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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↗

USGS2003

An integrated analysis of the effects of past land use on forest herb colonization at the landscape scale

A framework that summarizes the direct and indirect effects of past land use on forest herb recolonization is proposed, and used to analyse the colonization patterns of forest understorey herbaceous species in a 360-ha mixed forest, grassland and arable landscape in the Dijle river valley (central Belgium). Fine-scale distribution maps were constructed for 14 species. The species were mapped in 15 946 forest plots and outside forests (along parcel margins) in 5188 plots. Forest stands varied in age between 1 and more than 224 years. Detailed land-use history data were combined with the species distribution maps to identify species-specific colonization sources and to calculate colonization distances. The six most frequent species were selected for more detailed statistical analysis. Logistic regression models indicated that species frequency in forest parcels was a function of secondary forest age, distance from the nearest colonization source and their interaction. Similar age and distance effects were found within hedgerows. In 199 forest stands, data about soils, canopy structure and the cover of competitive species were collected. The relative importance of habitat quality and spatio-temporal isolation for the colonization of the forest herb species was quantified using structural equation modelling (SEM), within the framework proposed for the effects of past land use. The results of the SEM indicate that, except for the better colonizing species, the measured habitat quality variables are of minor importance in explaining colonization patterns, compared with the combination of secondary forest age and distance from colonization sources. Our results suggest the existence of a two-stage colonization process in which diaspore availability determines the initial pattern, which is affected by environmental sorting at later stages.

Journal of Ecology↗

USGS2021

Environmental and anthropogenic drivers of contaminants in agricultural watersheds with implications for land management

If not managed properly, modern agricultural practices can alter surface and groundwater quality and drinking water resources resulting in potential negative effects on aquatic and terrestrial ecosystems. Exposure to agriculturally derived contaminant mixtures has the potential to alter habitat quality and negatively affect fish and other aquatic organisms. Implementation of conservation practices focused on improving water quality continues to increase particularly in agricultural landscapes throughout the United States. The goal of this study was to determine the consequences of land management actions on the primary drivers of contaminant mixtures in five agricultural watersheds in the Chesapeake Bay, the largest watershed of the Atlantic Seaboard in North America where fish health issues have been documented for two decades. Surface water was collected and analyzed for 301 organic contaminants to determine the benefits of implemented best management practices (BMPs) designed to reduce nutrients and sediment to streams in also reducing contaminants in surface waters. Of the contaminants measured, herbicides (atrazine, metolachlor), phytoestrogens (formononetin, genistein, equol), cholesterol and total estrogenicity (indicator of estrogenic response) were detected frequently enough to statistically compare to seasonal flow effects, landscape variables and BMP intensity. Contaminant concentrations were often positively correlated with seasonal stream flow, although the magnitude of this effect varied by contaminant across seasons and sites. Land-use and other less utilized landscape variables including biosolids, manure and pesticide application and percent phytoestrogen producing crops were inversely related with site-average contaminant concentrations. Increased BMP intensity was negatively related to contaminant concentrations indicating potential co-benefits of BMPs for contaminant reduction in the studied watersheds. The information gained from this study will help prioritize ecologically relevant contaminant mixtures for monitoring and contributes to understanding the benefits of BMPs on improving surface water quality to better manage living resources in agricultural landscapes inside and outside the Chesapeake Bay watershed.

Delaware, Maryland, New Jersey, New York, Pennsylv↗

USGS2016

Inferring social structure and its drivers from refuge use in the desert tortoise, a relatively solitary species

