Spatial prioritization for widespread invasive species control: Trade-offs between current impact and future spread.

Spatially explicit prioritization of invasive species control is a complex issue, requiring consideration of trade-offs between immediate and future benefits. This study aimed to prioritize management efforts to account for current and future threats from widespread invasions and examine the strength of the trade-off between these different management goals. As a case study, we identified spatially explicit management priorities for the widespread invasion of introduced willow into riparian and wetland habitats across a 102,145-km2 region in eastern Australia. In addition to targeting places where willow threatens biodiversity now, a second set of management goals was to limit reinfestation and further spread that could occur via two different mechanisms (downstream and by wind). A model of likely willow distribution across the region was combined with spatial data for biodiversity (native vegetation, threatened species and communities), ecological conditions, management costs, and two potential dispersal layers. We used systematic conservation planning software (Zonation) to prioritize where willow management should be focussed across more than 100,000 catchments for a range of different scenarios that reflected different weights between management goals. For willow invasion, we found that we could prioritize willow management to reduce the future threat of dispersal downstream with little reduction in the protection of biodiversity. However, accounting for future threats from wind dispersal resulted in a stronger trade-off with protection of threatened biodiversity. The strongest trade-off was observed when both dispersal mechanisms were considered together. This study shows that considering current and future goals together offers the potential to substantially improve conservation outcomes for invasive species management. Our approach also informs land managers about the relative trade-offs among different management goals under different control scenarios, helping to make management decisions more transparent. This approach can be used for other widespread invasive species to help improve invasive species management decisions.

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.

A framework for allocating conservation resources among multiple threats and actions.

Land managers decide how to allocate resources among multiple threats that can be addressed through multiple possible actions. Additionally, these actions vary in feasibility, effectiveness, and cost. We sought to provide a way to optimize resource allocation to address multiple threats when multiple management options are available, including mutually exclusive options. Formulating the decision as a combinatorial optimization problem, our framework takes as inputs the expected impact and cost of each threat for each action (including do nothing) and for each overall budget identifies the optimal action to take for each threat. We compared the optimal solution to an easy to calculate greedy algorithm approximation and a variety of plausible ranking schemes. We applied the framework to management of multiple introduced plant species in Australian alpine areas. We developed a model of invasion to predict the expected impact in 50 years for each species-action combination that accounted for each species' current invasion state (absent, localized, widespread); arrival probability; spread rate; impact, if present, of each species; and management effectiveness of each species-action combination. We found that the recommended action for a threat changed with budget; there was no single optimal management action for each species; and considering more than one candidate action can substantially increase the management plan's overall efficiency. The approximate solution (solution ranked by marginal cost-effectiveness) performed well when the budget matched the cost of the prioritized actions, indicating that this approach would be effective if the budget was set as part of the prioritization process. The ranking schemes varied in performance, and achieving a close to optimal solution was not guaranteed. Global sensitivity analysis revealed a threat's expected impact and, to a lesser extent, management effectiveness were the most influential parameters, emphasizing the need to focus research and monitoring efforts on their quantification.

