Searching for weeds – how to search where the weeds are?

Sampling for rare events, such as a new weed incursion, is not easy. At most of the sample points the species of interest is absent and only occasionally the species is recorded. Very often surveillance and monitoring for rare events (e.g. new weed incursions) is done using local knowledge and a statistical sampling design is not used. The stated reason for this approach is usually because the biodiversity managers knew where to look and didn’t need statistics. Adaptive, unequal probability survey designs can be used in these situations, ensuring both sample effort is focused on locations where there is a high likelihood of a weed being present. Time in the field is spent within locations where weeds are present and minimal time spent where weeds are absent. Any relevant information on where weeds are likely to be found (e.g. local knowledge and expertise) can be used to target survey effort in unequal probability survey designs. The advantage of an adaptive, unequal probability survey design is that not only can field effort be focused on areas where the weeds are thought to be. In addition, important weed parameters can be estimated and reported along with estimates of uncertainty. Weed parameters include the proportion of the total area that weeds are present, the diversity of weed species, the total abundance of weeds, and the total area covered by weeds. With reliable and consistent estimates of these weed parameters (e.g. weed cover or abundance) the efficacy of weed management can be tracked. Over time, with regular reporting of weed cover or abundance, the success (or otherwise) of weed management strategies can be measured.     

An improved survey method for monitoring population trends of Golden‐winged Warblers and other patchily distributed birds

Conventional surveys designed to monitor common and widespread species may fail to adequately track population changes of rare or patchily distributed species that are often of high conservation concern. We evaluated the performance of a new monitoring approach that employs both a spatially balanced sampling design and a targeted survey protocol designed to estimate population trends of one such patchily distributed species, the Golden‐winged Warbler (Vermivora chrysoptera), in the Appalachian Mountains Bird Conservation Region (BCR 28), USA. Our spatially balanced survey consisted of 105 sample quads (one‐quarter Delorme Atlas pages) across the current range of Golden‐winged Warblers within BCR 28, each with five sample points located in early successional habitat. From 2009 to 2013, collaborators visited each sample point once per year during the peak breeding season and conducted a 17‐min survey consisting of passive observation and playback of conspecific songs and mobbing vocalizations. We used multi‐season, single‐species occupancy models to estimate probability of quad occupancy, detection probability, and occupancy dynamics for Golden‐winged Warblers and closely related Blue‐winged Warblers (Vermivora cyanoptera). Our survey protocol resulted in high estimates of detection probability for Golden‐winged (92%) and Blue‐winged (79%) warblers, with 47% and 56% of quads estimated to be initially occupied, respectively. Derived population trend estimates (λ) indicated an average decline in population of 6% for Golden‐winged Warblers and 7% for Blue‐winged Warblers, resulting in estimated 21% and 22% declines, respectively, in quad occupancy after 5 yr. Our results demonstrate that coupling a spatially balanced survey design in appropriate habitat with a playback protocol to increase detection rates is a viable strategy for tracking populations of Golden‐winged Warblers in the Appalachian Mountains BCR. Similar survey methods should be considered for other rare, declining, or patchily distributed bird species that require targeted monitoring.     

Limited Effects of Large‐Scale Riparian Restoration on Seed Banks in Agriculture

I examined the effect of riparian forest restoration on plant abundance and diversity, including weed species, on agricultural lands along the Sacramento River in California (United States). Riparian forest restoration on the Sacramento River is occurring on a large‐scale, with a goal of restoring approximately 80,000 ha over 160 km of the river. In multiuse habitats, such as the Sacramento River, effects of adjoining habitat types and movement of species across these habitats can have important management implications in terms of landscape‐scale patterns of species distributions. Increased numbers of pest animals and weeds on agricultural lands associated with restored habitats could have negative economic impacts, and in turn affect support for restoration of natural areas. In order to determine the distribution and abundance of weeds associated with large‐scale restoration, I collected seed bank soil samples on orchards between 0 and 5.6 km from adjacent restored riparian, remnant riparian, and agricultural habitats. I determined the abundance, species richness, and dispersal mode of plant species in the seed bank and analyzed these variables in terms of adjacent habitat type and age of restored habitat. I found that agricultural weed species had higher densities at the edge of restored riparian habitat and that native plants had higher densities adjacent to remnant riparian habitat. Weed seed abundance increased significantly on walnut farms adjacent to restored habitat with time since restored. I supply strong empirical evidence that large areas of natural and restored habitats do not lead to a greater penetration of weed species into agricultural areas, but rather that weed penetration is both temporally and spatially limited.     

