Designing a large-scale track-based monitoring program to detect changes in species distributions in arid Australia.

Monitoring trends in animal populations in arid regions is challenging due to remoteness and low population densities. However, detecting species' tracks or signs is an effective survey technique for monitoring population trends across large spatial and temporal scales. In this study, we developed a simulation framework to evaluate the performance of alternative track-based monitoring designs at detecting change in species distributions in arid Australia. We collated presence-absence records from 550 2-ha track-based plots for 11 vertebrates over 13 years and fitted ensemble species distribution models to predict occupancy in 2018. We simulated plausible changes in species' distributions over the next 15 years and, with estimates of detectability, simulated monitoring to evaluate the statistical power of three alternative monitoring scenarios: (1) where surveys were restricted to existing 2-ha plots, (2) where surveys were optimized to target all species equally, and (3) where surveys were optimized to target two species of conservation concern. Across all monitoring designs and scenarios, we found that power was higher when detecting increasing occupancy trends compared to decreasing trends owing to the relatively low levels of initial occupancy. Our results suggest that surveying 200 of the existing plots annually (with a small subset resurveyed twice within a year) will have at least an 80% chance of detecting 30% declines in occupancy for four of the five invasive species modeled and one of the six native species. This increased to 10 of the 11 species assuming larger (50%) declines. When plots were positioned to target all species equally, power improved slightly for most compared to the existing survey network. When plots were positioned to target two species of conservation concern (crest-tailed mulgara and dusky hopping mouse), power to detect 30% declines increased by 29% and 31% for these species, respectively, at the cost of reduced power for the remaining species. The effect of varying survey frequency depended on its trade-off with the number of sites sampled and requires further consideration. Nonetheless, our research suggests that track-based surveying is an effective and logistically feasible approach to monitoring broad-scale occupancy trends in desert species with both widespread and restricted distributions.

Quantifying the impact of vegetation-based metrics on species persistence when choosing offsets for habitat destruction.

Developers are often required by law to offset environmental impacts through targeted conservation actions. Most offset policies specify metrics for calculating offset requirements, usually by assessing vegetation condition. Despite widespread use, there is little evidence to support the effectiveness of vegetation-based metrics for ensuring biodiversity persistence. We compared long-term impacts of biodiversity offsetting based on area only; vegetation condition only; area × habitat suitability; and condition × habitat suitability in development and restoration simulations for the Hunter Region of New South Wales, Australia. We simulated development and subsequent offsetting through restoration within a virtual landscape, linking simulations to population viability models for 3 species. Habitat gains did not ensure species persistence. No net loss was achieved when performance of offsetting was assessed in terms of amount of habitat restored, but not when outcomes were assessed in terms of persistence. Maintenance of persistence occurred more often when impacts were avoided, giving further support to better enforce the avoidance stage of the mitigation hierarchy. When development affected areas of high habitat quality for species, persistence could not be guaranteed. Therefore, species must be more explicitly accounted for in offsets, rather than just vegetation or habitat alone. Declines due to a failure to account directly for species population dynamics and connectivity overshadowed the benefits delivered by producing large areas of high-quality habitat. Our modeling framework showed that the benefits delivered by offsets are species specific and that simple vegetation-based metrics can give misguided impressions on how well biodiversity offsets achieve no net loss.

