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USGS2006

Effect of dispersal at range edges on the structure of species ranges

Range edges are of particular interest to ecology because they hold key insights into the limits of the realized niche and associated population dynamics. A recent feature of Oikos summarized the state of the art on range edge ecology. While the typical question is what causes range edges, another important question is how range edges influence the distribution of abundances across a species geographic range when dispersal is present. We used a single species population dynamics model on a coupled-lattice to determine the effects of dispersal on peripheral populations as compared to populations at the core of the range. In the absence of resource gradients, the reduced neighborhood and thus lower connectivity or higher isolation among populations at the range edge alone led to significantly lower population sizes in the periphery of the range than in the core. Lower population sizes mean higher extinction risks and lower adaptability at the range edge, which could inhibit or slow range expansions, and thus effectively stabilize range edges. The strength of this effect depended on the potential population growth rate and the maximum dispersal distance. Lower potential population growth rates led to a stronger effect of dispersal resulting in a higher difference in population sizes between the two areas. The differential effect of dispersal on population sizes at the core and periphery of the range in the absence of resource gradients implies that traditional, habitat-based distribution models could result in misleading conclusions about the habitat quality in the periphery. Lower population sizes at the periphery are also relevant to conservation, because habitat removal not only eliminates populations but also creates new edges. Populations bordering these new edges may experience declines, due to their increased isolation. ?? OIKOS.

Oikos↗

USGS2022

Coupling validation effort with in situ bioacoustic data improves estimating relative activity and occupancy for multiple species with cross-species misclassifications

The increasing complexity and pace of ecological change requires natural resource managers to consider entire species assemblages. Acoustic recording units (ARUs) require minimal cost and effort to deploy and inform relative activity, or encounter rates, for multiple species simultaneously. ARU-based surveys require post-processing of the recordings via software algorithms that assign a species label to each recording. The automated classification process can result in cross-species misidentifications that should be accounted for when employing statistical modelling for conservation decision-making. Using simulation and ARU-based detection counts from 17 bat species in British Columbia, Canada, we investigate three strategies for adjusting statistical inference for species misclassification: (a) ‘coupling’ ambiguous and unambiguous detections by validating a subset of survey events post-hoc, (b) using a calibration dataset on the software algorithm's (in)accuracy for species identification or (c) specifying informative Bayesian priors on classification probabilities. We explore the impact of different Bayesian prior specifications for the classification probabilities on posterior estimation. We then consider how the quantity of data validated post-hoc impacts model convergence and resulting inferences for bat species relative activity as related to nightly conditions and yearly site occupancy after accounting for site-level environmental variables. Coupled methods resulted in less bias and uncertainty when estimating relative activity and species classification probabilities relative to calibration approaches. We found that species that were difficult-to-detect and those that were often inaccurately identified by the software required more validation effort than more easily detected and/or identified species. Our results suggest that, when possible, acoustic surveys should rely on coupled validated detection information to account for false-positive detections, rather than uncoupled calibration datasets. However, if the assemblage of interest contains a large number of rarely detected or less prevalent species, an intractable amount of effort may be required, suggesting there are benefits to curating a calibration dataset that is representative of the observation process. Our findings provide insights into the practical challenges associated with statistical analyses of ARU data and possible analytical solutions to support reliable and cost-effective decision-making for wildlife conservation/management in the face of known sources of observation errors.