A practical sampling design for acoustic surveys of bats

Acoustic surveys are widely used for describing bat occurrence and activity patterns and are increasingly important for addressing concerns for habitat management, wind energy, and disease on bat populations. Designing these surveys presents unique challenges, particularly when a probabilistic sample is required for drawing inference to unsampled areas. Sampling frame errors and other logistical constraints often require survey sites to be dropped from the sample and new sites added. Maintaining spatial balance and representativeness of the sample when these changes are made can be problematic. Spatially balanced sampling designs recently developed to support aquatic surveys along rivers provide solutions to a number of practical challenges faced by bat researchers and allow for sample site additions and deletions, support unequal-probability selection of sites, and provide an approximately unbiased local neighborhood-weighted variance estimator that is efficient for spatially structured populations such as is typical for bats. We implemented a spatially balanced design to survey canyon bat (Parastrellus hesperus) activity along a stream network. The spatially balanced design accommodated typical logistical challenges and yielded a 25% smaller estimated standard error for the mean activity level than the usual simple random sampling estimator. Spatially balanced designs have broad application to bat research and monitoring programs and will improve studies relying on model-based inference (e.g., occupancy models) by providing flexibility and protection against violations of the independence assumption, even if design-based estimators are not used. Our approach is scalable and can be used for pre- and post-construction surveys along wind turbine arrays and for regional monitoring programs. © 2011 The Wildlife Society.     

Intra-specific niche partitioning obscures the importance of fine-scale habitat data in species distribution models

Geographic information systems (GIS) allow researchers to make cost-effective, spatially explicit predictions of species’ distributions across broad geographic areas. However, there has been little research on whether using fine-scale habitat data collected in the field could produce more robust models of species’ distributions. Here we used radio-telemetry data collected on a declining species, the North American wood turtle (Glyptemys insculpta), to test whether fine-scale habitat variables were better predictors of occurrence than land-cover and topography variables measured in a GIS. Patterns of male and female occurrence were similar in the spring; however, females used a much wider array of land-cover types and topographic positions in the summer and early fall, making it difficult for GIS-based models to accurately predict female occurrence at this time of year. Males on the other hand consistently selected flat, low-elevation, riparian areas throughout the year, and this consistency in turn led to the development of a strong GIS-based model. These results demonstrate the importance of taking a more sex-specific and temporally dynamic view of the environmental niche.     

Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition

The availability of suitable habitat is a key predictor of the changing status of biodiversity. Quantifying habitat availability over large spatial scales is, however, challenging. Although remote sensing techniques have high spatial coverage, there is uncertainty associated with these estimates due to errors in classification. Alternatively, the extent of habitats can be estimated from ground‐based field survey. Financial and logistical constraints mean that on‐the‐ground surveys have much lower coverage, but they can produce much higher quality estimates of habitat extent in the areas that are surveyed. Here, we demonstrate a new combined model which uses both types of data to produce unified national estimates of the extent of four key habitats across Great Britain based on Countryside Survey and Land Cover Map. This approach considers that the true proportion of habitat per km² (Z_i) is unobserved, but both ground survey and remote sensing can be used to estimate Z_i. The model allows the relationship between remote sensing data and Z_i to be spatially biased while ground survey is assumed to be unbiased. Taking a statistical model‐based approach to integrating field survey and remote sensing data allows for information on bias and precision to be captured and propagated such that estimates produced and parameters estimated are robust and interpretable. A simulation study shows that the combined model should perform best when error in the ground survey data is low. We use repeat surveys to parameterize the variance of ground survey data and demonstrate that error in this data source is small. The model produced revised national estimates of broadleaved woodland, arable land, bog, and fen, marsh and swamp extent across Britain in 2007.     