For several species, refuges (such as burrows, dens, roosts, nests) are an essential resource for protection from predators and extreme environmental conditions. Refuges also serve as focal sites for social interactions, including mating, courtship, and aggression. Knowledge of refuge use patterns can therefore provide information about social structure, mating, and foraging success, as well as the robustness and health of wildlife populations, especially for species considered to be relatively solitary. In this study, we construct networks of burrow use to infer social associations in a threatened wildlife species typically considered solitary—the desert tortoise. We show that tortoise social networks are significantly different than null networks of random associations, and have moderate spatial constraints. We next use statistical models to identify major mechanisms behind individual-level variation in tortoise burrow use, popularity of burrows in desert tortoise habitat, and test for stressor-driven changes in refuge use patterns. We show that seasonal variation has a strong impact on tortoise burrow switching behavior. On the other hand, burrow age and topographical condition influence the number of tortoises visiting a burrow in desert tortoise habitat. Of three major population stressors affecting this species (translocation, drought, disease), translocation alters tortoise burrow switching behavior, with translocated animals visiting fewer unique burrows than residents. In a species that is not social, our study highlights the importance of leveraging refuge use behavior to study the presence of and mechanisms behind non-random social structure and individual-level variation. Our analysis of the impact of stressors on refuge-based social structure further emphasizes the potential of this method to detect environmental or anthropogenic disturbances.

Behavioral Ecology and Sociobiology↗

USGS2012

Climate-change-driven deterioration of water quality in a mineralized watershed

A unique 30-year streamwater chemistry data set from a mineralized alpine watershed with naturally acidic, metal-rich water displays dissolved concentrations of Zn and other metals of ecological concern increasing by 100–400% (400–2000 μg/L) during low-flow months, when metal concentrations are highest. SO 4 and other major ions show similar increases. A lack of natural or anthropogenic land disturbances in the watershed during the study period suggests that climate change is the underlying cause. Local mean annual and mean summer air temperatures have increased at a rate of 0.2–1.2 °C/decade since the 1980s. Other climatic and hydrologic indices, including stream discharge during low-flow months, do not display statistically significant trends. Consideration of potential specific causal mechanisms driven by rising temperatures suggests that melting of permafrost and falling water tables (from decreased recharge) are probable explanations for the increasing concentrations. The prospect of future widespread increases in dissolved solutes from mineralized watersheds is concerning given likely negative impacts on downstream ecosystems and water resources, and complications created for the establishment of attainable remediation objectives at mine sites.

Environmental Science & Technology↗

USGS1999

Use of behavioral endpoints to determine protective concentrations of the insecticide fonofos for bluegill (Lepomis macrochirus)

This research compared the results of laboratory and mesocosm studies to determine the effectiveness of using behavioral measures of sublethal exposure to define environmental concentration ranges that are protective of free-ranging populations of bluegill (Lepomis macrochirus) exposed to the organophophate insecticide fonofos. Thirty-day laboratory chronic studies were conducted to determine the relative sensitivity of standard (e.g. survival and growth) and non-standard behavioral (e.g. swimming capacity, feeding efficiency, and aggression) endpoints in predicting concentrations of fonofos protective of bluegill growth and survival. The lowest observable effect concentration (LOECs) for the standard measures of survival and growth was 5.6 ??g/L. Two behavioral endpoints were of similar sensitivity to the standard measures: swimming capacity, LOEC of 5.6 ??g/L; and prey strike frequency, LOEC of 5.6 ??g/L. However, aggressive interactions were ten-fold more sensitive than swimming or feeding behavior with a LOEC occurring at 0.6 ??g/L. Lab results were compared to an aquatic mesocosm study which exposed adult and juvenile bluegill to a 9.41 ??g/L concentration of fonofos. The dissipation half-life of fonofos was 5 days in 0.1 hectare aquatic mesocosms. Significant mortality among caged bluegill occurred within 4 days of exposure at 9.41 ??g/L. However, the 9.41 ??g/L concentration of fonofos had no statistically significant effects on survival, growth, reproduction, or total biomass of free-ranging populations of bluegill. We conclude from these studies that laboratory data can accurately estimate concentrations that are lethal in the field and that the use of behavioral endpoints can provide ecologically relevant, yet conservative estimates of concentrations that are protective of field populations.