Un Marco de Referencia para Asignar Recursos para la Conservación entre Múltiples Amenazas y Acciones Resumen Los administradores de tierras deciden cómo asignar recursos entre múltiples amenazas que pueden abordarse por medio de múltiples acciones. Adicionalmente, estas acciones varían en viabilidad, efectividad y costo. Buscamos proporcionar una manera para optimizar la asignación de recursos para abordar varias amenazas cuando están disponibles muchas opciones de manejo, incluyendo opciones mutuamente excluyentes. Con una formulación de la decisión como un problema combinatorio de optimización, nuestro marco de referencia toma como entradas el impacto esperado y el costo de cada amenaza para cada acción (incluyendo hacer nada) y para cada presupuesto generalizado identifica la acción óptima a realizar ante cada amenaza. Comparamos la solución óptima con una aproximación de un algoritmo avaricioso fácil de calcular y una variedad de esquemas plausibles de clasificación. Aplicamos el marco de trabajo al manejo de múltiples especies de plantas introducidas en las áreas alpinas de Australia. Desarrollamos un modelo de invasión para predecir el impacto esperado en 50 años para cada combinación de especie-acción que consideró el estado actual de invasión para cada especie (ausente, localizada, ampliamente distribuida), la probabilidad de invasión, la tasa de esparcimiento, el impacto, cuando abundante, de cada especie y la efectividad de manejo de cada combinación especie-acción. Descubrimos que la acción recomendada para una amenaza cambia con el presupuesto, que no existe una acción única de manejo óptimo para cada especie y que considerar más de una acción candidata puede incrementar sustancialmente la eficiencia general del plan de manejo. La solución aproximada (solución clasificada por rentabilidad) tuvo un buen desempeño cuando el presupuesto fue igual al costo de las acciones prioritarias, lo que indica que esta estrategia sería efectiva si el presupuesto está fijado como parte del proceso de priorización. Los esquemas de clasificación variaron en cuanto a desempeño, y lograr una solución cercana a lo óptimo no estuvo garantizado. El análisis de sensibilidad global reveló que el impacto esperado de una amenaza y, a menor grado, la efectividad del manejo no fueron los parámetros con mayor influencia, lo que enfatiza la necesidad de enfocar la investigación y los esfuerzos de monitoreo en la cuantificación del impacto esperado y la efectividad del manejo.

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.

Evaluating conservation dogs in the search for rare species.

Detecting rare species is important for both threatened species management and invasive species eradication programs. Conservation scent dogs provide an olfactory survey tool that has advantages over traditional visual and auditory survey techniques for some cryptic species. From the literature, we identified 5 measures important in evaluating the use of scent dogs: precision, sensitivity, effort, cost, and comparison with other techniques. We explored the scale at which performance is evaluated and examined when field testing under real working conditions is achievable. We also identified cost differences among studies. We examined 61 studies published in 1976-2018 that reported conservation dog performance, and considered the inconsistencies in the reporting of scent dog performance among these studies. The majority of studies reported some measure of performance; however, only 8 studies reported all 3 aspects necessary for performance evaluation: precision, sensitivity, and effort. Although effort was considered in 43 studies, inconsistent methods and incomplete reporting prevented meaningful evaluation of performance and comparison among studies. Differences in cost between similar studies were influenced by geographical location and how the dog and handler were sourced for the study. To develop consistent reporting for evaluation, we recommend adoption of sensitivity, precision, and effort as standard performance measures. We recommend reporting effort as the total area and total time spent searching and reporting sensitivity and precision as proportions of the sample size. Additionally, reporting of costs, survey objectives, dog training and experience, type of detection task, and human influences will provide better opportunities for comparison within and among studies.

Evaluación del Uso de Perros en la Conservación para la Búsqueda de Especies Raras Resumen La detección de las especies raras es importante tanto para el manejo de especies amenazadas como para los programas de erradicación de especies invasoras. Los perros usados para la conservación proporcionan una herramienta de censo olfativo que presenta algunas ventajas sobre las técnicas de censos visuales y auditivos usados para algunas especies crípticas. Identificamos cinco medidas importantes para la evaluación del uso de perros a partir de la literatura: precisión, sensibilidad, esfuerzo, costo y comparación con otras técnicas. Exploramos la escala a la cual se evalúa el desempeño y examinamos cuando son alcanzables las pruebas de campo bajo condiciones de trabajo reales. Examinamos 61 estudios publicados entre 1976 y 2018, los cuales reportaron sobre el desempeño de los perros para la conservación y consideramos las incongruencias en el reporte del desempeño de perros de olfato entre estos estudios. La mayoría de los estudios reportaron los tres aspectos necesarios para la evaluación del desempeño: precisión, sensibilidad y esfuerzo. Aunque 43 estudios consideraron al esfuerzo, los métodos incongruentes y el reporte incompleto impidieron una evaluación significativa del desempeño y una comparación entre los estudios. Las diferencias en el costo entre estudios similares estuvieron influenciadas por la ubicación geográfica y cómo el perro y su adiestrador fueron seleccionados para el estudio. Para desarrollar un reporte coherente para la evaluación, recomendamos la adopción de la sensibilidad la precisión y el esfuerzo como medidas estandarizadas del desempeño. Recomendamos que se reporte el esfuerzo como el área y el tiempo invertido en la búsqueda y la precisión como las proporciones del tamaño de la muestra. Además, el reporte de los costos, los objetivos de los censos, el entrenamiento y experiencia del perro, el tipo de labor de detección y la influencia humana proporcionarán mejores oportunidades para comparar dentro de un estudio o entre los estudios.