British Columbia↗

USGS2024

Integrating presence-only and detection/non-detection data to estimate distributions and expected abundance of difficult-to-monitor species on a landscape-scale

Estimating species distribution and abundance is foundational to effective management and conservation. Using an integrated species distribution model that combines presence-only data from various sources with detection/non-detection data from structured surveys, we estimated the distribution and expected abundance of three difficult-to-monitor mammals of management concern across New York State, namely, coyotes ( Canis latrans ), bobcats ( Lynx rufus ) and black bears ( Ursus americanus ). Three distinct landscape-scale camera trap surveys provided detection/non-detection data over 9 years between 2013 and 2021, and we augmented those data with incidental records of our focal species from public repositories. We used an inhomogeneous Poisson point process to construct an integrated model that fit both data types simultaneously. We demonstrate a simple application of spatial point density of all species records in the accessed public databases to inform the thinning process to account for unknown spatial sampling in the presence-only data, often referred to as the ‘magic covariate’. Using this approach, we examine habitat associations and provide spatially explicit estimates in expected abundance across the entirety of New York State for all three focal species. As expected, coyotes were the most widely distributed and abundant species, with a strong positive association with agricultural land uses. Bobcats exhibited low expected abundance throughout the state and showed positive associations with deciduous forest and forest edge, and a negative association with road density. Finally, we observed considerable spatial variation in abundance of black bears with expected abundance increasing in association with various forest cover and composition covariates and decreasing with crop cover. We present insights into habitat associations and spatial variation in abundance, and provide management implications for each of the species of interest. Synthesis and applications . Our integrated modelling method allows for managers to use citizen sightings combined with detection/non-detection surveys to estimate robust indices of abundance for both high- and low-density, and wide-spread versus patchily distributed species. Through comparison with previous studies, we highlight how broad-scale programmes, such as the statewide efforts to estimate species distributions undertaken here, can benefit substantively from integrated models that leverage additional data (here, incidental records) from a larger region of space, and thus capture more landscape heterogeneity than is plausible within formalized surveys alone.

New York↗

USGS2011

An overview of methods for developing bioenergetic and life history models for rare and endangered species

Many fish species are at risk to some degree, and conservation efforts are planned or underway to preserve sensitive populations. For many imperiled species, models could serve as useful tools for researchers and managers as they seek to understand individual growth, quantify predator-prey dynamics, and identify critical sources of mortality. Development and application of models for rare species however, has been constrained by small population sizes, difficulty in obtaining sampling permits, limited opportunities for funding, and regulations on how endangered species can be used in laboratory studies. Bioenergetic and life history models should help with endangered species-recovery planning since these types of models have been used successfully in the last 25 years to address management problems for many commercially and recreationally important fish species. In this paper we discuss five approaches to developing models and parameters for rare species. Borrowing model functions and parameters from related species is simple, but uncorroborated results can be misleading. Directly estimating parameters with laboratory studies may be possible for rare species that have locally abundant populations. Monte Carlo filtering can be used to estimate several parameters by means of performing simple laboratory growth experiments to first determine test criteria. Pattern-oriented modeling (POM) is a new and developing field of research that uses field-observed patterns to build, test, and parameterize models. Models developed using the POM approach are closely linked to field data, produce testable hypotheses, and require a close working relationship between modelers and empiricists. Artificial evolution in individual-based models can be used to gain insight into adaptive behaviors for poorly understood species and thus can fill in knowledge gaps. ?? Copyright by the American Fisheries Society 2008.

Transactions of the American Fisheries Society↗

USGS2023

A hierarchical modelling framework for estimating individual- and population-level reproductive success from movement data

Rapidly advancing animal telemetry technologies paired with new statistical models can provide insight into the behaviour of otherwise unobservable free-living animals. Changes in behaviour apparent from pairing telemetry with statistical models often occur as animals undertake key life-history activities, such as reproduction. For many species that are secretive or occupy remote areas, these life-history events are difficult to detect with conventional survey techniques, and consequently, vital rates are difficult to estimate. We present a hierarchical modelling framework, which integrates movement data observed via animal-borne telemetry and optional, infrequent survey data, to estimate individual- and population-level reproductive success. The approach combines a mechanistic movement model and survival model, and allows for assessing the effects of hypothesized individual and environmental covariates on reproductive success. We first tested our approach with simulated data, and then applied it to movement data from migratory golden eagles ( Aquila chrysaetos) breeding in southcentral Alaska across four breeding seasons. We show that results supported our biological hypotheses that changes in movement coincided with the timing of reproductive failures, and that changes in movement could be used to assess breeding success (and failure) at the individual and population levels. The analysis also provided evidence of inter-annual variation in population-level nest success and the timing of nesting failures. This new approach is adaptable to many species that care for young and can be tracked with telemetry devices, and can provide not only individual-level information useful for testing ecological hypotheses, but estimates of demographic parameters that can directly inform conservation and management if tagged animals are representative of the population.

