A field experiment characterizing variable detection rates during plant surveys.

Surveys aimed at finding threatened and invasive species can be challenging due to individual rarity and low and variable individual detection rates. Detection rate in plant surveys typically varies due to differences among observers, among the individual plants being surveyed (targets), and across background environments. Interactions among these 3 components may occur but are rarely estimated due to limited replication and control during data collection. We conducted an experiment to investigate sources of variation in detection of 2 Pilosella species that are invasive and sparsely distributed in the Alpine National Park, Australia. These species are superficially similar in appearance to other yellow-flowered plants occurring in this landscape. We controlled the presence and color of flowers on target Pilosella plants and controlled their placement in plots, which were selected for their variation in cover of non-target yellow flowers and dominant vegetation type. Observers mimicked Pilosella surveys in the plots and reported 1 categorical and 4 quantitative indicators of their survey experience level. We applied survival analysis to detection data to model the influence of both controlled and uncontrolled variables on detection rate. Orange- and yellow-flowering Pilosella in grass- and heath-dominated vegetation were detected at a higher rate than nonflowering Pilosella. However, this detection gain diminished as the cover of other co-occurring yellow-flowering species increased. Recent experience with Pilosella surveys improved detection rate. Detection experiments are a direct and accessible means of understanding detection processes and interpreting survey data for threatened and invasive species. Our detection findings have been used for survey planning and can inform progress toward eradication. Interaction of target and background characteristics determined detection rate, which enhanced predictions in the Pilosella eradication program and demonstrated the difficulty of transferring detection findings into untested environments.

Un Experimento de Campo que Caracteriza las Tasas Variables de Detección en los Censos de Plantas Resumen Los censos enfocados en encontrar especies amenazadas e invasoras pueden ser un reto debido a la rareza individual y las tasas bajas y variables de detección individual. Las tasas de detección en los censos botánicos varían comúnmente por las diferencias entre los observadores, entre las plantas individuales que se están censando (objetivo de búsqueda) y en el entorno ambiental. La interacción entre estos tres componentes puede ocurrir, pero rara vez se calcula debido a la replicación y control limitados durante la recolección de datos. Realizamos un experimento para investigar el origen de las variaciones en la detección de dos especies de Pilosella que son invasoras y están distribuidas escasamente en el Parque Nacional Alpino en Australia. Estas especies son superficialmente similares en apariencia a otras plantas de flores amarillas que habitan este paisaje. Controlamos la presencia y el color de las flores en las plantas de Pilosella, así como su colocación en lotes, los cuales fueron seleccionados por su variación en la cobertura de flores amarillas y tipos de vegetación circundantes. Los observadores imitaron los censos de Pilosella en los lotes y reportaron un indicador categórico y cuatro cuantitativos de su nivel de experiencia en censos. Aplicamos el análisis de supervivencia a los datos de detección para modelar la influencia de las variables controladas y no controladas sobre la tasa de detección. Las plantas de Pilosella con flores amarillas y anaranjadas en la vegetación dominada por pastos y brezales fueron detectadas con una tasa mayor que las plantas de Pilosella sin flores. Sin embargo, esta ganancia en la detección disminuyó conforme incrementó la cobertura de otras plantas con flores amarillas. La experiencia reciente de los observadores con censos de Pilosella aumentó la tasa de detección. Los experimentos de detección son un medio directo y accesible para entender los procesos de detección e interpretar los datos de los censos de especies amenazadas e invasoras. Nuestros resultados en la detección han sido utilizados para la planeación de censos y pueden guiar el progreso hacia la erradicación. La interacción de las características diana y del entorno determinaron la tasa de detección, la cual mejoró las predicciones en el programa de erradicación de Pilosella y demostró la dificultad de transferir los resultados de detección hacia ambientes sin ensayos.

Reallocating budgets among ongoing and emerging conservation projects.