Ecospace: Prediction of Mesoscale Spatial Patterns in Trophic Relationships of Exploited Ecosystems, with Emphasis on the Impacts of Marine Protected Areas

Growing disillusion with the predictive capability of single species fisheries assessment methods and the realization that the management approaches they imply will always fail to protect bycatch species has led to growing interest in the potential of marine protected areas (MPAs) as a tool for protecting such species and allowing for rebuilding populations of target species and damaged habitat. Ecospace is a spatially explicit model for policy evaluation that allows for considering the impact of MPAs in an ecosystem (that is, trophic) context, and that relies on the Ecopath mass-balance approach for most of its parameterization. Additional inputs are movement rates used to compute exchanges between grid cells, estimates of the importance of trophic interactions (top-down vs bottom up control), and habitat preferences for each of the functional groups included in the model. An application example, including the effect of an MPA, and validation against trawl survey data is presented in the form of a color map illustrating Ecospace predictions of biomass patterns on the shelf of Brunei Darussalam, Southeast Asia. A key general prediction of Ecospace is spatial “cascade” effects, wherein prey densities are low where predators are abundant, for example, in protected areas or areas where fishing costs are high. Ecospace also shows that the potential benefits of local protection can be easily negated by high movement rates, and especially by concentration of fishing effort at the edge of the MPAs, where cascade effects generate prey gradients that attract predators out of the protected areas. Despite various limitations (for example, no explicit consideration of seasonal changes or directed migration), the outward simplicity of Ecospace and the information-rich graphs it generates, coupled with the increasingly global availability of the required Ecopath files, will likely ensure a wide use for this approach, both for generating hypotheses about ecosystem function and evaluating policy choices. Received 24 February 1999; accepted 16 June 1999.     

Assessing the impact of revegetation and weed control on urban sensitive bird species

Nature in cities is concentrated in urban green spaces, which are key areas for urban biodiversity and also important areas to connect people with nature. To conserve urban biodiversity within these natural refugia, habitat restoration such as weed control and revegetation is often implemented. These actions are expected to benefit biodiversity, although species known to be affected by urbanization may not be interacting with restoration in the ways we anticipate. In this study, we use a case study to explore how urban restoration activities impact different bird species. Birds were grouped into urban sensitivity categories and species abundance, and richness was then calculated using a hierarchical species community model for individual species responses, with “urban class” used as the hierarchical parameter. We highlight variable responses of birds to revegetation and weed control based on their level of urban sensitivity. Revegetation of open grassy areas delivers significant bird conservation outcomes, but the effects of weed control are neutral or in some cases negative. Specifically, the species most reliant on remnant vegetation in cities seem to remain stable or decline in abundance in areas with weed control, which we suspect is the result of a simplification of the understorey. The literature reports mixed benefits of weed control between taxa and between locations. We recommend, in our case study site, that weed control be implemented in concert with replanting of native vegetation to provide the understory structure preferred by urban sensitive birds. Understanding the impacts of revegetation and weed control on different bird species is important information for practitioners to make restoration decisions about the allocation of funds for conservation action. This new knowledge can be used both for threatened species and invasive species management.     

Occupancy in dynamic systems: accounting for multiple scales and false positives using environmental DNA to inform monitoring

Occupancy is an important metric to understand current and future trends in populations that have declined globally. In addition, occupancy can be an efficient tool for conducting landscape-scale and long-term monitoring. A challenge for occupancy monitoring programs is to determine the appropriate spatial scale of analysis and to obtain precise occupancy estimates for elusive species. We used a multi-scale occupancy model to assess occupancy of Columbia spotted frogs in the Great Basin, USA, based on environmental DNA (eDNA) detections. We collected three replicate eDNA samples at 220 sites across the Great Basin. We estimated and modeled ecological factors that described watershed and site occupancy at multiple spatial scales simultaneously while accounting for imperfect detection. Additionally, we conducted visual and dipnet surveys at all sites and used our paired detections to estimate the probability of a false positive detection for our eDNA sampling. We applied the estimated false positive rate to our multi-scale occupancy dataset and assessed changes in model selection. We had higher naïve occupancy estimates for eDNA (0.37) than for traditional survey methods (0.20). We estimated our false positive detection rate per qPCR replicate at 0.023 (95% CI: 0.016–0.033). When the false positive rate was applied to the multi-scale dataset, we did not observe substantial changes in model selection or parameter estimates. Conservation and resource managers have an increasing need to understand species occupancy in highly variable landscapes where the spatial distribution of habitat changes significantly over time due to climate change and human impact. A multi-scale occupancy approach can be used to obtain regional occupancy estimates that can account for spatially dynamic differences in availability over time, especially when assessing potential declines. Additionally, this study demonstrates how eDNA can be used as an effective tool for improved occupancy estimates across broad geographic scales for long-term monitoring.     