Conference Paper↗

USGS2011

Estimating equivalence with quantile regression

Equivalence testing and corresponding confidence interval estimates are used to provide more enlightened statistical statements about parameter estimates by relating them to intervals of effect sizes deemed to be of scientific or practical importance rather than just to an effect size of zero. Equivalence tests and confidence interval estimates are based on a null hypothesis that a parameter estimate is either outside (inequivalence hypothesis) or inside (equivalence hypothesis) an equivalence region, depending on the question of interest and assignment of risk. The former approach, often referred to as bioequivalence testing, is often used in regulatory settings because it reverses the burden of proof compared to a standard test of significance, following a precautionary principle for environmental protection. Unfortunately, many applications of equivalence testing focus on establishing average equivalence by estimating differences in means of distributions that do not have homogeneous variances. I discuss how to compare equivalence across quantiles of distributions using confidence intervals on quantile regression estimates that detect differences in heterogeneous distributions missed by focusing on means. I used one-tailed confidence intervals based on inequivalence hypotheses in a two-group treatment-control design for estimating bioequivalence of arsenic concentrations in soils at an old ammunition testing site and bioequivalence of vegetation biomass at a reclaimed mining site. Two-tailed confidence intervals based both on inequivalence and equivalence hypotheses were used to examine quantile equivalence for negligible trends over time for a continuous exponential model of amphibian abundance. ?? 2011 by the Ecological Society of America.

Ecological Applications↗

USGS2010

Climate change threatens polar bear populations: A stochastic demographic analysis

The polar bear (Ursus maritimus) depends on sea ice for feeding, breeding, and movement. Significant reductions in Arctic sea ice are forecast to continue because of climate warming. We evaluated the impacts of climate change on polar bears in the southern Beaufort Sea by means of a demographic analysis, combining deterministic, stochastic, environment-dependent matrix population models with forecasts of future sea ice conditions from IPCC general circulation models (GCMs). The matrix population models classified individuals by age and breeding status; mothers and dependent cubs were treated as units. Parameter estimates were obtained from a capture-recapture study conducted from 2001 to 2006. Candidate statistical models allowed vital rates to vary with time and as functions of a sea ice covariate. Model averaging was used to produce the vital rate estimates, and a parametric bootstrap procedure was used to quantify model selection and parameter estimation uncertainty. Deterministic models projected population growth in years with more extensive ice coverage (2001-2003) and population decline in years with less ice coverage (2004-2005). LTRE (life table response experiment) analysis showed that the reduction in ?? in years with low sea ice was due primarily to reduced adult female survival, and secondarily to reduced breeding. A stochastic model with two environmental states, good and poor sea ice conditions, projected a declining stochastic growth rate, log ??s, as the frequency of poor ice years increased. The observed frequency of poor ice years since 1979 would imply log ??s ' - 0.01, which agrees with available (albeit crude) observations of population size. The stochastic model was linked to a set of 10 GCMs compiled by the IPCC; the models were chosen for their ability to reproduce historical observations of sea ice and were forced with "business as usual" (A1B) greenhouse gas emissions. The resulting stochastic population projections showed drastic declines in the polar bear population by the end of the 21st century. These projections were instrumental in the decision to list the polar bear as a threatened species under the U.S. Endangered Species Act. ?? 2010 by the Ecological Society of America.

Ecology↗

USGS2013

Counting at low concentrations: the statistical challenges of verifying ballast water discharge standards

Discharge from the ballast tanks of ships is one of the primary vectors of nonindigenous species in marine environments. To mitigate this environmental and economic threat, international, national, and state entities are establishing regulations to limit the concentration of living organisms that may be discharged from the ballast tanks of ships. The proposed discharge standards have ranged from zero detectable organisms to <10 organisms/m 3 . If standard sampling methods are used, verifying whether ballast discharge complies with these stringent standards will be challenging due to the inherent stochasticity of sampling. Furthermore, at low concentrations, very large volumes of water must be sampled to find enough organisms to accurately estimate concentration. Despite these challenges, adequate sampling protocols comprise a critical aspect of establishing standards because they help define the actual risk level associated with a standard. A standard that appears very stringent may be effectively lax if it is paired with an inadequate sampling protocol. We describe some of the statistical issues associated with sampling at low concentrations to help regulators understand the uncertainties of sampling as well as to inform the development of sampling protocols that ensure discharge standards are adequately implemented.