Conserving biodiversity and combating ecological hazards require cost-effective allocation of limited resources among potential management projects. Project priorities, however, can change over time as underlying social-ecological systems progress, novel priorities emerge, and management capabilities evolve. Thus, reallocation of ongoing investments in response to shifting priorities could improve management outcomes and address urgent demands, especially when additional funding is not available immediately. Resource reallocation, however, could incur transaction costs, require additional monitoring and reassessment, and be constrained by ongoing project commitments. Such complexities may prevent managers from considering potentially beneficial reallocation strategies, reducing long-term effectiveness. We propose an iterative project prioritization approach, based on marginal return-on-investment estimation and portfolio optimization, that guides resource reallocation among ongoing and new projects. Using simulation experiments in 2 case studies, we explored how this approach can improve efficacy under varying reallocation constraints, frequencies, costs, and rates of project portfolio change. Periodic budget reallocation could enhance the management of stochastically emerging invasive weeds in Australia and thus reduce the overall risk by up to 50% compared with a static budget. Reallocation frequency and the rate of new weed incursion synergistically increased the conservation gains achieved by allowing unconstrained reallocation. Conversely, budget reallocation would not improve the International Union for Conservation of Nature conservation status of threatened Australian birds due to slow rates of transition among conservation states; extinction risk could increase if portfolio reassessment is costly. Although other project prioritization studies may recommend periodic reassessment and reallocation, our findings revealed conditions when reallocation is valuable and demonstrated a structured approach that can help conservation agencies schedule and implement iterative budget-allocation decisions cost-effectively.

Reasignación de Presupuestos entre los Proyectos de Conservación Emergentes y En Curso Resumen La conservación de la biodiversidad y el combate a los riesgos ecológicos requieren de una asignación rentable de los recursos limitados entre los proyectos potenciales de manejo. Sin embargo, las prioridades de los proyectos pueden cambiar con el tiempo conforme avanzan los sistemas socio-ecológicos subyacentes, emergen prioridades nuevas y evolucionan las capacidades de manejo. Por lo tanto, la reasignación de las inversiones en curso como respuesta a las prioridades cambiantes podría mejorar los resultados de manejo y resolver demandas urgentes, especialmente cuando el financiamiento adicional no está disponible de manera inmediata. Sin embargo, la reasignación de recursos podría incurrir en costos de transacción, requerir de monitoreo y reevaluación adicionales y estar restringida por los compromisos hechos por los proyectos en curso. Dichas complejidades pueden evitar que los administradores consideren estrategias de reasignación potencialmente benéficas, reduciendo así la efectividad a largo plazo. Proponemos un enfoque iterativo de priorización de proyectos basado en una estimación marginal de rentabilidad y en la optimización del portafolio, el cual guíe la reasignación de recursos entre los proyectos nuevos y en curso. Mediante experimentos de simulación en dos estudios de caso, exploramos cómo este enfoque puede mejorar la eficacia bajo cambiantes restricciones de reasignación, frecuencias, costos y tasas de cambio en el portafolio del proyecto. La reasignación periódica de presupuestos podría mejorar el manejo de las hierbas invasoras con surgimiento estocástico en Australia y así reducir el riesgo general hasta en un 50% en comparación con un presupuesto estático. La frecuencia de reasignación y la tasa de incursión de hierbas nuevas incrementaron de manera sinérgica las ganancias de conservación obtenidas al permitir una reasignación sin restricciones. En cambio, la reasignación de presupuestos no mejoraría el estado de conservación según la Unión Internacional para la Conservación de la Naturaleza de las aves australianas amenazadas debido a las tasas lentas de transición entre los estados de conservación; el riesgo de extinción podría incrementar si la reevaluación del portafolio es costosa. Aunque otros estudios de priorización de proyectos pueden recomendar una reevaluación y una reasignación periódicas, nuestros resultados revelaron condiciones cuando la reasignación es valiosa y demostraron una estrategia estructurada que puede ayudar a las agencias de conservación a programar e implementar decisiones iterativas de asignación de presupuestos de manera rentable.