Building the Foundation for International Conservation Planning for Breeding Ducks across the U.S. and Canadian Border

We used publically available data on duck breeding distribution and recently compiled geospatial data on upland habitat and environmental conditions to develop a spatially explicit model of breeding duck populations across the entire Prairie Pothole Region (PPR). Our spatial population models were able to identify key areas for duck conservation across the PPR and predict between 62.1 – 79.1% (68.4% avg.) of the variation in duck counts by year from 2002 – 2010. The median difference in observed vs. predicted duck counts at a transect segment level was 4.6 ducks. Our models are the first seamless spatially explicit models of waterfowl abundance across the entire PPR and represent an initial step toward joint conservation planning between Prairie Pothole and Prairie Habitat Joint Ventures. Our work demonstrates that when spatial and temporal variation for highly mobile birds is incorporated into conservation planning it will likely increase the habitat area required to support defined population goals. A major goal of the current North American Waterfowl Management Plan and subsequent action plan is the linking of harvest and habitat management. We contend incorporation of spatial aspects will increase the likelihood of coherent joint harvest and habitat management decisions. Our results show at a minimum, it is possible to produce spatially explicit waterfowl abundance models that when summed across survey strata will produce similar strata level population estimates as the design-based Waterfowl Breeding Pair and Habitat Survey (r² = 0.977). This is important because these design-based population estimates are currently used to set duck harvest regulations and to set duck population and habitat goals for the North American Waterfowl Management Plan. We hope this effort generates discussion on the important linkages between spatial and temporal variation in population size, and distribution relative to habitat quantity and quality when linking habitat and population goals across this important region.     

Hierarchical multi-scale occupancy estimation for monitoring wildlife populations

Occupancy estimation is an effective analytic framework, but requires repeated surveys of a sample unit to estimate the probability of detection. Detection rates can be estimated from spatially replicated rather than temporally replicated surveys, but this may violate the closure assumption and result in biased estimates of occupancy. We present a new application of a multi-scale occupancy model that permits the simultaneous use of presence–absence data collected at 2 spatial scales and uses a removal design to estimate the probability of detection. Occupancy at the small scale corresponds to local territory occupancy, whereas occupancy at the large scale corresponds to regional occupancy of the sample units. Small-scale occupancy also corresponds to a spatial availability or coverage parameter where a species may be unavailable for sampling at a fraction of the survey stations. We applied the multi-scale occupancy model to a hierarchical sample design for 2 bird species in the Black Hills National Forest: brown creeper (Certhia americana) and lark sparrow (Chondestes grammacus). Our application of the multi-scale occupancy model is particularly well suited for hierarchical sample designs, such as spatially replicated survey stations within sample units that are typical of avian monitoring programs. The model appropriately accounts for the non-independence of the spatially replicated survey stations, addresses the closure assumption for the spatially replicated survey stations, and is useful for decomposing the observation process into detection and availability parameters. This analytic approach is likely to be useful for monitoring at local and regional scales, modeling multi-scale habitat relationships, and estimating population state variables for rare species of conservation concern. © 2011 The Wildlife Society.     

Using abundance and habitat variables to identify high conservation value areas for threatened mammals