Ecological Applications↗

USGS2012

Hydrologic and landscape database for the Cache and White River National Wildlife Refuges and contributing watersheds in Arkansas, Missouri, and Oklahoma

A hydrologic and landscape database was developed by the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, for the Cache River and White River National Wildlife Refuges and their contributing watersheds in Arkansas, Missouri, and Oklahoma. The database is composed of a set of ASCII files, Microsoft Access® files, Microsoft Excel® files, an Environmental Systems Research Institute (ESRI) ArcGIS® geodatabase, ESRI ArcGRID® raster datasets, and an ESRI ArcReader® published map. The database was developed as an assessment and evaluation tool to use in examining refuge-specific hydrologic patterns and trends as related to water availability for refuge ecosystems, habitats, and target species; and includes hydrologic time-series data, statistics, and hydroecological metrics that can be used to assess refuge hydrologic conditions and the availability of aquatic and riparian habitat. Landscape data that describe the refuge physiographic setting and the locations of hydrologic-data collection stations are also included in the database. Categories of landscape data include land cover, soil hydrologic characteristics, physiographic features, geographic and hydrographic boundaries, hydrographic features, regional runoff estimates, and gaging-station locations. The database geographic extent covers three hydrologic subregions—the Lower Mississippi–St Francis (0802), the Upper White (1101), and the Lower Arkansas (1111)—within which human activities, climatic variation, and hydrologic processes can potentially affect the hydrologic regime of the refuges and adjacent areas. Database construction has been automated to facilitate periodic updates with new data. The database report (1) serves as a user guide for the database, (2) describes the data-collection, data-reduction, and data-analysis methods used to construct the database, (3) provides a statistical and graphical description of the database, and (4) provides detailed information on the development of analytical techniques designed to assess water availability for ecological needs.

Arkansas;Missouri;Oklahoma↗

USGS2013

Evaluating model structure adequacy: The case of the Maggia Valley groundwater system, southern Switzerland

Model adequacy is evaluated with alternative models rated using model selection criteria (AICc, BIC, and KIC) and three other statistics. Model selection criteria are tested with cross-validation experiments and insights for using alternative models to evaluate model structural adequacy are provided. The study is conducted using the computer codes UCODE_2005 and MMA (MultiModel Analysis). One recharge alternative is simulated using the TOPKAPI hydrological model. The predictions evaluated include eight heads and three flows located where ecological consequences and model precision are of concern. Cross-validation is used to obtain measures of prediction accuracy. Sixty-four models were designed deterministically and differ in representation of river, recharge, bedrock topography, and hydraulic conductivity. Results include: (1) What may seem like inconsequential choices in model construction may be important to predictions. Analysis of predictions from alternative models is advised. (2) None of the model selection criteria consistently identified models with more accurate predictions. This is a disturbing result that suggests to reconsider the utility of model selection criteria, and/or the cross-validation measures used in this work to measure model accuracy. (3) KIC displayed poor performance for the present regression problems; theoretical considerations suggest that difficulties are associated with wide variations in the sensitivity term of KIC resulting from the models being nonlinear and the problems being ill-posed due to parameter correlations and insensitivity. The other criteria performed somewhat better, and similarly to each other. (4) Quantities with high leverage are more difficult to predict. The results are expected to be generally applicable to models of environmental systems.

Water Resources Research↗

USGS2023

Revealing the extent of sea otter impacts on bivalve prey through multi-trophic monitoring and mechanistic models