Using decision science to evaluate global biodiversity indices.

Global biodiversity indices are used to measure environmental change and progress toward conservation goals, yet few indices have been evaluated comprehensively for their capacity to detect trends of interest, such as declines in threatened species or ecosystem function. Using a structured approach based on decision science, we qualitatively evaluated 9 indices commonly used to track biodiversity at global and regional scales against 5 criteria relating to objectives, design, behavior, incorporation of uncertainty, and constraints (e.g., costs and data availability). Evaluation was based on reference literature for indices available at the time of assessment. We identified 4 key gaps in indices assessed: pathways to achieving goals (means objectives) were not always clear or relevant to desired outcomes (fundamental objectives); index testing and understanding of expected behavior was often lacking; uncertainty was seldom acknowledged or accounted for; and costs of implementation were seldom considered. These gaps may render indices inadequate in certain decision-making contexts and are problematic for indices linked with biodiversity targets and sustainability goals. Ensuring that index objectives are clear and their design is underpinned by a model of relevant processes are crucial in addressing the gaps identified by our assessment. Uptake and productive use of indices will be improved if index performance is tested rigorously and assumptions and uncertainties are clearly communicated to end users. This will increase index accuracy and value in tracking biodiversity change and supporting national and global policy decisions, such as the post-2020 global biodiversity framework of the Convention on Biological Diversity.

Uso de las Ciencias de la Decisión para Evaluar los Índices Globales de Biodiversidad Resumen Los índices globales de biodiversidad se usan para medir el cambio ambiental y el avance hacia los objetivos de conservación, aunque pocos han sido evaluados completamente en cuanto a su capacidad para detectar las tendencias de interés como las declinaciones de especies amenazadas o la función del ecosistema. Evaluamos cualitativamente nueve índices de uso común para dar seguimiento a la biodiversidad a escala global y regional contra cinco criterios relacionados con los objetivos, diseño, comportamiento, incorporación de la incertidumbre y restricciones (p. ej.: costos y disponibilidad de datos) mediante una estrategia estructurada basada en las ciencias de la decisión. La evaluación se basó en la literatura de referencia para los índices disponibles al momento del análisis. Identificamos cuatro vacíos importantes en los índices estudiados: las vías para lograr los objetivos (objetivos medios) no fueron siempre claras o relevantes para los resultados deseados (objetivos fundamentales); el análisis del índice y el entendimiento del comportamiento esperado casi siempre fueron escasos; pocas veces se consideró o explicó la incertidumbre; y casi nunca se consideraron los costos de la implementación. Estos vacíos pueden hacer que los índices sean inadecuados en ciertos contextos de toma de decisiones y son problemáticos para los índices vinculados a los objetivos de biodiversidad y las metas de sustentabilidad. Es de suma importancia asegurarse que los objetivos del índice sean claros y que su diseño esté respaldado por un modelo de procesos relevantes para tratar con los vacíos identificados en nuestro estudio. La aceptación y el uso productivo de los índices mejorarán si el desempeño del índice es evaluado rigurosamente y las suposiciones e incertidumbres se les comunican claramente a los usuarios finales. Lo anterior aumentará la precisión y valor del índice en el seguimiento de los cambios de la biodiversidad y en el apoyo a las decisiones políticas nacionales y mundiales, como el marco de trabajo para la biodiversidad post-2020 establecido por la Convención sobre la Diversidad Biológica.

Simultaneous-count models to estimate abundance from counts of unmarked individuals with imperfect detection.