A field ecologist's adventures in the virtual world: using simulations to design data collection for complex models.

Field data collection can be expensive, time consuming, and difficult; insightful research requires statistical analyses supported by sufficient data. Pilot studies and power analysis provide guidance on sampling design but can be challenging to perform, as ecologists increasingly collect multiple types of data over different scales. Despite a growing simulation literature, it remains unclear how to appropriately design data collection for many complex projects. Approaches that seek to achieve realism in decision-making contexts, such as management strategy evaluation and virtual ecologist simulations, can help. For a relatively complex analysis, we develop and demonstrate a flexible simulation approach that informs what data are needed and how long those data will take to collect, under realistic fieldwork constraints. We simulated data collection and analysis under different constraint scenarios that varied in deterministic (field trip length, travel, and measurement times) and stochastic (species detection and occupancy rates and inclement weather) features. In our case study, we fit plant height data to a multispecies, three-parameter, nonlinear growth model. We tested how the simulated data sets, based on the varying constraint scenarios, affected the model fit (parameter bias, uncertainty, and capture rate). Species prevalence in the field exerted a stronger influence on the data sets and downstream model performance than deterministic aspects such as travel times. When species detection and occupancy were not considered, the field time needed to collect an adequate data set was underestimated by 40%. Simulations can assist in refining fieldwork design, estimating field costs, and incorporating uncertainties into project planning. We argue that combining data collection, analysis, and decision-making processes in a flexible virtual setting can help address many of the decisions that field ecologists face when designing field-based research.

The gender gap in science: How long until women are equally represented?

Women comprise a minority of the Science, Technology, Engineering, Mathematics, and Medicine (STEMM) workforce. Quantifying the gender gap may identify fields that will not reach parity without intervention, reveal underappreciated biases, and inform benchmarks for gender balance among conference speakers, editors, and hiring committees. Using the PubMed and arXiv databases, we estimated the gender of 36 million authors from >100 countries publishing in >6000 journals, covering most STEMM disciplines over the last 15 years, and made a web app allowing easy access to the data (https://lukeholman.github.io/genderGap/). Despite recent progress, the gender gap appears likely to persist for generations, particularly in surgery, computer science, physics, and maths. The gap is especially large in authorship positions associated with seniority, and prestigious journals have fewer women authors. Additionally, we estimate that men are invited by journals to submit papers at approximately double the rate of women. Wealthy countries, notably Japan, Germany, and Switzerland, had fewer women authors than poorer ones. We conclude that the STEMM gender gap will not close without further reforms in education, mentoring, and academic publishing.

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.

Optimal surveillance strategy for invasive species management when surveys stop after detection.

Invasive species are a cause for concern in natural and economic systems and require both monitoring and management. There is a trade-off between the amount of resources spent on surveying for the species and conducting early management of occupied sites, and the resources that are ultimately spent in delayed management at sites where the species was present but undetected. Previous work addressed this optimal resource allocation problem assuming that surveys continue despite detection until the initially planned survey effort is consumed. However, a more realistic scenario is often that surveys stop after detection (i.e., follow a "removal" sampling design) and then management begins. Such an approach will indicate a different optimal survey design and can be expected to be more efficient. We analyze this case and compare the expected efficiency of invasive species management programs under both survey methods. We also evaluate the impact of mis-specifying the type of sampling approach during the program design phase. We derive analytical expressions that optimize resource allocation between monitoring and management in surveillance programs when surveys stop after detection. We do this under a scenario of unconstrained resources and scenarios where survey budget is constrained. The efficiency of surveillance programs is greater if a "removal survey" design is used, with larger gains obtained when savings from early detection are high, occupancy is high, and survey costs are not much lower than early management costs at a site. Designing a surveillance program disregarding that surveys stop after detection can result in an efficiency loss. Our results help guide the design of future surveillance programs for invasive species. Addressing program design within a decision-theoretic framework can lead to a better use of available resources. We show how species prevalence, its detectability, and the benefits derived from early detection can be considered.