Sea otters are apex predators that can exert considerable influence over the nearshore communities they occupy. Since facing near extinction in the early 1900s, sea otters are making a remarkable recovery in Southeast Alaska, particularly in Glacier Bay, the largest protected tidewater glacier fjord in the world. The expansion of sea otters across Glacier Bay offers both a challenge to monitoring and stewardship and an unprecedented opportunity to study the top-down effect of a novel apex predator across a diverse and productive ecosystem. Our goal was to integrate monitoring data across trophic levels, space, and time to quantify and map the predator–prey interaction between sea otters and butter clams Saxidomus gigantea , one of the dominant large bivalves in Glacier Bay and a favoured prey of sea otters. We developed a spatially-referenced mechanistic differential equation model of butter clam dynamics that combined both environmental drivers of local population growth and estimates of otter abundance from aerial survey data. We embedded this model in a Bayesian statistical framework and fit it to clam survey data from 43 intertidal and subtidal sites across Glacier Bay. Prior to substantial sea otter expansion, we found that butter clam density was structured by an environmental gradient driven by distance from glacier (represented by latitude) and a quadratic effect of current speed. Estimates of sea otter attack rate revealed spatial heterogeneity in sea otter impacts and a negative relationship with local shoreline complexity. Sea otter exploitation of productive butter clam habitat substantially reduced the abundance and altered the distribution of butter clams across Glacier Bay, with potential cascading consequences for nearshore community structure and function. Spatial variation in estimated sea otter predation processes further suggests that community context and local environmental conditions mediate the top-down influence of sea otters on a given prey. Overall, our framework provides high-resolution insights about the interaction among components of this food web and could be applied to a variety of other systems involving invasive species, epidemiology or migration.

Journal of Animal Ecology↗

USGS2020

Assessment of methods for soil monitoring in the Adirondack region of New York

Repeated sampling to detect changes in forest soils was rarely used before 1990, but the value of soil monitoring in understanding environmental change is becoming well established. The growing number of resampling studies has shown that sampling designs and procedures must be adapted to the objectives of the monitoring program and the soils being monitored. In the Adirondack region, current priorities include the response of soils to large increases, and more recently, large decreases in acidic deposition, and changes driven by trending climate such as altered pools of soil organic carbon, as well as other unforeseen factors that will occur in the future. To improve methods and assess the feasibility of long-term soil monitoring in the Adirondack region, the United States Geological Survey (USGS) conducted a pilot project to evaluate a new sampling method for characterizing soils on a watershed basis. Results obtained with this new approach, referred to as the ADK sampling method, was compared to methods used in previous sampling conducted in 2004 as part of the Western Adirondack Stream Survey (WASS), and also to previous high-replication pit sampling in the North and South Tributary watersheds of Buck Creek (North Buck and South Buck). The number of sampling locations and spatial distribution of sampling points within watersheds differed among the methods, although pit excavation was used to obtain samples in all cases. In addition, this investigation evaluated the use of small diameter corers as a means to measure forest floor mass with greater accuracy and precision than commonly used methods such as pit excavation. Sufficient statistical power to detect ecologically relevant changes in upper profile horizons (Oe, Oa and upper 10 cm of the B) were achieved with the ADK sampling method that utilized 18 pit excavations per watershed. The sampling locations were organized within each watershed into three study areas (six sampling locations per study area) that represented the primary types of landscape within the watershed. Sampling at 18 locations per watershed was found to be nearly as effective at detecting changes as sampling at 28 locations per watershed. Numerous significant changes (P < 0.10) were detected with both 18 and 28 sampling locations at sampling intervals of 12 to 16 years. The relationship between soil data obtained with the ADK method and stream chemistry at the base of the watershed suggested that this approach adequately characterized soil variability within the watershed for the purpose of studying soil-stream linkages. Significant changes in upper B horizon calcium (P < 0.10) and Oa horizon aluminum (P < 0.01) were detected when the data from the four WASS watersheds were combined with the two Buck Creek watersheds, which suggested that there would be value in resampling other WASS watersheds previously sampled in 2004 to support a regional assessment. Study results support small diameter cores as a useful method to monitor changes in the organic matter mass of the forest floor. This method showed high reproducibility in repeated sampling tests and lower spatial variability in sample data than traditional approaches when compared on a watershed basis. Soil coring is also faster and requires less equipment than pit excavation methods, which makes it more conducive to sampling over large areas. However, organic matter mass of the forest floor determined by coring was consistently less than the values obtained by the ADK sampling method that used pit sampling and vertical horizon measurements, and also literature values of a previous Adirondack study that utilized pit sampling in which the entire horizon was collected over a measured area. However, a high correlation (R2 = 0.87) occurred between organic matter content (expressed as Mg ha-1) determined by coring and the ADK sampling method. Differing methods with regard to where sample could be collected, and how organic matter was collected for chemical analysis were the likely reasons for differences in quantification of forest floor organic mass. Collection of forest floor cores in conjunction with the ADK method is recommended to provide improved sensitivity in detecting changes in the forest floor in proximity of where full analyses of the soil profile are being done. This duel sampling approach represents an optimized method for measuring and understanding how Adirondack soils will change in the future.

