Linking landscape characteristics to local grizzly bear abundance using multiple detection methods in a hierarchical model

ABSTRACTWe studied elk (Cervus canadensis nelsoni) parturition sites at coarse (314‐km2 and 7‐km2) and fine (0.2‐ha) scales in the Black Hills, South Dakota, 2011–2013, following a period of population decline and poor calf recruitment. Our objective was to test whether female elk selected parturition sites across spatial scales in association with forage, terrain ruggedness, road density, or hiding and security cover. At coarse scales in forests and grasslands, female elk selected sites in areas with greater proportions of vegetation communities that provided forage (56–74% of area) and more rugged topography (194–248 m) than found at random. At coarse scales in grasslands, elk selected sites in areas with lower road densities (≤1.24 km/plot). At the fine scale in forests and grasslands, female elk selected sites in areas with intermediate slope (19%), closer to water (355–610 m), and far from roads (541–791 m). Further, elk in forests and grasslands selected sites with intermediate security cover (50–88 m). We hypothesize elk selected for intermediate rugged terrain at larger scales for security from high road densities and human disturbance, but these areas may have placed elk in riskier environments for puma (Puma concolor) predation. Forest management that maintains open‐canopied vegetation communities in less rugged areas and prevents ponderosa pine (Pinus ponderosa) encroachment of meadows to provide forage may be beneficial for elk. Further, elk parturition sites occurred close to roads, particularly on public lands, and agencies should consider road‐use restrictions and vegetation buffers beside roads in areas with less rugged terrain, which may provide favorable calving habitat. © 2015 The Wildlife Society.

Research Article| March 01, 1941 Stratigraphy of the Belt series in Libby and Trout Creek quadrangles, northwestern Montana and northern Idaho RUSSELL GIBSON; RUSSELL GIBSON Search for other works by this author on: GSW Google Scholar W. F. JENKS; W. F. JENKS Search for other works by this author on: GSW Google Scholar IAN CAMPBELL IAN CAMPBELL Search for other works by this author on: GSW Google Scholar GSA Bulletin (1941) 52 (3): 363–379. https://doi.org/10.1130/GSAB-52-363 Article history received: 27 Jun 1940 first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation RUSSELL GIBSON, W. F. JENKS, IAN CAMPBELL; Stratigraphy of the Belt series in Libby and Trout Creek quadrangles, northwestern Montana and northern Idaho. GSA Bulletin 1941;; 52 (3): 363–379. doi: https://doi.org/10.1130/GSAB-52-363 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyGSA Bulletin Search Advanced Search Abstract Three formations of the Belt series—Prichard, Wallace, and Striped Peak—can be traced northward from the northeastern part of the Coeur d’Alene district, Idaho, through the Trout Creek quadrangle to the northern part of the Libby quadrangle, Montana.The differences upon which the separation of the Burke, Revett, and St. Regis is based in the Coeur d’Alene district, however, become less definite northward and fail entirely beyond the Clark Fork. In the area under consideration, therefore, these three are grouped under the term Ravalli according to the usage adopted by Calkins.Stratigraphic details of parts of the Ravalli group and the Wallace and Striped Peak formations are given in tabular sections. Certain beds of the Wallace in the Trout Creek quadrangle show “molar tooth” structures similar to those of the Siyeh and Kitchener formations along the international boundary. In the Libby quadrangle both the Wallace and the Striped Peak contain beds of dolomitic limestone showing fossil algal forms similar to those in Glacier National Park. This content is PDF only. Please click on the PDF icon to access. First Page Preview Close Modal You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