We developed a method to estimate population abundance from simultaneous counts of unmarked individuals over multiple sites. We considered that at each sampling occasion, individuals in a population could be detected at 1 of the survey sites or remain undetected and used either multinomial or binomial simultaneous-count models to estimate abundance, the latter being equivalent to an N-mixture model with one site. We tested model performance with simulations over a range of detection probabilities, population sizes, growth rates, number of years, sampling occasions, and sites. We then applied our method to 3 critically endangered vulture species in Cambodia to demonstrate the real-world applicability of the model and to provide the first abundance estimates for these species in Cambodia. Our new approach works best when existing methods are expected to perform poorly (i.e., few sites and large variation in abundance among sites) and if individuals may move among sites between sampling occasions. The approach performed better when there were >8 sampling occasions and net probability of detection was high (>0.5). We believe our approach will be useful in particular for simultaneous surveys at aggregation sites, such as roosts. The method complements existing approaches for estimating abundance of unmarked individuals and is the first method designed specifically for simultaneous counts.

Modelos de Conteo Simultáneo para Estimar la Abundancia a partir de Conteos de Individuos No Marcados con Detección Imperfecta Resumen Desarrollamos un método para estimar la abundancia poblacional a partir de conteos simultáneos de individuos sin marcaje en múltiples sitios. Consideramos que en cada ocasión de muestreo los individuos de una población podrían ser detectados en uno de los sitios de censos o podrían permanecer sin ser detectados y usamos modelos de conteo simultáneo multinomial o binomial para estimar la abundancia, con el binomial como equivalente a un modelo de mezcla N con un solo sitio. Probamos el desempeño del modelo con simulaciones en un rango de probabilidades de detección, tamaños poblacionales, tasas de crecimiento, número de años, ocasiones de muestreo, y sitios. Después aplicamos nuestro método a tres especies de buitre que se encuentran en peligro crítico en Camboya para demostrar cuán aplicable es el modelo en el mundo real y para proporcionar las primeras estimaciones de abundancia para estas especies en Camboya. Nuestra nueva estrategia trabaja de mejor manera cuando se espera que los modelos existentes tengan un desempeño pobre (es decir, pocos sitios y una gran variación en la abundancia entre sitios) y si los individuos podrían moverse de un sitio a otro entre cada ocasión de muestreo. La estrategia tuvo un mejor desempeñó cuando hubo >8 ocasiones de muestreo y la probabilidad neta de detección fue alta (>0.5). Creemos que nuestra estrategia será especialmente útil para censos simultáneos en sitios de agregación, como los nidos. El método complementa las estrategias existentes para estimar la abundancia de individuos sin marcaje y es el primer método diseñado específicamente para conteos simultáneos.

Optimal timing of biodiversity offsetting for metapopulations.

Biodiversity offsetting schemes permit habitat destruction, provided that losses are compensated by gains elsewhere. While hundreds of offsetting schemes are used around the globe, the optimal timing of habitat creation in such projects is poorly understood. Here, we developed a spatially explicit metapopulation model for a single species subject to a habitat compensation scheme. Managers could compensate for destruction of a patch by creating a new patch either before, at the time of, or after patch loss. Delaying patch creation is intuitively detrimental to species persistence, but allowed managers to invest financial compensation, accrue interest, and create a larger patch at a later date. Using stochastic dynamic programming, we found the optimal timing of patch creation that maximizes the number of patches occupied at the end of a 50-yr habitat compensation scheme when a patch is destroyed after 10 yr. Two case studies were developed for Australian species subject to habitat loss but with very different traits: the endangered growling grass frog (Litoria raniformis) and the critically endangered Mount Lofty Ranges Southern Emu-wren (Spititurus malachurus intermedius). Our results show that adding a patch either before or well after habitat destruction can be optimal, depending on the occupancy state of the metapopulation, the interest rate, the area of the destroyed patch and metapopulation parameters of the focal species. Generally, it was better to delay patch creation when the interest rate was high, when the species had a relatively high colonization rate, when the patch nearest the new patch was occupied, and when the destroyed patch was small. Our framework can be applied to single-species metapopulations subject to habitat loss, and demonstrates that considering the timing of habitat compensation could improve the effectiveness of offsetting schemes.

Metaresearch for Evaluating Reproducibility in Ecology and Evolution.