Cuantificación del Impacto de las Medidas Basadas en la Vegetación sobre la Persistencia de las Especies cuando se Eligen las Compensaciones por la Destrucción del Hábitat Resumen Con frecuencia se requiere por ley que los desarrolladores compensen los impactos ambientales por medio de acciones de conservación. La mayoría de las políticas de compensación especifican medidas para calcular los requerimientos de cada compensación, generalmente mediante la evaluación de las condiciones de la vegetación. A pesar del uso extenso de estas medidas basadas en la vegetación, existe muy poca evidencia que respalde su efectividad para asegurar la persistencia de la biodiversidad. Comparamos los impactos a largo plazo de las compensaciones de biodiversidad basadas solamente en el área; solamente en la condición de la vegetación; la idoneidad del área x hábitat; y la idoneidad condición x hábitat en las simulaciones de desarrollo y restauración para la Región Hunter de Nueva Gales del Sur, Australia. Simulamos el desarrollo y las compensaciones subsecuentes mediante la restauración dentro de un paisaje virtual, conectando las simulaciones con los modelos de viabilidad poblacional para tres especies. Las ganancias del hábitat no aseguraron la persistencia de las especies. No hubo pérdida neta cuando el desempeño de las compensaciones se evaluó en relación con la persistencia. El mantenimiento de la persistencia ocurrió más seguido cuando se evitaron los impactos, lo que proporciona un mayor respaldo para mejorar la aplicación de la fase de prevención de la jerarquía de mitigación. Cuando el desarrollo afectó a las áreas con una alta calidad de hábitat para las especies, no se pudo garantizar la persistencia. Por lo tanto, las especies deben considerarse más explícitamente en las compensaciones, en lugar de sólo considerar a la vegetación o al hábitat. Las declinaciones causadas por la falta de consideración directa de las dinámicas poblacionales de las especies y de la conectividad opacaron los beneficios producidos por las grandes áreas de hábitat de alta calidad. Nuestro marco de trabajo para el modelado demostró que los beneficios producidos por las compensaciones son específicos para cada especie y que las medidas simples basadas en la vegetación pueden brindar impresiones mal informadas sobre qué tanto influyen las compensaciones de biodiversidad en la no pérdida neta.

A checklist of attributes for effective monitoring of threatened species and threatened ecosystems.

Monitoring of threatened species and threatened ecosystems is critical for determining population trends, identifying urgency of management responses, and assessing the efficacy of management interventions. Yet many threatened species and threatened ecosystems are not monitored and for those that are, the quality of the monitoring is often poor. Here we provide a checklist of factors that need to be considered for inclusion in robust monitoring programs for threatened species and threatened ecosystems. These factors can be grouped under four broad themes - the design of monitoring programs, the structure and governance of monitoring programs, data management and reporting, and appropriate funding and legislative support. We briefly discuss key attributes of our checklist under these themes. Key topics in our first theme of the design of monitoring programs include appropriate objective setting, identification of the most appropriate entities to be measured, consistency in methodology and protocols through time, ensuring monitoring is long-term, and embedding monitoring into management. Under our second theme which focuses on the structure and governance of monitoring programs for threatened species and ecosystems, we touch on the importance of adopting monitoring programs that: test the effectiveness of management interventions, produce results that are relevant to management, and engage with (and are accepted by) the community. Under Theme 3, we discuss why data management is critical and highlight that the costs of data curation, analysis and reporting need to be factored into budgets for monitoring programs. This requires that appropriate levels of funding are made available for monitoring programs, beyond just the cost of data collection - a key topic examined in Theme 4. We provide examples, often from Australia, to highlight the importance of each of the four themes. We recognize that these themes and topics in our checklist are often closely inter-related and therefore provide a conceptual model highlighting these linkages. We suggest that our checklist can help identify the parts of existing monitoring programs for threatened species and threatened ecosystems that are adequate for the purpose or may be deficient and need to be improved.

Spatially explicit power analysis for detecting occupancy trends for multiple species.

Assessing the statistical power to detect changes in wildlife populations is a crucial yet often overlooked step when designing and evaluating monitoring programs. Here, we developed a simulation framework to perform spatially explicit statistical power analysis of biological monitoring programs for detecting temporal trends in occupancy for multiple species. Using raster layers representing the spatial variation in current occupancy and species-level detectability for one or multiple observation methods, our framework simulates changes in occupancy over space and time, with the capacity to explicitly model stochastic disturbances at monitoring sites (i.e., dynamic landscapes). Once users specify the number and location of sites, the frequency and duration of surveys, and the type of detection method(s) for each species, our framework estimates power to detect occupancy trends, both across the landscape and/or within nested management units. As a case study, we evaluated the power of a long-term monitoring program to detect trends in occupancy for 136 species (83 birds, 33 reptiles, and 20 mammals) across and within Kakadu, Litchfield, and Nitmiluk National Parks in northern Australia. We assumed continuation of an original monitoring design implemented since 1996, with the addition of camera trapping. As expected, power to detect trends was sensitive to the direction and magnitude of the change in occupancy, detectability, initial occupancy levels, and the rarity of species. Our simulations suggest that monitoring has at least an 80% chance at detecting a 50% decline in occupancy for 22% of the modeled species across the three parks over the next 15 yr. Monitoring is more likely to detect increasing occupancy trends, with at least an 80% chance at detecting a 50% increase in 87% of species. The addition of camera-trapping increased average power to detect a 50% decline in mammals compared with using only live trapping by 63%. We provide a flexible tool that can help decision-makers design and evaluate monitoring programs for hundreds of species at a time in a range of ecological settings, while explicitly considering the distribution of species and alternative sampling methods.

