Using distance sampling and hierarchical models to improve estimates of Dall's sheep abundance

Management of large mammal populations has often been based on aerial minimum count surveys that are uncorrected for incomplete detection and lack estimates of precision. These limitations can be particularly problematic for Dall's sheep (Ovis dalli dalli) due to the high cost of surveys and variation in detection probability across time and space. The limitations of these methods have been recognized for some time, but previously proposed alternatives for sheep surveys proved to be too costly and logistically unfeasible in most circumstances (Udevitz et al. 2006). We assessed the potential for a combination of distance sampling surveys and a hierarchical modeling approach to provide a more efficient means for estimating Dall's sheep abundance by conducting aerial contour transect surveys over all sheep habitat in Gates of the Arctic National Park and Preserve (GAAR), Alaska in 2009 and 2010. We estimated the population of Dall's sheep was 8,412 (95% CI: 6,517–11,090) and 10,072 (95% CI 8,081–12,520) in 2009 and 2010, respectively. Abundance within the Itkillik Preserve area within GAAR was 1,898 (95% CI: 1,421–2,578) and 1,854 (95% CI: 1,342–2,488) in 2009 and 2010, respectively. Estimates of lamb abundance in 2010 were more than double those of 2009 after correcting for detection bias related to group size, suggesting that the apparent estimate of lambs in the population may be biased in some years depending on the degree of aggregation. Overall, the contour transect surveys were feasible logistically, cost 70–80% less than minimum count surveys, and produced precise estimates of abundance, indicating that the application of these methods could be used effectively to increase the statistical rigor and spatial extent of Dall's sheep abundance surveys throughout Alaska. These methods could be used to improve the assessment of long-term trends in populations and productivity and provide valuable information for harvest management at both local and landscape scales at reduced costs in comparison to traditional minimum count surveys. © 2011 The Wildlife Society.     

Improved spatial estimates of climate predict patchier species distributions

Aim

Correlative species distribution models (SDMs) combined with spatial layers of climate and species' localities represent a frequently utilized and rapid method for generating spatial estimates of species distributions. However, an SDM is only as accurate as the inputs upon which it is based. Current best‐practice climate layers commonly utilized in SDM (e.g. ANUCLIM) are frequently inaccurate and biased spatially. Here, we statistically downscale 30 years of existing spatial weather estimates against empirical weather data and spatial layers of topography and vegetation to produce highly accurate spatial layers of weather. We proceed to demonstrate the effect of inaccurately quantified spatial data on SDM outcomes.

Location

The Australian Wet Tropics.

Methods

We use Boosted Regression Trees (BRTs) to generate 30 years of spatial estimates of daily maximum and minimum temperature for the study region and aggregate the resultant weather layers into ‘accuCLIM’ climate summaries, comparable with those generated by current best‐practice climate layers. We proceed to generate for seven species of rainforest skink comparable SDMs within species; one model based on ANUCLIM climate estimates and another based on accuCLIM climate estimates.

Results

Boosted Regression Trees weather layers are more accurate with respect to empirically measured temperature, particularly for maximum temperature, when compared to current best‐practice weather layers. ANUCLIM climate layers are least accurate in heavily forested upland regions, frequently over‐predicting empirical mean maximum temperature by as much as 7°. Distributions of the focal species as predicted by accuCLIM were more fragmented and contained less core distributional area.

Conclusion

Combined these results reveal a source of bias in climate‐based SDMs and indicate a solution in the form of statistical downscaling. This technique will allow researchers to produce fine‐grained, ground‐truthed spatial estimates of weather based on existing estimates, which can be aggregated in novel ways, and applied to correlative or process‐based modelling techniques.