Methods in Ecology and Evolution↗

USGS2016

Development of habitat suitability indices for the Candy Darter, with cross-scale validation across representative populations

Understanding relationships between habitat associations for individuals and habitat factors that limit populations is a primary challenge for managers of stream fishes. Although habitat use by individuals can provide insight into the adaptive significance of selected microhabitats, not all habitat parameters will be significant at the population level, particularly when distributional patterns partially result from habitat degradation. We used underwater observation to quantify microhabitat selection by an imperiled stream fish, the Candy Darter Etheostoma osburni , in two streams with robust populations. We developed multiple-variable and multiple-life-stage habitat suitability indices (HSIs) from microhabitat selection patterns and used them to assess the suitability of available habitat in streams where Candy Darter populations were extirpated, localized, or robust. Next, we used a comparative framework to examine relationships among (1) habitat availability across streams, (2) projected habitat suitability of each stream, and (3) a rank for the likely long-term viability (robustness) of the population inhabiting each stream. Habitat selection was characterized by ontogenetic shifts from the low-velocity, slightly embedded areas used by age-0 Candy Darters to the swift, shallow areas with little fine sediment and complex substrate, which were used by adults. Overall, HSIs were strongly correlated with population rank. However, we observed weak or inverse relationships between predicted individual habitat suitability and population robustness for multiple life stages and variables. The results demonstrated that microhabitat selection by individuals does not always reflect population robustness, particularly when based on a single life stage or season, which highlights the risk of generalizing habitat selection that is observed during nonstressful periods or for noncritical resources. These findings suggest that stream fish managers may need to be cautious when implementing conservation measures based solely on observations of habitat selection by individuals and that detailed study at the individual and population levels may be necessary to identify habitat that limits populations.

Transactions of the American Fisheries Society↗

USGS2024

Silver Chub spawning confirmed in the Maumee River, a tributary of Lake Erie

Objective Biodiversity is declining due to invasive species and other factors that can affect individual species differently. Silver Chub Macrhybopsis storeriana are declining in their native range, and their conservation status in the Great Lakes ranges from secure to possibly extirpated. Lake Erie once supported a large Silver Chub population until it crashed in the 1950s. Additionally, the spawning behavior and reproductive guild of Silver Chub in Lake Erie is unknown. Our objective was to document Silver Chub spawning in the Maumee River, a Lake Erie tributary. Methods Invasive Grass Carp Ctenopharyngodon idella are known to spawn in the Maumee River during high-flow events from May to July, and the University of Toledo and U.S. Geological Survey regularly sample the lower 24 km for early life stages using paired bongo nets. Contents from paired bongo nets are returned to the laboratory for processing, and a subset of potential Grass Carp eggs are sent for genetic analysis. Result On June 8, 2022, several potential Grass Carp eggs were captured at two sites on the Maumee River during a high-flow event. Fifteen potential Grass Carp eggs were sent for genetic analysis, and DNA sequencing revealed that six of these eggs were Silver Chub. Discussion This was the first known collection of Silver Chub eggs in a Lake Erie tributary, and our findings indicate that Silver Chub likely belong to the pelagophil reproductive guild. Although Grass Carp and Silver Chub spawn under similar conditions, management actions to control Grass Carp in the Maumee River may be unlikely to affect Silver Chub due to electrofishing settings used in the capture of Grass Carp. The verification of Silver Chub spawning in a Western Erie Basin tributary provides insights into their reproductive biology that could be useful in recovery planning in Lake Erie and throughout the Great Lakes.