Recent replication projects in other disciplines have uncovered disturbingly low levels of reproducibility, suggesting that those research literatures may contain unverifiable claims. The conditions contributing to irreproducibility in other disciplines are also present in ecology. These include a large discrepancy between the proportion of "positive" or "significant" results and the average statistical power of empirical research, incomplete reporting of sampling stopping rules and results, journal policies that discourage replication studies, and a prevailing publish-or-perish research culture that encourages questionable research practices. We argue that these conditions constitute sufficient reason to systematically evaluate the reproducibility of the evidence base in ecology and evolution. In some cases, the direct replication of ecological research is difficult because of strong temporal and spatial dependencies, so here, we propose metaresearch projects that will provide proxy measures of reproducibility.

Two-step adaptive management for choosing between two management actions.

Adaptive management is widely advocated to improve environmental management. Derivations of optimal strategies for adaptive management, however, tend to be case specific and time consuming. In contrast, managers might seek relatively simple guidance, such as insight into when a new potential management action should be considered, and how much effort should be expended on trialing such an action. We constructed a two-time-step scenario where a manager is choosing between two possible management actions. The manager has a total budget that can be split between a learning phase and an implementation phase. We use this scenario to investigate when and how much a manager should invest in learning about the management actions available. The optimal investment in learning can be understood intuitively by accounting for the expected value of sample information, the benefits that accrue during learning, the direct costs of learning, and the opportunity costs of learning. We find that the optimal proportion of the budget to spend on learning is characterized by several critical thresholds that mark a jump from spending a large proportion of the budget on learning to spending nothing. For example, as sampling variance increases, it is optimal to spend a larger proportion of the budget on learning, up to a point: if the sampling variance passes a critical threshold, it is no longer beneficial to invest in learning. Similar thresholds are observed as a function of the total budget and the difference in the expected performance of the two actions. We illustrate how this model can be applied using a case study of choosing between alternative rearing diets for hihi, an endangered New Zealand passerine. Although the model presented is a simplified scenario, we believe it is relevant to many management situations. Managers often have relatively short time horizons for management, and might be reluctant to consider further investment in learning and monitoring beyond collecting data from a single time period.

Consistent patterns of vehicle collision risk for six mammal species.

The occurrence and rate of wildlife-vehicle collisions are related to both anthropocentric and environmental variables, however, few studies compare collision risks for multiple species within a model framework that is adaptable and transferable. Our research compares collision risk for multiple species across a large geographic area using a conceptually simple risk framework. We used six species of native terrestrial mammal often involved with wildlife-vehicle collisions in south-east Australia. We related collisions reported to a wildlife organisation to the co-occurrence of each species and a threatening process (presence and movement of road vehicles). For each species, we constructed statistical models from wildlife atlas data to predict occurrence across geographic space. Traffic volume and speed on road segments (also modelled) characterised the magnitude of threatening processes. The species occurrence models made plausible spatial predictions. Each model reduced the unexplained variation in patterns and distributions of species between 29.5% (black wallaby) and 34.3% (koala). The collision models reduced the unexplained variation in collision event data between 7.4% (koala) and 19.4% (common ringtail possum) with predictor variables correlating similarly with collision risk across species. Road authorities and environmental managers need simple and flexible tools to inform projects. Our model framework is useful for directing mitigation efforts (e.g. on road effects or species presence), predicting risk across differing spatial and temporal scales and target species, inferring patterns of threat, and identifying areas warranting additional data collection, analysis, and study.

Abiotic and biotic interactions determine whether increased colonization is beneficial or detrimental to metapopulation management.