A challenge with species that have disappeared from most of their range is to identify the correlates of local persistence. With species decimated by trade, site‐specific trapping risk is hard to capture by remotely accessed predictors. The recently split yellow‐crested cockatooCacatua sulphureaand citron‐crested cockatooC. citrinocristatahave undergone catastrophic declines due to habitat loss and especially trapping, and are now extinct in much of their former range across Indonesia. Of 144 sites on 30 islands known to contain the species in 1950, only 76 on 27 islands did so in 2017–2019, with many of the other 68 experiencing extinctions between 1985 and 2000. We compared socio‐ecological conditions such as forest cover and loss, human population density and infrastructure, and protected area status between the occupied and unoccupied sites, using ‘random forests’ within decreasing time intervals 1950–2015. Populations on Sulawesi and West Nusa Tenggara were more likely to become extinct than those on Sumba, Timor‐Leste and small remote islands. Sites retaining cockatoos had high proportions of tree cover, low road density and low human densities. The relative importance of these factors changed little over time, but road density and human density became respectively more and less important in recent years. The examination of local conditions at ‘false negative’ sites (where cockatoos survived contrary to model predictions) showed that, particularly in recent years, cockatoo survival has been promoted by site‐specific protection due to traditional beliefs, NGO activities, dedicated individual residents and local topographic barriers. Some of these local influences add complexity to the task of conserving cockatoo strongholds, but also offer exciting possibilities for low‐cost conservation prescriptions tailored to individual sites. Studies combining field and remotely sensed data, and examining false negative sites for beneficial location‐specific conditions, have broad application for the conservation of taxa with once‐large ranges.

The outbreak of COVID-19 in Beijing has been sporadic since the beginning of 2022 and has become increasingly severe since October. In China’s policy of insisting on dynamic clearance, fine-grained management has become the focus of current epidemic prevention and control. In this paper, we conduct a refined COVID-19 risk prediction and identification of its influencing factors in Beijing based on neighborhood-scale spatial statistical units. We obtained geographic coordinate data of COVID-19 cases in Beijing and quantified them into risk indices of each statistical unit. Additionally, spatial autocorrelation was used to analyze the epidemic risk clustering characteristics. With the multi-source data, 20 influencing elements were constructed, and their spatial heterogeneity was explored by screening 8 for Multiscale Geographically weighted regression (MGWR) model analysis. Finally, a neural network classification model was used to predict the risk of COVID-19 within the sixth ring of Beijing. The MGWR model and the neural network classification model showed good performance: the R2 of the MGWR model was 0.770, and the accuracy of the neural network classification model was 0.852. The results of this study show that: (1) COVID-19 risk is uneven, with the highest clustering within the Fifth Ring Road of Beijing; (2) The results of the MGWR model show that population structure, population density, road density, residential area density, and living service facility density have significant spatial heterogeneity on COVID-19 risk; and (3) The prediction results show a high COVID-19 risk, with the most severe risk being in the eastern, southeastern and southern regions. It should be noted that the prediction results are highly consistent with the current epidemic situation in Shijingshan District, Beijing, and can provide a strong reference for fine-grained epidemic prevention and control in Beijing.

AimMapping species distributions is a crucial but challenging requirement of wildlife management. The frequent need to sample vast expanses of potential habitat increases the cost of planned surveys and rewards accumulation of opportunistic observations. In this paper, we integrate planned‐survey data from roost counts with opportunistic samples from eBird, WikiAves and Xeno‐canto citizen‐science platforms to map the geographic range of the endangered Vinaceous‐breasted Parrot. We demonstrate the estimation and mapping of species occurrence based on data integration while accounting for specifics of each dataset, including observation technique and uncertainty about the observations.LocationArgentina, Brazil and Paraguay.MethodsOur analysis illustrates (a) the incorporation of sampling effort, spatial autocorrelation and site covariates in a joint‐likelihood, hierarchical, data integration model; (b) the evaluation of the contribution of each dataset, as well as the contribution of effort covariates, spatial autocorrelation and site covariates to the predictive ability of fitted models using a cross‐validation approach; and (c) how spatial representation of the latent occupancy state (i.e. realized occupancy) helps identify areas with high uncertainty that should be prioritized in future fieldwork.ResultsWe estimate a Vinaceous‐breasted Parrot geographic range of 434,670 km2, which is three times larger than the “Extant” area previously reported in the IUCN Red List. The exclusion of one dataset at a time from the analyses always resulted in worse predictions by the models of truncated data than by the Full Model, which included all datasets. Likewise, exclusion of spatial autocorrelation, site covariates or sampling effort resulted in worse predictions.Main conclusionsThe integration of different datasets into one joint‐likelihood model produced a more reliable representation of the species range than any individual dataset taken on its own, improving the use of citizen‐science data in combination with planned‐survey results.