Ohio↗

USGS2013

Projecting demographic responses to climate change: adult and juvenile survival respond differently to direct and indirect effects of weather in a passerine population

Few studies have quantitatively projected changes in demography in response to climate change, yet doing so can provide important insights into the processes that may lead to population declines and changes in species distributions. Using a long-term mark-recapture data set, we examined the influence of multiple direct and indirect effects of weather on adult and juvenile survival for a population of Song Sparrows (Melospiza melodia) in California. We found evidence for a positive, direct effect of winter temperature on adult survival, and a positive, indirect effect of prior rainy season precipitation on juvenile survival, which was consistent with an effect of precipitation on food availability during the breeding season. We used these relationships, and climate projections of significantly warmer and slightly drier winter weather by the year 2100, to project a significant increase in mean adult survival (12-17%) and a slight decrease in mean juvenile survival (4-6%) under the B1 and A2 climate change scenarios. Together with results from previous studies on seasonal fecundity and postfledging survival in this population, we integrated these results in a population model and projected increases in the population growth rate under both climate change scenarios. Our results underscore the importance of considering multiple, direct, and indirect effects of weather throughout the annual cycle, as well as differences in the responses of each life stage to climate change. Projecting demographic responses to climate change can identify not only how populations will be affected by climate change but also indicate the demographic process(es) and specific mechanisms that may be responsible. This information can, in turn, inform climate change adaptation plans, help prioritize future research, and identify where limited conservation resources will be most effectively and efficiently spent.

California↗

USGS2023

Interactions among rainfall, fire, forbs and non-native grasses predict occupancy dynamics for the endangered Pacific pocket mouse (Perognathus longimembris pacificus) in a Mediterranean-type ecosystem

It is important to understand species-habitat relationships to implement effective adaptive management for rare species. However, it can be challenging to assess habitat associations and their relationships to abiotic stressors in dynamic habitats without the insights that can be gained from long-term monitoring. We report results from the first six years of extensive track tube monitoring of the largest two of three remaining extant populations of federally endangered Pacific pocket mouse ( Perognathus longimembris pacificus ) in a coastal Mediterranean-type ecosystem on Marine Corps Base, Camp Pendleton in southern California, USA. We used dynamic occupancy and structural equation modeling to assess potential drivers of population trends that included habitat, fire history, rainfall, disturbance, and the presence of other small mammals. We found that the variables that best predicted mouse occupancy were moderate to high forb and perennial herb cover (40–80%), and moderate to high open ground (20–70%) and low non-native grass cover (<20%), Non-native grass cover (>20%) was also a strong predictor of lower PPM colonization and increased extinction probabilities, with the extent of non-native grass cover being strongly influenced by annual rainfall and recency of fire. Our study adds to the growing literature on effects of invasive annual grasses on native species in Mediterranean-type ecosystems. We suggest that habitat management could be based upon promotion of open forb and perennial herb dominated habitats with reduction of non-native grasses by prescribed fire and other methods. These types of spatial and temporal monitoring programs can support land managers by creating a monitoring and management feedback loop. They can reveal landscape and environmental variables associated with species persistence, inform habitat management goals, and help managers to assess the success of management actions on populations of conservation concern.

California↗

USGS2023

Factors influencing autumn–winter movements of midcontinent Mallards and consequences for harvest and habitat management