The present study used abundance and habitat variables to design High Conservation Value Forests for wildlife protection. We considered great apes (Gorilla gorilla gorilla and Pan troglodytes troglodytes) as model species, and we used nest surveys, dietary analysis and botanical inventories to evaluate whether the traditional methods that use abundance data alone were consistent with the survival of the species. We assumed that setting a local priority area for animal conservation can be made possible if at least one variable (abundance or habitat variables) is spatially clustered and that the final decision for a species may depend on the pattern of spatial association between abundance, nesting habitat and feeding habitat. We used Kernel Density Estimation to evaluate the spatial pattern of each biological variable. The results indicate that all three variables were spatially clustered for both gorillas and chimpanzees. The abundance variables of both animal species were spatially correlated to their preferred nesting habitat variables. But while the chimpanzee feeding habitat variable was spatially correlated to the abundance and nesting habitat variables, the same pattern was not observed for gorillas. We then proposed different methods to be considered to design local priority areas for the conservation of each great ape species. Alone, the abundance variable does not successfully represent the spatial distribution of major biological requirements for the survival of wildlife species; we, therefore, recommend the integration of the spatial distribution of their food resources to overcome the mismatch caused by the existence of a biological interaction between congeneric species.     

Site and landscape features ruling the habitat use and occupancy of the polecat (Mustela putorius) in a low density area: a multiscale approach

We studied the habitat of the polecat at different scales in a low density area. For this purpose we gathered data on the presence of the species and characterised them by location, home range and landscape scales. Polecats selected areas of high diversity close to, but not in, streams whilst avoiding intensively managed conifer plantations and dense urban areas. Variables determining the presence/absence of the species were found at home range scales, which implies that management and conservation practices for the species should be aimed mainly at this scale. Finally, our results agree with previously published works, which validate GIS-based approaches as a tool for carnivore management in areas with scarce data or in cases of rare species.     

An expert-based approach to invertebrate conservation: Identification of priority areas in central-eastern Alps

The private and public agencies for nature protection often ask land managers to implement biodiversity conservation plans. Invertebrates constitute a substantial proportion of terrestrial and freshwater biodiversity and are critical to ecosystem functions. However, their inclusion in conservation planning and management is under represented, particularly in the Alps.In this paper we propose a new methodological solution and challenge for the identification of priority areas based on the integration of three approaches: invertebrate multi-taxa based; expert-based; and, GIS-based. The Trentino Province (eastern Italian Alps), was investigated as a case study.The first methodological step was to select a panel of nineteen experts which played a strategic role in the suggestion of 229 species, endangered or of mandatory conservation interest. The second step was to find objective criteria for species prioritisation. These criteria, crossed with the experts taxonomical and ecological knowledge resulted in a list of 70 focal invertebrate species. The third step was to integrate with the GIS-based approach data layers from the habitat requirements of each of the 70 focal species to generate potential spatial-distributional maps. Potential distribution maps gave information about the sites (priority areas) in which the highest number of focal species could concentrate, thus suggesting where to focus future monitoring efforts. Several priority areas resulted outside the protected ones. Alluvial forest and hop-hornbeam woods were the habitats with the highest number of focal species, and thus they represent the habitats of major conservation interest and concern, because they are usually small, fragmented, and near urbanised areas located in the bottom of the valleys. The relatively simple processes involved in species and potential habitat distribution proposed in this paper can be conducted with a minimal amount of data, making it an attractive tool when time and funds are in short supply.     

Evaluation and mapping of the conservation significance of habitats using GIS: an example from Crete, Greece

To ensure the long-term future of NATURA 2000 sites across Europe, effective techniques are required for evaluating and monitoring their conservation significance. This paper describes a GIS-based method that uses multi-criteria evaluation (MCE) to determine the conservation significance of vegetation communities and habitats for a case study of a proposed NATURA 2000 site on the northwest coast of Crete, Greece. The method uses the most frequently used criteria for the selection of priority areas for nature conservation—species and habitat diversity, rarity of species and habitats, naturalness, threat of human disturbance and replaceability. For each community and corresponding habitat type, each criterion was scored according to field data and expert knowledge using a numerical scale. The final conservation score for each community was derived using MCE within a GIS and mapped. The results demonstrated that the method is an effective tool for evaluating and comparing conservation significance and could be applied to other sites across Europe and to monitor change.     