Biodiversity monitoring at large spatial and temporal scales is essential for informing conservation policies. The International Waterbird Census (IWC) is one of the longest-running global citizen science monitoring schemes, providing critical information to several international agreements. However, analyzing IWC count data poses statistical challenges, including zero inflation, overdispersion, spatial autocorrelation, and missing data. While various modeling approaches have been used to estimate waterbird population size and trends, their ability to handle these issues and the implications for trend estimates remain unassessed. Using IWC count data from five species in the East Atlantic Flyway, we compared four modeling approaches: TRIM (TRends and Indices for Monitoring data), LORI (Low-Rank Interactions), and two generalized linear mixed models (GLMMs) with simple or optimized parametrizations. We benchmarked their performance in addressing zero inflation, overdispersion, and spatial autocorrelation across different realistic sampling designs (i.e., alternative dataset configurations). Our results highlight significant limitations in commonly used methods. Simple GLMMs, TRIM, and LORI generally failed to mitigate both zero inflation and overdispersion. In contrast, optimized GLMMs improved model convergence and better addressed these issues by selecting appropriate probability distributions. However, no single distribution performed consistently well across species and sampling designs. Spatial structures were effective in reducing spatial autocorrelation in most cases. We recommend a careful species-specific selection of statistical methods when analyzing count data, as inadequate models may misrepresent population trends and thus misguide conservation efforts. Future research should explore the integration of advanced hierarchical and spatio-temporal models to improve inference from large-scale citizen science datasets.

Global climate change (GCC) may be causing distribution range shifts in many organisms worldwide. Multiple efforts are currently focused on the development of models to better predict distribution range shifts due to GCC. We addressed this issue by including intraspecific genetic structure and spatial autocorrelation (SAC) of data in distribution range models. Both factors reflect the joint effect of ecoevolutionary processes on the geographical heterogeneity of populations. We used a collection of 301 georeferenced accessions of the annual plant Arabidopsis thaliana in its Iberian Peninsula range, where the species shows strong geographical genetic structure. We developed spatial and nonspatial hierarchical Bayesian models (HBMs) to depict current and future distribution ranges for the four genetic clusters detected. We also compared the performance of HBMs with Maxent (a presence-only model). Maxent and nonspatial HBMs presented some shortcomings, such as the loss of accessions with high genetic admixture in the case of Maxent and the presence of residual SAC for both. As spatial HBMs removed residual SAC, these models showed higher accuracy than nonspatial HBMs and handled the spatial effect on model outcomes. The ease of modelling and the consistency among model outputs for each genetic cluster was conditioned by the sparseness of the populations across the distribution range. Our HBMs enrich the toolbox of software available to evaluate GCC-induced distribution range shifts by considering both genetic heterogeneity and SAC, two inherent properties of any organism that should not be overlooked.

BackgroundFast food restaurants reportedly target specific populations by locating in lower-income and in minority neighborhoods. Physical proximity to fast food restaurants has been associated with higher obesity rates.ObjectiveTo examine possible associations, at the census tract level, between area demographics, arterial road density, and fast food restaurant density in King County, WA, USA.MethodsData on median household incomes, property values, and race/ethnicity were obtained from King County and from US Census data. Fast food restaurant addresses were obtained from Public Health-Seattle & King County and were geocoded. Fast food density was expressed per tract unit area and per capita. Arterial road density was a measure of vehicular and pedestrian access. Multivariate logistic regression models containing both socioeconomic status and road density were used in data analyses.ResultsOver one half (53.1%) of King County census tracts had at least one fast food restaurant. Mean network distance from dwelling units to a fast food restaurant countywide was 1.40 km, and 1.07 km for census tracts containing at least one fast food restaurant. Fast food restaurant density was significantly associated in regression models with low median household income (p < 0.001) and high arterial road density (p < 0.001) but not with percent of residents who were nonwhite.ConclusionNo significant association was observed between census tract minority status and fast food density in King County. Although restaurant density was linked to low household incomes, that effect was attenuated by arterial road density. Fast food restaurants in King County are more likely to be located in lower income neighborhoods and higher traffic areas.