Correction: Occupancy and detectability modelling of vertebrates in northern Australia using multiple sampling methods.

[This corrects the article DOI: 10.1371/journal.pone.0203304.].

Occupancy and detectability modelling of vertebrates in northern Australia using multiple sampling methods.

Understanding where species occur and how difficult they are to detect during surveys is crucial for designing and evaluating monitoring programs, and has broader applications for conservation planning and management. In this study, we modelled occupancy and the effectiveness of six sampling methods at detecting vertebrates across the Top End of northern Australia. We fitted occupancy-detection models to 136 species (83 birds, 33 reptiles, 20 mammals) of 242 recorded during surveys of 333 sites in eight conservation reserves between 2011 and 2016. For modelled species, mean occupancy was highly variable: birds and reptiles ranged from 0.01-0.81 and 0.01-0.49, respectively, whereas mammal occupancy was lower, ranging from 0.02-0.30. Of the 11 environmental covariates considered as potential predictors of occupancy, topographic ruggedness, elevation, maximum temperature, and fire frequency were retained more readily in the top models. Using these models, we predicted species occupancy across the Top End of northern Australia (293,017 km2) and generated species richness maps for each species group. For mammals and reptiles, high richness was associated with rugged terrain, while bird richness was highest in coastal lowland woodlands. On average, detectability of diurnal birds was higher per day of surveys (0.33 ± 0.09) compared with nocturnal birds per night of spotlighting (0.13 ± 0.06). Detectability of reptiles was similar per day/night of pit trapping (0.30 ± 0.09) as per night of spotlighting (0.29 ± 0.11). On average, mammals were highly detectable using motion-sensor cameras for a week (0.36 ± 0.06), with exception of smaller-bodied species. One night of Elliott trapping (0.20 ± 0.06) and spotlighting (0.19 ± 0.06) was more effective at detecting mammals than cage (0.08 ± 0.03) and pit trapping (0.05 ± 0.04). Our estimates of species occupancy and detectability will help inform decisions about how best to redesign a long-running vertebrate monitoring program in the Top End of northern Australia.

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.

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.

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.

The sensitivity of population viability analysis to uncertainty about habitat requirements: implications for the management of the endangered southern brown bandicoot.

Whenever population viability analysis (PVA) models are built to help guide decisions about the management of rare and threatened species, an important component of model building is the specification of a habitat model describing how a species is related to landscape or bioclimatic variables. Model-selection uncertainty may arise because there is often a great deal of ambiguity about which habitat model structure best approximates the true underlying biological processes. The standard approach to incorporate habitat models into PVA is to assume the best habitat model is correct, ignoring habitat-model uncertainty and alternative model structures that may lead to quantitatively different conclusions and management recommendations. Here we provide the first detailed examination of the influence of habitat-model uncertainty on the ranking of management scenarios from a PVA model. We evaluated and ranked 6 management scenarios for the endangered southern brown bandicoot (Isoodon obesulus) with PVA models, each derived from plausible competing habitat models developed with logistic regression. The ranking of management scenarios was sensitive to the choice of the habitat model used in PVA predictions. Our results demonstrate the need to incorporate methods into PVA that better account for model uncertainty and highlight the sensitivity of PVA to decisions made during model building. We recommend that researchers search for and consider a range of habitat models when undertaking model-based decision making and suggest that routine sensitivity analyses should be expanded to include an analysis of the impact of habitat-model uncertainty and assumptions.