Increasing the colonization rate of metapopulations can improve persistence, but can also increase exposure to threats. To make good decisions, managers must understand whether increased colonization is beneficial or detrimental to metapopulation persistence. While a number of studies have examined interactions between metapopulations, colonization, and threats, they have assumed that threat dynamics respond linearly to changes in colonization. Here, we determined when to increase colonization while explicitly accounting for non-linear dependencies between a metapopulation and its threats. We developed patch occupancy metapopulation models for species susceptible to abiotic, generalist, and specialist threats and modeled the total derivative of the equilibrium proportion of patches occupied by each metapopulation with respect to the colonization rate. By using the total derivative, we developed a rule for determining when to increase metapopulation colonization. This rule was applied to a simulated metapopulation where the dynamics of each threat responded to increased colonization following a power function. Before modifying colonization, we show that managers must understand: (1) whether a metapopulation is susceptible to a threat; (2) the type of threat acting on a metapopulation; (3) which component of threat dynamics might depend on colonization, and; (4) the likely response of a threat-dependent variable to changes in colonization. The sensitivity of management decisions to these interactions increases uncertainty in conservation planning decisions.

Learning about colonization when managing metapopulations under an adaptive management framework.

Adaptive management is a framework for resolving key uncertainties while managing complex ecological systems. Its use has been prominent in fisheries research and wildlife harvesting; however, its application to other areas of environmental management remains somewhat limited. Indeed, adaptive management has not been used to guide and inform metapopulation restoration, despite considerable uncertainty surrounding such actions. In this study, we determined how best to learn about the colonization rate when managing metapopulations under an adaptive management framework. We developed a mainland-island metapopulation model based on the threatened bay checkerspot butterfly (Euphydryas editha bayensis) and assessed three management approaches: adding new patches, adding area to existing patches, and doing nothing. Using stochastic dynamic programming, we found the optimal passive and active adaptive management strategies by monitoring colonization of vacant patches. Under a passive adaptive strategy, increasing patch area was best when the expected colonization rate was below a threshold; otherwise, adding new patches was optimal. Under an active adaptive strategy, it was best to add patches only when we were reasonably confident that the colonization rate was high. This research provides a framework for managing mainland-island metapopulations in the face of uncertainty while learning about the dynamics of these complex systems.

Planning for ex situ conservation in the face of uncertainty.

Ex situ conservation strategies for threatened species often require long-term commitment and financial investment to achieve management objectives. We present a framework that considers the decision to adopt ex situ management for a target species as the end point of several linked decisions. We used a decision tree to intuitively represent the logical sequence of decision making. The first decision is to identify the specific management actions most likely to achieve the fundamental objectives of the recovery plan, with or without the use of ex-situ populations. Once this decision has been made, one decides whether to establish an ex situ population, accounting for the probability of success in the initial phase of the recovery plan, for example, the probability of successful breeding in captivity. Approaching these decisions in the reverse order (attempting to establish an ex situ population before its purpose is clearly defined) can lead to a poor allocation of resources, because it may restrict the range of available decisions in the second stage. We applied our decision framework to the recovery program for the threatened spotted tree frog (Litoria spenceri) of southeastern Australia. Across a range of possible management actions, only those including ex situ management were expected to provide >50% probability of the species' persistence, but these actions cost more than use of in situ alternatives only. The expected benefits of ex situ actions were predicted to be offset by additional uncertainty and stochasticity associated with establishing and maintaining ex situ populations. Naïvely implementing ex situ conservation strategies can lead to inefficient management. Our framework may help managers explicitly evaluate objectives, management options, and the probability of success prior to establishing a captive colony of any given species.

I Environmental DNA sampling is more sensitive than a traditional survey technique for detecting an aquatic invader.