Annual phenology and distributions of migratory wildlife have been noticeably influenced by climate change, leading to concerns about sustainable populations. Recent studies exploring conditions influencing autumn migration departure have provided conflicting insights regarding factors influencing the movements of Mallards ( Anas platyrhynchos ), a popular game species. We determined factors affecting timing and magnitude of long-distance movements of 97 juvenile Mallards during autumn-winter across the midcontinent of North America marked with implanted transmitters in North and South Dakota, 2018–2019. Factors influencing variation in movement timing, along with direction and magnitudes, depended on type of movement (i.e., regional [25–310 km], initial migration, or subsequent migration movements [>310 km]). Photoperiod influenced probability of initiating all movements, although the effect was most influential for regional movements. Minimum temperature most influenced initial migration events (probability of movement increased 29% for each 1°C decrease); favorable winds also increased likelihood of initial migration events. Probability of subsequent migration events increased 80% for each 1 cm increase in depth of snow. Subsequent migration movements also were 2.0 times more likely to occur on weekend days, indicating disturbance from humans may influence movements. Migration distances increased 166 km for each 1°C reduction in minimum temperature. We also observed markedly different autumn-winter distributions of marked birds between years. Median locations during autumn-winter 2018–2019 were ~250 km farther north and ~300 km farther west during mid-December–January compared to the same time in 2019–2020. Concurrently, harvest rates for marked females and males were 10% and 26% during autumn-winter 2018–2019 and 26% and 31% during autumn-winter 2019–2020. Climate-related changes may result in increasingly variable autumn-winter distributions, with implications for wildlife recreationalists, conservation planners, and harvest managers.

Ecology and Evolution↗

USGS2020

Incorporating spatial synchrony in the status assessment of a threatened species with multivariate analysis

Spatial synchrony—correlated abundance fluctuations among distinct populations—is associated with increased extinction risk but is not a component of widely-used extinction risk assessments (e.g., IUCN Red List, U.S. Fish and Wildlife Service’s Species Status Assessment). Alongside traditional viability metrics (i.e., the number of populations, their spatial extent, the status of each population), consideration of spatial synchrony in these assessments may provide additional insight into extinction risk as well as the relative importance of intrinsic and extrinsic factors on population dynamics. We demonstrate a method for estimating abundance trends in populations of the endangered freshwater fish, the amber darter (Percina antesella), while simultaneously assessing support for spatial synchrony among its two populations in the Conasauga and Etowah rivers in Georgia, U.S.A. Our analysis was performed using multivariate autoregressive state-space (MARSS) models with annual sampling data from 1996-2018 at 16 sites distributed between the two populations. Our results indicate that amber darter populations have declined substantially, with 9% annual losses in both the Conasauga and Etowah rivers, suggesting rangewide imperilment. Furthermore, model selection indicated little support for models with independent dynamics between rivers, which may compound overall extinction risk. This analysis demonstrates the utility of tools such as MARSS models for assessing spatial synchrony and long-term population trajectories of imperiled species, resulting in improved vulnerability assessments that do not assume independence among separate populations.

Georgia↗

USGS2025

Fine-scale spatial risk models to predict avian collisions with power lines

1. Avian fatalities caused by collisions with overhead power lines are an important conservation issue worldwide. Although mitigation strategies can help reduce mortalities, given their considerable cost and the vast scale of power line infrastructure, cost-effective action requires that these efforts be prioritised to areas with the highest potential risk to birds. To date, this risk assessment has usually been guided by potentially biased information on the location of recorded fatalities. 2. Here we use five years of GPS tracking data from endangered Tasmanian wedge-tailed eagles to develop an alternative approach to risk assessment: fine-scale spatial risk models based on behavioural analyses. We built and cross-validated a model that generates spatially explicit predictions of the probability that eagles would cross power lines at hazardous altitudes throughout the entire Tasmanian electricity distribution network. 3. In our model, probability of power line crossings was most strongly associated with the proportion of forest edges, wet forest, open habitat, freshwater sources, and rural residential developments in the area surrounding the power lines. Cross-validation indicated that the model effectively predicted where Tasmanian wedge-tailed eagles cross power lines at low altitude. 4. Model validation suggested our approach was a powerful predictor of the locations of power line collisions involving eagles. The locations of almost all (94%) confirmed eagle fatalities were in the half of the total Tasmanian power line area assigned the higher risk by the model, and 50% of incidents occurred in the 30% of the power line area estimated to be highest risk. 5. Synthesis and applications . Our study illustrates a framework for using bird movement data to provide insights into avian behaviour and the risk they encounter around power line infrastructure. Electricity delivery industries can use these models to identify the electrical infrastructure that poses the highest risk to avian survival and prioritise mitigation efforts, thereby optimizing the benefit of investments to reduce detrimental effects on biodiversity. Our model can direct pre-emptive mitigation across Tasmania’s 20,310 km of distribution infrastructure to meet management targets aiming to reduce the negative effects of power lines on the Tasmanian wedge-tailed eagle.