Predicting habitat distribution and frequency from plant species co-occurrence data

Aim Species frequency data have been widely used in nature conservation to aid management decisions. To determine species frequencies, information on habitat occurrence is important: a species with a low frequency is not necessarily rare if it occupies all suitable habitats. Often, information on habitat distribution is available for small geographic areas only. We aim to predict grid‐based habitat occurrence from grid‐based plant species distribution data in a meso‐scale analysis. Location The study was carried out over two spatial extents: Germany and Bavaria. Methods Two simple models were set up to examine the number of characteristic plant species needed per grid cell to predict the occurrence of four selected habitats (species data from FlorKart, http://www.floraweb.de ). Both models were calibrated in Bavaria using available information on habitat distribution, validated for other federal states, and applied to Germany. First, a spatially explicit regression model (generalized linear model (GLM) with assumed binomial error distribution of response variable) was obtained. Second, a spatially independent optimization model was derived that estimated species numbers without using spatial information on habitat distribution. Finally, an additional uncalibrated model was derived that calculated the frequencies of 24 habitats. It was validated using NATURA2000 habitat maps. Results Using the Bavarian models it was possible to predict habitat distribution and frequency from the co‐occurrence of habitat‐specific species per grid cell. As the model validations for other German federal states were successful, the models were applied to all of Germany, and habitat distribution and frequencies could be retrieved for the national scale on the basis of habitat‐specific species co‐occurrences per grid cell. Using the third, uncalibrated model, which includes species distribution data only, it was possible to predict the frequencies of 24 habitats based on the co‐occurrence of 24% of formation‐specific species per grid cell. Predicted habitat frequencies deduced from this third model were strongly related to frequencies of NATURA2000 habitat maps. Main conclusions It was concluded that it is possible to deduce habitat distributions and frequencies from the co‐occurrence of habitat‐specific species. For areas partly covered by habitat mappings, calibrated models can be developed and extrapolated to larger areas. If information on habitat distribution is completely lacking, uncalibrated models can still be applied, providing coarse information on habitat frequencies. Predicted habitat distributions and frequencies can be used as a tool in nature conservation, for example as correction factors for species frequencies, as long as the species of interest is not included in the model set‐up.     

Dealing with non-equilibrium bias and survey effort in presence-only invasive Species Distribution Models (iSDM); predicting the range of muntjac deer in Britain and Ireland

Invasive species managers utilise species records to inform management. These data can also be used in Species Distribution Models (SDM) to predict future spread or potential invasion of new areas. However, issues with non-equilibrium (also called disequilibrium) can cause difficulties in modelling invasive species that have not fully colonised their potential distribution and, in addition, sampling bias can result from a lack of information on survey effort, a particular issue for presence only modelling techniques. Geographical confounds are unavoidable when building iSDMs but there are methods that allow prediction to be optimised. We used maximum entropy (Maxent) to model suitable habitat for invasive Reeve's muntjac deer (Muntiacus reevesi) throughout Great Britain and Ireland comparing several methods that aimed to address invasive Species Distribution Modelling (iSDM) bias including spatial filtering, weighted background points and targeted background points built at varying spatial extents. Model evaluation metrics suggested that the model, which explicitly failed to account for non-equilibrium at the full extent of Great Britain and Ireland using random background points, predicted the species' current invasive range best. This highlighted that negative environmental relationships are likely to represent uncolonised areas rather than habitat selection and thus, low predicted suitability of uncolonised areas was misleading. Of the models that dealt with non-equilibrium conceptually best, by restricting the training extent to their current invasive range or core range, and utilised targeted background points accounting for survey effort (cells with other deer species recorded as present yet with no records for muntjac) as the best model evaluation metric, yielded relatively poor predictive performance. This implied limited habitat selectivity or avoidance within the colonised range which, when spatially extrapolated, suggested virtually all regions in Great Britain and Ireland may be vulnerable to future muntjac invasion.     

Weeds and biodiversity conservation: A review of managing weeds under the New South Wales Threatened Species Conservation Act 1995