Effective management of alien species requires detecting populations in the early stages of invasion. Environmental DNA (eDNA) sampling can detect aquatic species at relatively low densities, but few studies have directly compared detection probabilities of eDNA sampling with those of traditional sampling methods. We compare the ability of a traditional sampling technique (bottle trapping) and eDNA to detect a recently established invader, the smooth newt Lissotriton vulgaris vulgaris, at seven field sites in Melbourne, Australia. Over a four-month period, per-trap detection probabilities ranged from 0.01 to 0.26 among sites where L. v. vulgaris was detected, whereas per-sample eDNA estimates were much higher (0.29-1.0). Detection probabilities of both methods varied temporally (across days and months), but temporal variation appeared to be uncorrelated between methods. Only estimates of spatial variation were strongly correlated across the two sampling techniques. Environmental variables (water depth, rainfall, ambient temperature) were not clearly correlated with detection probabilities estimated via trapping, whereas eDNA detection probabilities were negatively correlated with water depth, possibly reflecting higher eDNA concentrations at lower water levels. Our findings demonstrate that eDNA sampling can be an order of magnitude more sensitive than traditional methods, and illustrate that traditional- and eDNA-based surveys can provide independent information on species distributions when occupancy surveys are conducted over short timescales.

Incorporating detectability of threatened species into environmental impact assessment.

Environmental impact assessment (EIA) is a key mechanism for protecting threatened plant and animal species. Many species are not perfectly detectable and, even when present, may remain undetected during EIA surveys, increasing the risk of site-level loss or extinction of species. Numerous methods now exist for estimating detectability of plants and animals. Despite this, regulations concerning survey protocol and effort during EIAs fail to adequately address issues of detectability. Probability of detection is intrinsically linked to survey effort; thus, minimum survey effort requirements are a useful way to address the risks of false absences. We utilized 2 methods for determining appropriate survey effort requirements during EIA surveys. One method determined the survey effort required to achieve a probability of detection of 0.95 when the species is present. The second method estimated the survey effort required to either detect the species or reduce the probability of presence to 0.05. We applied these methods to Pimelea spinscens subsp. spinescens, a critically endangered grassland plant species in Melbourne, Australia. We detected P. spinescens in only half of the surveys undertaken at sites where it was known to exist. Estimates of the survey effort required to detect the species or demonstrate its absence with any confidence were much higher than the effort traditionally invested in EIA surveys for this species. We argue that minimum survey requirements be established for all species listed under threatened species legislation and hope that our findings will provide an impetus for collecting, compiling, and synthesizing quantitative detectability estimates for a broad range of plant and animal species.

Optimal fire histories for biodiversity conservation.

Fire is used as a management tool for biodiversity conservation worldwide. A common objective is to avoid population extinctions due to inappropriate fire regimes. However, in many ecosystems, it is unclear what mix of fire histories will achieve this goal. We determined the optimal fire history of a given area for biological conservation with a method that links tools from 3 fields of research: species distribution modeling, composite indices of biodiversity, and decision science. We based our case study on extensive field surveys of birds, reptiles, and mammals in fire-prone semi-arid Australia. First, we developed statistical models of species' responses to fire history. Second, we determined the optimal allocation of successional states in a given area, based on the geometric mean of species relative abundance. Finally, we showed how conservation targets based on this index can be incorporated into a decision-making framework for fire management. Pyrodiversity per se did not necessarily promote vertebrate biodiversity. Maximizing pyrodiversity by having an even allocation of successional states did not maximize the geometric mean abundance of bird species. Older vegetation was disproportionately important for the conservation of birds, reptiles, and small mammals. Because our method defines fire management objectives based on the habitat requirements of multiple species in the community, it could be used widely to maximize biodiversity in fire-prone ecosystems.

Determining when to change course in management actions.

Time is of the essence in conservation biology. To secure the persistence of a species, we need to understand how to balance time spent among different management actions. A new and simple method to test the efficacy of a range of conservation actions is required. Thus, we devised a general theoretical framework to help determine whether to test a new action and when to cease a trial and revert to an existing action if the new action did not perform well. The framework involves constructing a general population model under the different management actions and specifying a management objective. By maximizing the management objective, we could generate an analytical solution that identifies the optimal timing of when to change management action. We applied the analytical solution to the case of the Christmas Island pipistrelle bat (Pipistrelle murrayi), a species for which captive breeding might have prevented its extinction. For this case, we used our model to determine whether to start a captive breeding program and when to stop a captive breeding program and revert to managing the species in the wild, given that the management goal is to maximize the chance of reaching a target wild population size. For the pipistrelle bat, captive breeding was to start immediately and it was desirable to place the species in captivity for the entire management period. The optimal time to revert to managing the species in the wild was driven by several key parameters, including the management goal, management time frame, and the growth rates of the population under different management actions. Knowing when to change management actions can help conservation managers' act in a timely fashion to avoid species extinction.