     

An open spatial capture–recapture model for estimating density,movement, and population dynamics from line‐transect surveys

The purpose of many wildlife population studies is to estimate density, movement, or demographic parameters. Linking these parameters to covariates, such as habitat features, provides additional ecological insight and can be used to make predictions for management purposes. Line‐transect surveys, combined with distance sampling methods, are often used to estimate density at discrete points in time, whereas capture–recapture methods are used to estimate movement and other demographic parameters. Recently, open population spatial capture–recapture models have been developed, which simultaneously estimate density and demographic parameters, but have been made available only for data collected from a fixed array of detectors and have not incorporated the effects of habitat covariates. We developed a spatial capture–recapture model that can be applied to line‐transect survey data by modeling detection probability in a manner analogous to distance sampling. We extend this model to a) estimate demographic parameters using an open population framework and b) model variation in density and space use as a function of habitat covariates. The model is illustrated using simulated data and aerial line‐transect survey data for North Atlantic right whales in the southeastern United States, which also demonstrates the ability to integrate data from multiple survey platforms and accommodate differences between strata or demographic groups. When individuals detected from line‐transect surveys can be uniquely identified, our model can be used to simultaneously make inference on factors that influence spatial and temporal variation in density, movement, and population dynamics.     

Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics

Aim The study and prediction of species–environment relationships is currently mainly based on species distribution models. These purely correlative models neglect spatial population dynamics and assume that species distributions are in equilibrium with their environment. This causes biased estimates of species niches and handicaps forecasts of range dynamics under environmental change. Here we aim to develop an approach that statistically estimates process‐based models of range dynamics from data on species distributions and permits a more comprehensive quantification of forecast uncertainties. Innovation We present an approach for the statistical estimation of process‐based dynamic range models (DRMs) that integrate Hutchinson's niche concept with spatial population dynamics. In a hierarchical Bayesian framework the environmental response of demographic rates, local population dynamics and dispersal are estimated conditional upon each other while accounting for various sources of uncertainty. The method thus: (1) jointly infers species niches and spatiotemporal population dynamics from occurrence and abundance data, and (2) provides fully probabilistic forecasts of future range dynamics under environmental change. In a simulation study, we investigate the performance of DRMs for a variety of scenarios that differ in both ecological dynamics and the data used for model estimation. Main conclusions Our results demonstrate the importance of considering dynamic aspects in the collection and analysis of biodiversity data. In combination with informative data, the presented framework has the potential to markedly improve the quantification of ecological niches, the process‐based understanding of range dynamics and the forecasting of species responses to environmental change. It thereby strengthens links between biogeography, population biology and theoretical and applied ecology.     

Aggregated spatial distributions of species in a subtropical karst forest,southwestern China

Aims Spatial distribution patterns of species reflect not only the ecological processes but also the habitat features that are related to species distribution. In karst topography, species distribution patterns provide more specific information about their environments. The objectives of this study are as follows: (i) to analyse and explain the spatial distribution patterns of conspecific trees in an old-growth subtropical karst forest; (ii) to investigate pattern changes at different spatial scales; (iii) to test the spatial pattern similarity (or dissimilarity) between trees at different abundances, diameter at breast height classes, canopy layers and different functional groups (shade tolerance and seed dispersal mode); (iv) to examine whether habitat heterogeneity has an important effect on the species spatial distribution.Methods The spatial distributions of woody species with ≥20 individuals in a 1-ha subtropical karst forest plot at Maolan in southwestern China were quantified using the relative neighbourhood density Ω based on the average density of conspecific species in a circular neighbourhood around each species.Important findings Aggregated distribution is the dominant pattern in the karst forest, but the ratio of aggregated species in total species number decreases with an increase in spatial scale. Less abundant species are more aggregated than most abundant species. Aggregation is weaker in larger diameter classes, which is consistent with the prediction of self-thinning. Seed dispersal mode influences spatial patterns, with species dispersed by animals being less aggregated than those dispersed by wind and gravity. Other species functional traits (e.g. shade tolerance) also influence the species spatial distributions. Moreover, differences among species habitat associations, e.g. with rocky outcrops, play a significant role in species spatial distributions. These results indicate that habitat heterogeneity, seed dispersal limitation and self-thinning primarily contribute to the species spatial distributions in this subtropical karst forest.     