Summary   In New South Wales, alien plants pose the second greatest threat to biodiversity behind land clearing and habitat loss, yet current weed management does not always address the biodiversity at risk or put in place mechanisms to ensure their recovery. The problem arises in part from an assumption that control programmes which focus only on the weed will result in a biodiversity benefit, rather than acknowledging the need for an assessment of the biodiversity at risk and subsequent incorporation of such information into management strategies. The New South Wales Threatened Species Conservation Act 1995 (TSC Act) has been used as a tool to integrate weed control and biodiversity management through the listing of weeds as key threatening processes and the development and implementation of Threat Abatement Plans (TAPs). Through this process, weed management is forced to focus on actual biodiversity conservation outcomes by directing control to areas where the likelihood of a positive biodiversity response is maximized. Bitou Bush ( Chrysanthemoides monilifera ssp. rotundata ) was the first weed species listed under the TSC Act as a key threatening process and to have a TAP prepared. Implementation of the Bitou Bush TAP is now potentially assisting the recovery of over 150 native plant species and 24 ecological communities at more than 160 sites. The TAP process is now being used for Lantana ( Lantana camara ) nationally and for all widespread weed species that threaten biodiversity within each of the 13 Catchment Management Authorities across New South Wales. By focusing the objectives of weed control on biodiversity protection and recovery, and ensuring that sites throughout the distribution of the weed are prioritized, threat reduction and conservation outcomes are more likely to occur at a landscape scale.     

Using spotlight observations to predict resource selection and abundance for white-tailed deer

Spotlight surveys for white-tailed deer (Odocoileus virginianus) can yield large presence-only datasets applicable to a variety of resource selection modeling procedures. By understanding how populations distribute according to a given resource for a reference area, density and abundance can be predicted across new areas assuming the relationship between habitat quality (measured by an index of selection) and species distribution are equivalent. Habitat-based density estimators have been applied to wildlife species and are useful for addressing conservation and management concerns. Although achieving reliable population estimates is a primary goal for spotlighting studies, presence-only models have yet to be applied to spotlight data for estimating habitat selection and abundance for deer. From 2012 to 2017, we conducted spring spotlight surveys in each of 99 counties in Iowa, USA, and collected spatial locations for 20,149 groups of deer (n = 71,323 individuals). We used a resource selection function (RSF) based on deer locations to predict the relative probability of use for deer at the population level and to estimate statewide abundance. The number of deer observed statewide increased significantly with increasing RSF value for all years and the mean RSF value along survey transects explained 59% of the variability in county-level deer counts, indicating that a functional response between habitat quality and deer distribution existed at landscape scales. We applied our RSF to a habitat-based density estimator (extrapolation) and zero-inflated Poisson (ZIP) and negative binomial (ZINB) count models to predict statewide abundance from spotlight counts. Population estimates for 2012 were variable, indicating that atypical weather conditions may affect spotlight counts and population estimates in some years. For 2013–2017, we predicted a mean population of 439,129 (95% CI ∼ ± 55,926), 440,360 (∼ ± 43,676), and 465,959 (∼ ± 51,242) deer across years for extrapolation, ZIP, and ZINB models, respectively. Estimates from all models were not significantly different than estimates from an existing deer population accounting model in Iowa for 2013 and 2016, and differed by <76,000 deer for all models from 2013–2017. Extrapolation and ZIP models performed similarly and differed by <2,897 deer across all years, whereas ZINB models showed inconsistencies in model convergence and precision of estimates. Our results indicate that presence-only models are capable of producing reliable and precise estimates of resource selection and abundance for deer at broad landscape scales in Iowa and provide a tool for estimating deer abundance in a spatially explicit manner. © 2019 The Wildlife Society.     

Investigating the potential use of aerial line transect surveys for estimating polar bear abundance in sea ice habitats: A case study for the Chukchi Sea

The expense of traditional capture‐recapture methods, interest in less invasive survey methods, and the circumpolar decline of polar bear (Ursus maritimus) habitat require evaluation of alternative methods for monitoring polar bear populations. Aerial line transect distance sampling (DS) surveys are thought to be a promising monitoring tool. However, low densities and few observations during a survey can result in low precision, and logistical constraints such as heavy ice and fuel and safety limitations may restrict survey coverage. We used simulations to investigate the accuracy and precision of, DS for estimating polar bear abundance in sea ice habitats, using the Chukchi Sea subpopulation as an example. Simulation parameters were informed from a recent pilot survey. Predictions from a resource selection model were used for stratification, and we compared two ratio estimators to account for areas that cannot be sampled. The ratio estimator using predictions of resource selection by polar bears allowed for extrapolation beyond sampled areas and provided results with low bias and CVs ranging from 21% to 36% when abundance was >1,000. These techniques could be applied to other DS surveys to allocate effort and potentially extrapolate estimates to include portions of the landscape that are logistically impossible to survey.