Tasmania↗

USGS2024

The interplay of future solar energy, land cover change, and their projected impacts on natural lands and croplands in the US

Projections for deep decarbonization require large amounts of solar energy, which may compete with other land uses such as agriculture, urbanization, and conservation of natural lands. Existing capacity expansion models do not integrate land use land cover change (LULC) dynamics into projections. We explored the interaction between projected LULC, solar photovoltaic (PV) deployment, and solar impacts on natural lands and croplands by integrating projections of LULC with a model that can project future deployment of solar PV with high spatial resolution for the conterminous United States. We used scenarios of LULC projections from the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios from 2010 to 2050 and two electricity grid scenarios to model future PV deployment and compared those results against a baseline that held 2010 land cover constant through 2050. Though solar PV's overall technical potential was minimally impacted by LULC scenarios, deployed PV varied by −16.5 to 11.6 % in 2050 from the baseline scenario. Total land requirements for projected PV were similar to other studies, but measures of PV impacts on natural systems depended on the underlying land change dynamics occurring in a scenario. The solar PV deployed through 2050 resulted in 1.1 %–2.4 % of croplands and 0.3 %–0.7 % of natural lands being converted to PV. However, the deepest understanding of PV impacts and interactions with land cover emerged when the complete net gains and losses from all land cover change dynamics, including PV, were integrated. For example, one of the four LULC projections allows for high solar development and a net gain in natural lands, even though PV drives a larger percentage of natural land conversion. This paper shows that integrating land cover change dynamics with energy expansion models generates new insights into trade offs between decarbonization, impacts of renewables, and ongoing land cover change.

conterminous United States↗

USGS2022

Evaluating the effect of expert elicitation techniques on population status assessment in the face of large uncertainty

Population projection models are important tools for conservation and management. They are often used for population status assessments, for threat analyses, and to predict the consequences of conservation actions. Although conservation decisions should be informed by science, critical decisions are often made with very little information to support decision-making. Conversely, postponing decisions until better information is available may reduce the benefit of a conservation decision. When empirical data are limited or lacking, expert elicitation can be used to supplement existing data and inform model parameter estimates. The use of rigorous techniques for expert elicitation that account for uncertainty can improve the quality of the expert elicited values and therefore the accuracy of the projection models. One recurring challenge for summarizing expert elicited values is how to aggregate them. Here, we illustrate a process for population status assessment using a combination of expert elicitation and data from the ecological literature. We discuss the importance of considering various aggregation techniques, and illustrate this process using matrix population models for the wood turtle ( Glyptemys insculpta ) to assist U.S. Fish and Wildlife Service decision-makers with their Species Status Assessment. We compare estimates of population growth using data from the ecological literature and four alternative aggregation techniques for the expert-elicited values. The estimate of population growth rate based on estimates from the literature (λ mean = 0.952, 95% CI: 0.87–1.01) could not be used to unequivocally reject the hypotheses of a rapidly declining population nor the hypothesis of a stable, or even slightly growing population, whereas our results for the expert-elicited estimates supported the hypothesis that the wood turtle population will decline over time. Our results showed that the aggregation techniques used had an impact on model estimates, suggesting that the choice of techniques should be carefully considered. We discuss the benefits and limitations associated with each method and their relevance to the population status assessment. We note a difference in the temporal scope or inference between the literature-based estimates that provided insights about historical changes, whereas the expert-based estimates were forward looking. Therefore, conducting an expert-elicitation in addition to using parameter estimates from the literature improved our understanding of our species of interest.