New York↗

USGS2015

Individual heterogeneity in growth and age at sexual maturity: A gamma process analysis of capture–mark–recapture data

Knowledge of organisms’ growth rates and ages at sexual maturity is important for conservation efforts and a wide variety of studies in ecology and evolutionary biology. However, these life history parameters may be difficult to obtain from natural populations: individuals encountered may be of unknown age, information on age at sexual maturity may be uncertain and interval-censored, and growth data may include both individual heterogeneity and measurement errors. We analyzed mark–recapture data for Red-backed Salamanders ( Plethodon cinereus ) to compare sex-specific growth rates and ages at sexual maturity. Aging of individuals was made possible by the use of a von Bertalanffy model of growth, complemented with models for interval-censored and imperfect observations at sexual maturation. Individual heterogeneity in growth was modeled through the use of Gamma processes. Our analysis indicates that female P. cinereus mature earlier and grow more quickly than males, growing to nearly identical asymptotic size distributions as males.

Journal of Agricultural, Biological, and Environme↗

USGS2016

A goodness-of-fit test for occupancy models with correlated within-season revisits

Occupancy modeling is important for exploring species distribution patterns and for conservation monitoring. Within this framework, explicit attention is given to species detection probabilities estimated from replicate surveys to sample units. A central assumption is that replicate surveys are independent Bernoulli trials, but this assumption becomes untenable when ecologists serially deploy remote cameras and acoustic recording devices over days and weeks to survey rare and elusive animals. Proposed solutions involve modifying the detection-level component of the model (e.g., first-order Markov covariate). Evaluating whether a model sufficiently accounts for correlation is imperative, but clear guidance for practitioners is lacking. Currently, an omnibus goodnessof- fit test using a chi-square discrepancy measure on unique detection histories is available for occupancy models (MacKenzie and Bailey, Journal of Agricultural, Biological, and Environmental Statistics, 9, 2004, 300; hereafter, MacKenzie– Bailey test). We propose a join count summary measure adapted from spatial statistics to directly assess correlation after fitting a model. We motivate our work with a dataset of multinight bat call recordings from a pilot study for the North American Bat Monitoring Program. We found in simulations that our join count test was more reliable than the MacKenzie–Bailey test for detecting inadequacy of a model that assumed independence, particularly when serial correlation was low to moderate. A model that included a Markov-structured detection-level covariate produced unbiased occupancy estimates except in the presence of strong serial correlation and a revisit design consisting only of temporal replicates. When applied to two common bat species, our approach illustrates that sophisticated models do not guarantee adequate fit to real data, underscoring the importance of model assessment. Our join count test provides a widely applicable goodness-of-fit test and specifically evaluates occupancy model lack of fit related to correlation among detections within a sample unit. Our diagnostic tool is available for practitioners that serially deploy survey equipment as a way to achieve cost savings.

Ecology and Evolution↗

USGS2009

Disease, predation and demography: Assessing the impacts of bovine tuberculosis on African buffalo by monitoring at individual and population levels