Quantifying plant colour and colour difference as perceived by humans using digital images.

Human perception of plant leaf and flower colour can influence species management. Colour and colour contrast may influence the detectability of invasive or rare species during surveys. Quantitative, repeatable measures of plant colour are required for comparison across studies and generalisation across species. We present a standard method for measuring plant leaf and flower colour traits using images taken with digital cameras. We demonstrate the method by quantifying the colour of and colour difference between the flowers of eleven grassland species near Falls Creek, Australia, as part of an invasive species detection experiment. The reliability of the method was tested by measuring the leaf colour of five residential garden shrub species in Ballarat, Australia using five different types of digital camera. Flowers and leaves had overlapping but distinct colour distributions. Calculated colour differences corresponded well with qualitative comparisons. Estimates of proportional cover of yellow flowers identified using colour measurements correlated well with estimates obtained by measuring and counting individual flowers. Digital SLR and mirrorless cameras were superior to phone cameras and point-and-shoot cameras for producing reliable measurements, particularly under variable lighting conditions. The analysis of digital images taken with digital cameras is a practicable method for quantifying plant flower and leaf colour in the field or lab. Quantitative, repeatable measurements allow for comparisons between species and generalisations across species and studies. This allows plant colour to be related to human perception and preferences and, ultimately, species management.

Estimating detection-effort curves for plants using search experiments.

Recent studies suggest that plant detection is not perfect, even for large, highly visible plants. However, this is often not taken into account during plant surveys where failing to detect a plant when present can result in poor management and biodiversity outcomes. Including knowledge of imperfect detectability into survey design and evaluation is hampered by the paucity of empirical data, and in particular, how detectability will change with search effort, plant size and abundance, the surrounding vegetation, or observer experience. We carried out a search experiment to measure the detection-effort curve for the invasive species orange hawkweed (Hieracium aurantiacum) in Victoria, Australia. The probability that hawkweed was detected increased with increasing search effort and the number of plants at the location. While detection probability varied between observers, experience appeared to have little effect. Accounting for imperfect detectability in plant surveys holds much promise for improved survey design and biodiversity outcomes, and we encourage other researchers to undertake similar experiments to further our understanding of plant detectability.

Active adaptive conservation of threatened species in the face of uncertainty.

Adaptive management has a long history in the natural resource management literature, but despite this, few practitioners have developed adaptive strategies to conserve threatened species. Active adaptive management provides a framework for valuing learning by measuring the degree to which it improves long-run management outcomes. The challenge of an active adaptive approach is to find the correct balance between gaining knowledge to improve management in the future and achieving the best short-term outcome based on current knowledge. We develop and analyze a framework for active adaptive management of a threatened species. Our case study concerns a novel facial tumor disease affecting the Australian threatened species Sarcophilus harrisii: the Tasmanian devil. We use stochastic dynamic programming with Bayesian updating to identify the management strategy that maximizes the Tasmanian devil population growth rate, taking into account improvements to management through learning to better understand disease latency and the relative effectiveness of three competing management options. Exactly which management action we choose each year is driven by the credibility of competing hypotheses about disease latency and by the population growth rate predicted by each hypothesis under the competing management actions. We discover that the optimal combination of management actions depends on the number of sites available and the time remaining to implement management. Our approach to active adaptive management provides a framework to identify the optimal amount of effort to invest in learning to achieve long-run conservation objectives.

Streamlining 'search and destroy': cost-effective surveillance for invasive species management.

