvent the need to monitor all the targeted species., (© 2007 The Authors.)

Habitat remnants in urbanized areas typically conserve biodiversity and serve the recreation and urban open-space needs of human populations. Nevertheless, these goals can be in conflict if human activity negatively affects wildlife. Hence, when considering habitat remnants as conservation refuges it is crucial to understand how human activities and land uses affect wildlife use of those and adjacent areas. We used tracking data (animal tracks and den or bed sites) on 10 animal species and information on human activity and environmental factors associated with anthropogenic disturbance in 12 habitat fragments across San Diego County, California, to examine the relationships among habitat fragment characteristics, human activity, and wildlife presence. There were no significant correlations of species presence and abundance with percent plant cover for all species or with different land-use intensities for all species, except the opossum (Didelphis virginiana), which preferred areas with intensive development. Woodrats (Neotoma spp.) and cougars (Puma concolor) were associated significantly and positively and significantly and negatively, respectively, with the presence and prominence of utilities. Woodrats were also negatively associated with the presence of horses. Raccoons (Procyon lotor) and coyotes (Canis latrans) were associated significantly and negatively and significantly and positively, respectively, with plant bulk and permanence. Cougars and gray foxes (Urocyon cinereoargenteus) were negatively associated with the presence of roads. Roadrunners (Geococcyx californianus) were positively associated with litter. The only species that had no significant correlations with any of the environmental variables were black-tailed jackrabbits (Lepus californicus) and mule deer (Odocoileus hemionus). Bobcat tracks were observed more often than gray foxes in the study area and bobcats correlated significantly only with water availability, contrasting with results from other studies. Our results appear to indicate that maintenance of habitat fragments in urban areas is of conservation benefit to some animal species, despite human activity and disturbance, as long as the fragments are large.

Environmental management decisions typically lie at the interface of science and public policy. Consequently, these decisions involve a number of stakeholders with competing agendas and vested interests in the ultimate decision. In such cases, it is appropriate to adopt formal methods for consensus building to ensure transparent and repeatable decisions. In this paper, we use an environmental management case study to demonstrate the utility of a mathematical consensus convergence model in aggregating values (or weights) across groups. Consensus models are applicable when all parties agree to negotiate in order to resolve conflict. The advantage of this method is that it does not require that all members of the group reach agreement, often an impossible task in group decision making. Instead, it uses philosophical foundations in consensus building to aggregate group members' values in a way that guarantees convergence towards a single consensual value that summarizes the group position. We highlight current problems with ad hoc consensus and negotiation methods, provide justification for the adoption of formal consensus convergence models and compare the consensus convergence model with currently used methods for aggregating values across a group in a decision making context. The model provides a simple and transparent decision support tool for group decision making that is straightforward to implement.

Ecosystems and the species and communities within them are highly complex systems that defy predictions with any degree of certainty. Managing and conserving these systems in the face of uncertainty remains a daunting challenge, particularly with respect to developing networks of marine reserves. Here we review several modelling frameworks that explicitly acknowledge and incorporate uncertainty, and then use these methods to evaluate reserve spacing rules given increasing levels of uncertainty about larval dispersal distances. Our approach finds similar spacing rules as have been proposed elsewhere - roughly 20-200 km - but highlights several advantages provided by uncertainty modelling over more traditional approaches to developing these estimates. In particular, we argue that uncertainty modelling can allow for (1) an evaluation of the risk associated with any decision based on the assumed uncertainty; (2) a method for quantifying the costs and benefits of reducing uncertainty; and (3) a useful tool for communicating to stakeholders the challenges in managing highly uncertain systems. We also argue that incorporating rather than avoiding uncertainty will increase the chances of successfully achieving conservation and management goals.

The U.S. Environmental Protection Agency (U.S. EPA) is sponsoring development of ecological soil screening levels (Eco-SSLs) for terrestrial wildlife. These are intended to be used to identify chemicals of potential ecological concern at Superfund sites. Ecological soil screening levels represent concentrations of contaminants in soils that are believed to be protective of ecological receptors. An exposure model, based on soil- and food-ingestion rates and the relationship between the concentrations of contaminants in soil and food, has been developed for estimation of wildlife Eco-SSLs. It is important to understand how conservative and protective these values are, how parameterization of the model influences the resulting Eco-SSL, and how the treatment of uncertainty impacts results. The Eco-SSLs were calculated for meadow voles (Microtus pennsylvanicus) and northern short-tailed shrews (Blarina brevicauda) for lead and DDT using deterministic and probabilistic methods. Conclusions obtained include that use of central-tendency point estimates may result in hazard quotients much larger than one; that a Monte Carlo approach also leads to hazard quotients that can be substantially larger than one; that, if no hazard quotients larger than one are allowed, any probabilistic approach is identical to a worst-case approach; and that an improvement in the quality and amount of data is necessary to increase confidence that Eco-SSLs are protective at their intended levels of conservatism.

This study examines estimates of extinction rates for the current purported biotic crisis and from the fossil record. Studies that compare current and geological extinctions sometimes use metrics that confound different sources of error and reflect different features of extinction processes. The per taxon extinction rate is a standard measure in paleontology that avoids some of the pitfalls of alternative approaches. Extinction rates reported in the conservation literature are rarely accompanied by measures of uncertainty, despite many elements of the calculations being subject to considerable error. We quantify some of the most important sources of uncertainty and carry them through the arithmetic of extinction rate calculations using fuzzy numbers. The results emphasize that estimates of current and future rates rely heavily on assumptions about the tempo of extinction and on extrapolations among taxa. Available data are unlikely to be useful in measuring magnitudes or trends in current extinction rates.