Summary Understanding the effects of disease is critical to determining appropriate management responses, but estimating those effects in wildlife species is challenging. We used bovine tuberculosis (BTB) in the African buffalo Syncerus caffer population of Kruger National Park, South Africa, as a case study to highlight the issues associated with estimating chronic disease effects in a long‐lived host. We used known and radiocollared buffalo, aerial census data, and a natural gradient in pathogen prevalence to investigate if: (i) at the individual level, BTB infection reduces reproduction; (ii) BTB infection increases vulnerability to predation; and (iii) at the population level, increased BTB prevalence causes reduced population growth. There was only a marginal reduction in calving success associated with BTB infection, as indexed by the probability of sighting a known adult female with or without a calf ( P = 0·065). Since 1991, BTB prevalence increased from 27 to 45% in the southern region and from 4 to 28% in the central region of Kruger National Park. The prevalence in the northern regions was only 1·5% in 1998. Buffalo population growth rates, however, were neither statistically different among regions nor declining over time. Lions Panthera leo did not appear to preferentially kill test‐positive buffalo. The best (Akaike's Information Criterion corrected for small sample size) AIC c model with BTB as a covariate [exp(β) = 0·49; 95% CI = (0·24–1·02)] suggested that the mortality hazard for positive individuals was no greater than for test‐negative individuals. Synthesis and applications . Test accuracy, time‐varying disease status, and movement among populations are some of the issues that make the detection of chronic disease impacts challenging. For these reasons, the demographic impacts of bovine tuberculosis in the Kruger National Park remain undetectable despite 6 years of study on known individuals and 40 years of population counts. However, the rainfall and forage conditions during this study were relatively good and the impacts of many chronic diseases may be a non‐linear function of environmental conditions such that they are only detectable in stressful periods.

Kruger National Park↗

USGS2011

Local richness along gradients in the Siskiyou herb flora: R. H. Whittaker revisited

In his classic study in the Siskiyou Mountains (Oregon, USA), one of the most botanically rich forested regions in North America, R. H. Whittaker (1960 ) foreshadowed many modern ideas on the multivariate control of local species richness along environmental gradients related to productivity. Using a structural equation model to analyze his data, which were never previously statistically analyzed, we demonstrate that Whittaker was remarkably accurate in concluding that local herb richness in these late‐seral forests is explained to a large extent by three major abiotic gradients (soils, topography, and elevation), and in turn, by the effects of these gradients on tree densities and the numbers of individual herbs. However, while Whittaker also clearly appreciated the significance of large‐scale evolutionary and biogeographic influences on community composition, he did not fully articulate the more recent concept that variation in the species richness of local communities could be explained in part by variation in the sizes of regional species pools. Our model of his data is among the first to use estimates of regional species pool size to explain variation in local community richness along productivity‐related gradients. We find that regional pool size, combined with a modest number of other interacting abiotic and biotic factors, explains most of the variation in local herb richness in the Siskiyou biodiversity hotspot.

Oregon↗

USGS2018

Landscape-scale variation in canopy water content of giant sequoias during drought

Recent drought (2012–2016) caused unprecedented foliage dieback in giant sequoias ( Sequoiadendron giganteum ), a species endemic to the western slope of the southern Sierra Nevada in central California. As part of an effort to understand and map sequoia response to droughts, we studied the patterns of remotely sensed canopy water content (CWC), both within and among sequoia groves in two successive years during the drought period (2015 and 2016). Our aims were: (1) to quantify giant sequoia responses to severe drought stress at a landscape scale using CWC as an indicator of crown foliage status, and (2) to estimate the effect of environmental correlates that mediate CWC change within and among giant sequoia groves. We utilized airborne high fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) data from the Carnegie Airborne Observatory to assess giant sequoia foliage status during 2015 and 2016 of the 2012–2016 droughts. A series of statistical models were generated to classify giant sequoias and to map their location in Sequoia and Kings Canyon National Parks (SEKI) and vicinity. We explored the environmental correlates and the spatial patterns of CWC change at the landscape scale. The mapped CWC was highly variable throughout the landscape during the two observation years, and proved to be most closely related to geological substrates, topography, and site-specific water balance. While there was an overall net gain in sequoia CWC between 2015 and 2016, certain locations (lower elevations, steeper slopes, areas more distant from surface water sources, and areas with greater climate water deficit) showed CWC losses. In addition, we found greater CWC loss in shorter sequoias and those growing in areas with lower sequoia stem densities. Our results suggest that CWC change indicates sequoia response to droughts across landscapes. Long-term monitoring of giant sequoia CWC will likely be useful for modeling and predicting their population-level response to future climate change.

California↗