Estimating abundance and population trends when detection is low and highly variable: A comparison of three methods for the Hermann's tortoise

Assessing population trends is a basic prerequisite to carrying out adequate conservation strategies. Selecting an appropriate method to monitor animal populations can be challenging, particularly for low-detection species such as reptiles. This study compares 3 detection-corrected abundance methods (capture–recapture, distance sampling, and N-mixture) used to assess population size of the threatened Hermann's tortoise. We used a single dataset of 432 adult tortoise observations collected at 118 sampling sites in the Plaine des Maures, southeastern France. We also used a dataset of 520 tortoise observations based on radiotelemetry data collected from 10 adult females to estimate and model the availability (g0) needed for distance sampling. We evaluated bias for N-mixture and capture–recapture, by using simulations based on different values of detection probabilities. Finally, we conducted a power analysis to estimate the ability of the 3 methods to detect changes in Hermann's tortoise abundances. The abundance estimations we obtained using distance sampling and N-mixture models were respectively 1.75 and 2.19 times less than those obtained using the capture–recapture method. Our results indicated that g0 was influenced by temperature variations and can differ for the same temperature on different days. Simulations showed that the N-mixture models provide unstable estimations for species with detection probabilities <0.5, whereas capture–recapture estimations were unbiased. Power analysis showed that none of the 3 methods were precise enough to detect slow population changes. We recommend that great care should be taken when implementing monitoring designs for species with large variation in activity rates and low detection probabilities. Although N-mixture models are easy to implement, we would not recommend using them in situations where the detection probability is very low at the risk of providing biased estimates. Among the 3 methods allowing estimation of tortoise abundances, capture–recapture should be preferred to assess population trends. © 2013 The Wildlife Society.     

Addressing Temporal Variability in Bird Calling with Design and Estimation: A Northern Bobwhite Example

Imprecise or biased density estimates can lead to inadequate conservation action, overexploitation of game species, or lost recreational opportunities. Common approaches to estimating density of avian populations often either ignore the probability that an individual is present within the sampling area but is not available to be sampled (e.g., not vocalizing), or do not consider covariates that could influence availability. Additionally, management decisions made at the management unit scale are often informed by inadequate monitoring practices, such as limited sampling intensity. In such cases, management agencies calculate density by applying correction factors (e.g., detection probabilities estimated using empirical data from a different study system) to count data, rather than estimating a detection function directly using statistical models. We conducted a simulation study using northern bobwhite (Colinus virginianus; bobwhite) as a model species to quantify the consequences of mis-specifying avian point count models on bias and precision of density estimates. We compared bias and precision of estimates from a fully specified distance-sampling model that estimates availability and detection to 4 different mis-specified approaches, including 2 approaches to calculating density using correction factors. Using correction factors to calculate density produced estimates with low bias but relatively lower precision compared to the fully specified model (CV of density estimates at 35 sites over 5 years: fully specified = 10%, correction factors = 25% and 30%). Although the mean precision and bias of the fully specified model improved with more data (70 sites over 5 years, CV = 9%; 35 sites over 10 years, CV = 9%), precision of correction factors did not (70 sites over 5 years, CV = 22% and 27%; 35 sites over 10 years, CV = 24% and 29%). The fully specified model captured the underlying temporal variation in detection and availability. Increasing sampling duration from 5 to 10 years improved modeled estimates of growth rate, even for mis-specified models, but not derived growth rates using pre-determined detection functions. We demonstrated that conducting point counts 3 times/year at a feasible number of sites can produce relatively unbiased estimates of bobwhite density. Pre-determined detection functions can be fortuitously unbiased for certain years, but they are not a reliable method for determining density or identifying trends in density over time. © 2020 The Wildlife Society.     

Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence‐only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km²) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest.     

Predicting population consequences of ocean climate change for an ecosystem sentinel,the seabird Cassin's auklet