Journal of Environmental Management↗

USGS2022

A statistical framework for modelling migration corridors

1. Management of animal populations requires spatially explicit knowledge of movement corridors, such as those used during seasonal migrations. GPS tracking data allows for mapping of corridors from directly observed movements, providing important insights, but tracking data is absent for many populations. 2. We developed a novel statistical corridor modeling approach that predicts movement corridors from cost-distance movement models fit directly to migration tracking data. Unlike existing predictive approaches, this does not require the ad-hoc transformation of habitat suitability surfaces into resistance surfaces. We tested the ability of the approach to recover parameters used to generate simulated movements. We then used GPS data from three migrating mule deer (Odocoileus hemionus) herds in Idaho and Wyoming to model corridors as a function of elevation, slope, aspect, percent shrub, date of peak green-up, snow-off date, and human footprint. We assessed the predictive ability of the fitted models using validation tracks from the same herd as well as from the other herds. 3. The approach reproduced parameters used to generate the simulated movements, predicted the corridors used by migratory populations, and described the direction, magnitude, and confidence levels of the effects of environmental variables on corridors. The effects environmental variables had on corridors differed depending on the herd. Within-herd validation indicated that fitted corridor models are more accurate at predicting migration corridors than null models, and cross-herd validation indicated that fitted models for some herds accurately predicted the observed migrations of other herds. 4. In addition to the practical benefit of mapping corridors for management, our statistical corridor modeling framework sets the stage for evaluating fundamental questions about the fitness tradeoffs, navigation, learning, fidelity, and movement constraints that influence migratory and other corridor-generating behavior. Models of predictive corridors can inform management and planning for the conservation of migrations across taxa, including the potential restoration of corridors. Our corridor modeling approach is also readily applied to non-migratory animal movements.

Idaho, Wyoming↗

USGS2021

Across borders: External factors and prior behaviour influence North Pacific albatross associations with fishing vessels

Understanding encounters between marine predators and fisheries across national borders and outside national jurisdictions offers new perspectives on unwanted interactions to inform ocean management and predator conservation. Although seabird–fisheries overlap has been documented at many scales, remote identification of vessel encounters has lagged because vessel movement data often are lacking. Here, we reveal albatrosses–fisheries associations throughout the North Pacific Ocean. We identified commercial fishing operations using Global Fishing Watch data and algorithms to detect fishing vessels. We compiled GPS tracks of adult black-footed Phoebastria nigripes and Laysan Phoebastria immutabilis albatrosses, and juvenile short-tailed albatrosses Phoebastria albatrus . We quantified albatrosses-vessel encounters based on the assumed distance that birds perceive a vessel (≤30 km), and associations when birds approached vessels (≤3 km). For each event we quantified bird behaviour, environmental conditions and vessel characteristics and then applied Boosted Regression Tree models to identify drivers and the duration of these associations. In regions of greater fishing effort short-tailed and Laysan albatrosses associated with fishing vessels more frequently. However, fishing method (e.g. longline, trawl) and flag nation did not influence association prevalence nor the duration short-tailed albatrosses attended fishing vessels. Laysan albatrosses were more likely to approach longer vessels. Black-footed albatrosses were the most likely to approach vessels (61.9%), but limited vessel encounters ( n = 21) prevented evaluation of meaningful explanatory models for this species of high bycatch concern. Temporal variables (time of day and month) and bird behavioural state helped explain when short-tailed albatrosses were in close proximity to a vessel, but environmental conditions were more important for explaining interaction duration. Laysan albatrosses were more likely to associate with vessels while searching and during the last 60% (by time) of their trips. Our results provide specific species–fisheries insight regarding contributing factors of high-risk associations that could lead to bycatch of albatrosses within national waters and on the high seas. Policy implications . Given the availability of Global Fishing Watch data, our analysis can be applied to other marine predators—if tracking data are available—to identify spatio-temporal patterns, vessel specific attributes and predator behaviours associated with fishing vessel associations, thus enabling predictive modelling and targeted mitigation measures.