Effects of road network on diversiform forest cover changes in the highest coverage region in China: An analysis of sampling strategies

Several methods are commonly used to determine the age of seedlings, and destructive aging methods are often assumed to be the most reliable. Terminal bud-scar counts can be used as a nondestructive alternative for aging seedlings, although clear criteria for which this method is appropriate are not well-known. This article evaluates the use of terminal bud-scar counts for aging seedlings of four conifer species in Glacier National Park, Montana. The results of our study suggest that terminal bud scars are reliable indicators of annual vertical growth, but that the accuracy of the method is limited to varying degrees by the age and height of seedlings. Method accuracy and error are more strongly related to seedling age and height for fast-growing species (western larch and lodgepole pine) than for slower-growing species (Engelmann spruce and Douglas-fir).

Wildfire effects on stream food webs and nutrient dynamics in Glacier National Park, USA

This paper presents an analysis of models for Japanese short-term inter- est rate. The models are constructed based on mean reverting model using Bayesian method to capture the dynamics of short-term interest rate. The parameters of our models are estimated by marginal likelihood and posterior expectation and we shall make model selection using the information criterion EIC(extended information cri- terion). An application of the models will be implemented using weekly Japanese average interest rates on certificates of deposit (new issues) less than 30 days in the period from January 2001 to December 2008. 1 Introduction Correct modeling of the short-term interest rate is essential in finace, as it is this rate that is fundamental to the pricing of securities and important for risk man- agement. In the study of the short-term interest rate dynamics, various models have been suggested. There are examples of these models, such as Vasicek model by Vasicek(1977), CIR model by Cox, Ingersoll and Ross(1985) and so on. An empirical comparison of these models was made by Chan, Karolyi, Longstaff and Sanders(1992). In the paper, the param- eters are estimated by generalized method of moments(GMM) and they implemented the hypothesis testing methods developed by Newey and West for evaluation of the models. In the parameter estimation of mean reverting model using GMM, it is known that the results are easy to be influenced by an initial values, and it becomes often unstable. Recently, Ahangarani(2005) employed maximum likelihood method for parameter esti- mation and log likelihood ratio test for model selection, and Kawada(2007) implemented GMM and made model selection using the information criterion EIC(extended information criterion) for various short-term interest rate models. Another articles of the interest rate model, using Bayesian framework, Jones(2003) published study about nonlinear drift of the interest rate model in detail, and Gray(2005) studied continuous time short rate models and the parameters estimated by Markov chain Monte Carlo(MCMC) method. Sanford and Martin(2006) made estimation using MCMC algorithm and model selection is made by Bayes factors for each model calculated using Savage-Dickey density ratio. In addition, a recent study by Hong and Lin(2006) examined a variety of short-term interest rate mod- els including the single-factor diffusion models, GARCH models, Markov regime-switching models and jump-diffusion models for Chinese rates. However, more useful models which capture the dynamics of the short-term interest rate are needed. In this paper, we suggest two hierarchical Bayes models based on mean reverting model. The parameters of the models are treated as random variables so that we have to consider appropriate prior distributions for them. By regarding parameters as random variables, we can consider that model construction and estimation involve necessarily some uncertainty and utilize the prior information of interest rate. In the first hierarchical Bayes model, we assume that volatility is constant, and in the second one, it depends on the