Linking indices for biodiversity monitoring to extinction risk theory.

Biodiversity indices often combine data from different species when used in monitoring programs. Heuristic properties can suggest preferred indices, but we lack objective ways to discriminate between indices with similar heuristics. Biodiversity indices can be evaluated by determining how well they reflect management objectives that a monitoring program aims to support. For example, the Convention on Biological Diversity requires reporting about extinction rates, so simple indices that reflect extinction risk would be valuable. We developed 3 biodiversity indices that are based on simple models of population viability that relate extinction risk to abundance. We based the first index on the geometric mean abundance of species and the second on a more general power mean. In a third index, we integrated the geometric mean abundance and trend. These indices require the same data as previous indices, but they also relate directly to extinction risk. Field data for butterflies and woodland plants and experimental studies of protozoan communities show that the indices correlate with local extinction rates. Applying the index based on the geometric mean to global data on changes in avian abundance suggested that the average extinction probability of birds has increased approximately 1% from 1970 to 2009.

Predicting the effect of urban noise on the active space of avian vocal signals.

Urbanization changes the physical environment of nonhuman species but also markedly changes their acoustic environment. Urban noise interferes with acoustic communication in a range of animals, including birds, with potentially profound impacts on fitness. However, a mechanistic theory to predict which species of birds will be most affected by urban noise is lacking. We develop a mathematical model to predict the decrease in the active space of avian vocal signals after moving from quiet forest habitats to noisy urban habitats. We find that the magnitude of the decrease is largely a function of signal frequency. However, this relationship is not monotonic. A metaregression of observed increases in the frequency of birdsong in urban noise supports the model's predictions for signals with frequencies between 1.5 and 4 kHz. Using results of the metaregression and the model described above, we show that the expected gain in active space following observed frequency shifts is up to 12% and greatest for birds with signals at the lower end of this frequency range. Our generally applicable model, along with three predictions regarding the behavioral and population-level responses of birds to urban noise, represents an important step toward a theory of acoustic communication in urban habitats.

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.

A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change.

1. Informative Bayesian priors can improve the precision of estimates in ecological studies or estimate parameters for which little or no information is available. While Bayesian analyses are becoming more popular in ecology, the use of strongly informative priors remains rare, perhaps because examples of informative priors are not readily available in the published literature. 2. Dispersal distance is an important ecological parameter, but is difficult to measure and estimates are scarce. General models that provide informative prior estimates of dispersal distances will therefore be valuable. 3. Using a world-wide data set on birds, we develop a predictive model of median natal dispersal distance that includes body mass, wingspan, sex and feeding guild. This model predicts median dispersal distance well when using the fitted data and an independent test data set, explaining up to 53% of the variation. 4. Using this model, we predict a priori estimates of median dispersal distance for 57 woodland-dependent bird species in northern Victoria, Australia. These estimates are then used to investigate the relationship between dispersal ability and vulnerability to landscape-scale changes in habitat cover and fragmentation. 5. We find evidence that woodland bird species with poor predicted dispersal ability are more vulnerable to habitat fragmentation than those species with longer predicted dispersal distances, thus improving the understanding of this important phenomenon. 6. The value of constructing informative priors from existing information is also demonstrated. When used as informative priors for four example species, predicted dispersal distances reduced the 95% credible intervals of posterior estimates of dispersal distance by 8-19%. Further, should we have wished to collect information on avian dispersal distances and relate it to species' responses to habitat loss and fragmentation, data from 221 individuals across 57 species would have been required to obtain estimates with the same precision as those provided by the general model.