Journal of Applied Ecology↗

USGS2017

Are ranger patrols effective in reducing poaching-related threats within protected areas?

Poaching is one of the greatest threats to wildlife conservation world-wide. However, the spatial and temporal patterns of poaching activities within protected areas, and the effectiveness of ranger patrols and ranger posts in mitigating these threats, are relatively unknown. We used 10 years (2006–2015) of ranger-based monitoring data and dynamic multi-season occupancy models to quantify poaching-related threats, to examine factors influencing the spatio-temporal dynamics of these threats and to test the efficiency of management actions to combat poaching in Nyungwe National Park (NNP), Rwanda. The probability of occurrence of poaching-related threats was highest at lower elevations (1,801–2,200 m), especially in areas that were close to roads and tourist trails; conversely, occurrence probability was lowest at high elevation sites (2,601–3,000 m), and near the park boundary and ranger posts. The number of ranger patrols substantially increased the probability that poaching-related threats disappear at a site if threats were originally present (i.e. probability of extinction of threats). Without ranger visits, the annual probability of extinction of poaching-related threats was an estimated 7%; this probability would increase to 20% and 57% with 20 and 50 ranger visits per year, respectively. Our results suggest that poaching-related threats can be effectively reduced in NNP by adding ranger posts in areas where they do not currently exist, and by increasing the number of patrols to sites where the probability of poaching activities is high. Synthesis and applications . Our application of dynamic occupancy models to predict the probability of presence of poaching-related threats is novel, and explicitly considers imperfect detection of illegal activities. Based on the modelled relationships, we identify areas that are most vulnerable to poaching, and offer insights regarding how ranger patrols can be optimally deployed to reduce poaching-related threats and other illegal activites, while taking into account potential sampling biases. We show that poaching can be effectively reduced by increasing ranger patrols to areas under high risk of poaching activities, and by adding ranger patrols near these sites. These findings are broadly applicable to national parks and protected areas experiencing a high degree of poaching and other illegal activities.

Nyungwe National Park↗

USGS2017

Building capacity in biodiversity monitoring at the global scale

Human-driven global change is causing ongoing declines in biodiversity worldwide. In order to address these declines, decision-makers need accurate assessments of the status of and pressures on biodiversity. However, these are heavily constrained by incomplete and uneven spatial, temporal and taxonomic coverage. For instance, data from regions such as Europe and North America are currently used overwhelmingly for large-scale biodiversity assessments due to lesser availability of suitable data from other, more biodiversity-rich, regions. These data-poor regions are often those experiencing the strongest threats to biodiversity, however. There is therefore an urgent need to fill the existing gaps in global biodiversity monitoring. Here, we review current knowledge on best practice in capacity building for biodiversity monitoring and provide an overview of existing means to improve biodiversity data collection considering the different types of biodiversity monitoring data. Our review comprises insights from work in Africa, South America, Polar Regions and Europe; in government-funded, volunteer and citizen-based monitoring in terrestrial, freshwater and marine ecosystems. The key steps to effectively building capacity in biodiversity monitoring are: identifying monitoring questions and aims; identifying the key components, functions, and processes to monitor; identifying the most suitable monitoring methods for these elements, carrying out monitoring activities; managing the resultant data; and interpreting monitoring data. Additionally, biodiversity monitoring should use multiple approaches including extensive and intensive monitoring through volunteers and professional scientists but also harnessing new technologies. Finally, we call on the scientific community to share biodiversity monitoring data, knowledge and tools to ensure the accessibility, interoperability, and reporting of biodiversity data at a global scale.

Biodiversity and Conservation↗