Salt marshes in the northeastern United States support several specialized breeding bird species that are threatened by sea level rise (SLR) and coastal development, processes that drive habitat change and fragmentation. There have been rapid, widespread declines in some species, but mechanisms driving population change and whether declines continue remain unclear. We examined the influence of phenomena expected to modify salt marshes, including SLR, sediment delivery rates, and land use, on the population trajectories of saltmarsh breeding birds. We modeled population trajectories of 5 species with spatially extensive point count surveys conducted from Maine to Virginia from 2011 to 2022. We used Bayesian hierarchical abundance models and model selection to identify phenomena that had the strongest effect on population change. Clapper rails (Rallus crepitans) continued their long-term decline (-4.1%/year). Willets (Tringa semipalmata semipalmata) (2.6%/year) and saltmarsh sparrows (Ammospiza caudacuta) (4.1%/year) increased, and seaside (Ammospiza maritima) and Nelson's (Ammospiza nelsoni subvirgatus) sparrows exhibited no clear change in abundance. The estimated increase for saltmarsh sparrow was not consistent with trends over the previous 25years but aligned with prior demographic modeling, which predicted a short-term stabilization during the study years before an expected return to a long-term decline. Road density and other tidal restrictions near marshes were generally good predictors of abundance over the study period, as was marsh habitat composition. Local rates of SLR and sediment delivery were not as good predictors. During periods of relatively low rates of realized SLR, local-scale drivers of population trends had relatively stronger effects than global drivers on the persistence of several saltmarsh breeding birds. Conservation practitioners, however, should be attentive to global drivers, especially as rates of SLR accelerate in the future.

Aim To examine the influence of environmental variables on species richness patterns of amphibians, reptiles, mammals and birds and to assess the general usefulness of regional atlases of fauna.Location Navarra (10,421 km2) is located in the north of the Iberian Peninsula, in a territory shared by Mediterranean and Eurosiberian biogeographic regions. Important ecological patterns, climate, topography and land‐cover vary significantly from north to south.Methods Maps of vertebrate distribution and climatological and environmental data bases were used in a geographic information systems framework. Generalized additive models and partial regression analysis were used as statistical tools to differentiate (A) the purely spatial fraction, (B) the spatially structured environmental fraction and (C) the purely environmental fraction. In this way, we can evaluate the explanatory capacity of each variable, avoiding false correlations and assessing true causality. Final models were obtained through a stepwise procedure.Results Energy‐related features of climate, aridity and land‐cover variables show significant correlation with the species richness of reptiles, mammals and birds. Mammals and birds exhibit a spatial pattern correlated with variables such as aridity index and vegetation land‐cover. However, the high values of the spatially structured environmental fraction B and the low values of the purely environmental fraction A suggest that these predictor variables have a limited causal relationship with species richness for these vertebrate groups. An increment in land‐cover diversity is correlated with an increment of specific richness in reptiles, mammals and birds. No variables were found to be statistically correlated with amphibian species richness.Main conclusions Although aridity and land‐cover are the best predictor variables, their causal relationship with species richness must be considered with caution. Historical factors exhibiting a similar spatial pattern may be considered equally important in explaining the patterns of species richness. Also, land‐cover diversity appears as an important factor for maintaining biological diversity. Partial regression analysis has proved a useful technique in dealing with spatial autocorrelation. These results highlight the usefulness of coarsely sampled data and cartography at regional scales to predict and explain species richness patterns for mammals and birds. The accuracy of models appears to be related to the range perception of each group and the scale of the information.

With the advance of methods for estimating species distribution models has come an interest in how to best combine datasets to improve estimates of species distributions. This has spurred the development of data integration methods that simultaneously harness information from multiple datasets while dealing with the specific strengths and weaknesses of each dataset.We outline the general principles that have guided data integration methods and review recent developments in the field. We then outline key areas that allow for a more general framework for integrating data and provide suggestions for improving sampling design and validation for integrated models.Key to recent advances has been using point‐process thinking to combine estimators developed for different data types. Extending this framework to new data types will further improve our inferences, as well as relaxing assumptions about how parameters are jointly estimated. These along with the better use of information regarding sampling effort and spatial autocorrelation will further improve our inferences.Recent developments form a strong foundation for implementation of data integration models. Wider adoption can improve our inferences about species distributions and the dynamic processes that lead to distributional shifts.