Invasive species surveillance has typically been targeted to where the species is most likely to occur. However, spatially varying environmental characteristics and land uses may affect more than just the probability of occurrence. Biodiversity or economic value, and the ease of detection and control are also likely to vary. We incorporate these factors into a detection and treatment model of a low-density invader to determine the surveillance strategy that minimizes expected management costs. Sites with a high probability of invader occurrence and great benefits associated with detection warrant intensive surveillance; however, the optimum investment is a nonlinear function of these factors. Environments where the invader is relatively easy to detect are prioritized for surveillance, although only a moderate investment is necessary to ensure a high probability of detection. Intensive surveillance effort may be allocated to other sites if the probability of occurrence, budget and/or expected benefits is sufficiently high.

Optimal adaptive management for the translocation of a threatened species.

Active adaptive management (AAM) is an approach to wildlife management that acknowledges our imperfect understanding of natural systems and allows for some resolution of our uncertainty. Such learning may be characterized by risky strategies in the short term. Experimentation is only considered acceptable if it is expected to be repaid by increased returns in the long term, generated by an improved understanding of the system. By setting AAM problems within a decision theory framework, we can find this optimal balance between achieving our objectives in the short term and learning for the long term. We apply this approach to managing the translocation of the bridled nailtail wallaby (Onychogalea fraenata), an endangered species from Queensland, Australia. Our task is to allocate captive-bred animals, between two sites or populations to maximize abundance at the end of the translocation project. One population, at the original site of occupancy, has a known growth rate. A population potentially could be established at a second site of suitable habitat, but we can only learn the growth rate of this new population by monitoring translocated animals. We use a mathematical programming technique called stochastic dynamic programming, which determines optimal management decisions for every possible management trajectory. We find optimal strategies under active and passive adaptive management, which enables us to examine the balance between learning and managing directly. Learning is more often optimal when we have less prior information about the uncertain population growth rate at the new site, when the growth rate at the original site is low, and when there is substantial time remaining in the translocation project. Few studies outside the area of optimal harvesting have framed AAM within a decision theory context. This is the first application to threatened species translocation.

How we value the future affects our desire to learn.

Active adaptive management is increasingly advocated in natural resource management and conservation biology. Active adaptive management looks at the benefit of employing strategies that may be suboptimal in the near term but which may provide additional information that will facilitate better management in future years. However, when comparing management policies it is traditional to weigh future rewards geometrically (at a constant discount rate) which results in far-distant rewards making a negligible contribution to the total benefit. Under such a discounting scheme active adaptive management is rarely of much benefit, especially if learning is slow. A growing number of authors advocate the use of alternative forms of discounting when evaluating optimal strategies for long-term decisions which have a social component. We consider a theoretical harvested population for which the recovery rate from an unharvestably small population size is unknown and look at the effects on the benefit of experimental management when three different forms of discounting are employed. Under geometric discounting, with a discount rate of 5% per annum, managing to learn actively had little benefit. This study demonstrates that discount functions which weigh future rewards more heavily result in more conservative harvesting strategies, but do not necessarily encourage active learning. Furthermore, the optimal management strategy is not equivalent to employing geometric discounting at a lower rate. If alternative discount functions are made mandatory in calculating optimal management strategies for environmental management then this will affect the structure of optimal management regimes and change when and how much we are willing to invest in learning.

Should managed populations be monitored every year?

We often need to estimate the size of wild populations to determine the appropriate management action, for example, to set a harvest quota. Monitoring is usually planned under the assumption that it must be carried out at fixed intervals in time, typically annually, before the harvest quota is set. However, monitoring can be very expensive, and we should weigh the cost of monitoring against the improvement that it makes in decision making. A less costly alternative to monitoring annually is to predict the population size using a population model and information from previous surveys. In this paper, the problem of monitoring frequency is posed within a decision-theory framework. We discover that a monitoring regime that varies according to the state of the system can outperform fixed-interval monitoring. This idea is illustrated using data for a red kangaroo (Macropus rufus) population in South Australia. Whether or not one should monitor in a given year is dependent on the estimated population density in the previous year, the uncertainty in that population estimate, and past rainfall. We discover that monitoring is important when a model-based prediction of population density is very uncertain. This may occur if monitoring has not taken place for several years, or if rainfall has been above average. Monitoring is also important when prior information suggests that the population is near a critical threshold in population abundance. However, monitoring is less important when the optimal management action would not be altered by new information.