Forecasting the ecological effects of climate change on marine species is critical for informing greenhouse gas mitigation targets and developing marine conservation strategies that remain effective and increase species' resilience under changing climate conditions. Highly productive coastal upwelling systems are predicted to experience substantial effects from climate change, making them priorities for ecological forecasting. We used a population modeling approach to examine the consequences of ocean climate change in the California Current upwelling ecosystem on the population growth rate of the planktivorous seabird Cassin's auklet (Ptychoramphus aleuticus), a demographically sensitive indicator of marine climate change. We use future climate projections for sea surface temperature and upwelling intensity from a regional climate model to forecast changes in the population growth rate of the auklet population at the important Farallon Island colony in central California. Our study projected that the auklet population growth rate will experience an absolute decline of 11–45% by the end of the century, placing this population on a trajectory toward extinction. In addition, future changes in upwelling intensity and timing of peak upwelling are likely to vary across auklet foraging regions in the California Current Ecosystem (CCE), producing a mosaic of climate conditions and ecological impacts across the auklet range. Overall, the Farallon Island Cassin's auklet population has been declining during recent decades, and ocean climate change in this century under a mid‐level emissions scenario is projected to accelerate this decline, leading toward population extinction. Because our study species has proven to be a sensitive indicator of oceanographic conditions in the CCE and a powerful predictor of the abundance of other important predators (i.e. salmon), the significant impacts we predicted for the Cassin's auklet provide insights into the consequences that ocean climate change may have for other plankton predators in this system.     

Fire,landscape change and models of small mammal habitat suitability at multiple spatial scales

Fire is an important process in many ecosystems, but inappropriate fire regimes can adversely affect biodiversity. We identified a naturally flammable heathy woodland ecosystem where the use of planned fire had increased the extent of older vegetation, and quantified the abundance of two small native mammals in this landscape (silky mouse Pseudomys apodemoides and heath rat P. shortridgei). We defined four time‐since‐fire (TSF) categories representing a 2‐ to 55‐year post‐fire sequence and, on the basis of a habitat accommodation model, predicted that both species would select younger age‐classes over older ones. We also predicted that (i) much of the variance in vegetation structure would remain unexplained by TSF and (ii) statistical models of mammal abundance and occupancy including structural variables as predictors would be better than models including TSF. Pseudomys apodemoides selected 17‐ to 23‐year‐old sites, while there was no evidence that P. shortridgei selected a particular TSF category, findings that were inconsistent with our predictions. In line with our predictions, relatively large portions of the variance in vegetation structure remained unexplained by TSF (adjustedr² for four structural variables: 0.24, 0.29, 0.35 and 0.57), and in three of four cases there was strong evidence that statistical models of mammal abundance and occupancy including structural variables were better than those including TSF. At the site scale (hectares), P. shortridgei abundance was positively related to the cover of dead material at the base of Xanthorrhoea plants and at the trap scale (metres), the trapability of both species was significantly related to vegetation volume at 0–20 cm. Our findings suggest that TSF may not be a good proxy for either vegetation structure or species abundance/occupancy.     

Integrating distance sampling survey data with population indices to separate trends in abundance and temporary immigration

Managers rely on accurate estimators of wildlife abundance and trends for management decisions. Despite the focus of contemporary wildlife science on developing methods to improve inference from wildlife surveys, legacy datasets often rely on index counts that lack information about the detection process. Data integration can be a useful tool for combining index counts with data collected under more rigorous designs (i.e., designs that account for the detection process), but care is required when datasets represent different population processes or are mismatched in space and time. This can be particularly problematic in cases where animals aggregate in response to a spatially or temporally limited resource because individuals may temporarily immigrate from outside the study area and be included in the abundance index. Abundance indices based on brown bear (Ursus arctos) feeding aggregations within coastal meadows in early summer in Lake Clark National Park and Preserve, Alaska, USA, are one such example. These indices reflect the target population (brown bears residing within the park) and temporary immigrants (i.e., bears drawn from outside the park boundary). To properly account for the effects of temporary immigration, we integrated the index data with abundance data collected via park-wide distance sampling surveys, the latter of which properly addressed the detection process. By assuming that the distance data provide inference on abundance and the index counts represent some combination of abundance and temporary immigration processes, we were able to decompose the relative contribution of each to overall trend. We estimated that the density of brown bears within our study area was 38–54 adults/1,000 km² during 2003–2019 and that abundance increased at a rate of approximately 1.4%/year. The contribution of temporary immigrants to overall trend in the index was low, so we created 3 hypothetical scenarios to more fully demonstrate how the integrated approach could be useful in situations where the composite trend in meadow counts may obscure trends in abundance (e.g., opposing trends in abundance and temporary immigration). Our work represents a conceptual advance supporting the integration of legacy index data with more rigorous data streams and is broadly applicable in cases where trends in index values may represent a mixture of population processes.