292 research outputs found
What's Normal? -- Temperature, Gender, and Heart Rate
No full textThis article, created by Allen L. Shoemaker of Calvin College, describes a dataset on body temperature, gender, and heart rate. The data is taken from a paper in the "Journal of the American Medical Association" that examined whether humans' true body temperature was 98.6 degrees. It addresses concepts like true means, confidence intervals, t-statistics, t-tests, the normal distribution, and regression. The author states that "it helps students to grasp concepts about true means, confidence intervals and t-statistics." This is a nice introduction into how statistics can be used in the medical field
Barform deposits of the Carolyn Shoemaker formation, Gale crater, Mars
No full textInternational audienceThe early environmental history of Mars is encoded in the planet's record of sedimentary rocks. Since 2012, the Curiosity rover has been ascending Mount Sharp, Gale crater's central mound, making detailed observations of sedimentary strata exposed there. The primary depositional setting represented by the rocks examined thus far has been a perennial lake, represented by the mudstones and sandstone lenses of the Murray formation. Here, we report on the sedimentology of outcrops examined in the Carolyn Shoemaker formation, which sits stratigraphically above the Murray formation. We interpret strata exposed in the Glasgow and Mercou members of the Carolyn Shoemaker formation to represent river bars in ancient alluvial and shoreline settings based on sedimentary structures, stratal geometries measured from photogrammetric data, and erosional morphology. The transition from a lacustrine to a fluvial depositional setting records the aggradation and progradation of coastal rivers into what was previously the extent of the Gale lake system. This may have occurred due to the shrinking of the lake over time due to climate-driven changes in the basin water balance, or local three-dimensionality in shoreline evolution, such as the formation of a new sedimentary lobe following a channel switch
Geography of current and future global mammal extinction risk.
No full textIdentifying which species are at greatest risk, what makes them vulnerable, and where they are distributed are central goals for conservation science. While knowledge of which factors influence extinction risk is increasingly available for some taxonomic groups, a deeper understanding of extinction correlates and the geography of risk remains lacking. Here, we develop a predictive random forest model using both geospatial and mammalian species' trait data to uncover the statistical and geographic distributions of extinction correlates. We also explore how this geography of risk may change under a rapidly warming climate. We found distinctive macroecological relationships between species-level risk and extinction correlates, including the intrinsic biological traits of geographic range size, body size and taxonomy, and extrinsic geographic settings such as seasonality, habitat type, land use and human population density. Each extinction correlate exhibited ranges of values that were especially associated with risk, and the importance of different risk factors was not geographically uniform across the globe. We also found that about 10% of mammals not currently recognized as at-risk have biological traits and occur in environments that predispose them towards extinction. Southeast Asia had the most actually and potentially threatened species, underscoring the urgent need for conservation in this region. Additionally, nearly 40% of currently threatened species were predicted to experience rapid climate change at 0.5 km/year or more. Biological and environmental correlates of mammalian extinction risk exhibit distinct statistical and geographic distributions. These results provide insight into species-level patterns and processes underlying geographic variation in extinction risk. They also offer guidance for future conservation research focused on specific geographic regions, or evaluating the degree to which species-level patterns mirror spatial variation in the pressures faced by populations within the ranges of individual species. The added impacts from climate change may increase the susceptibility of at-risk species to extinction and expand the regions where mammals are most vulnerable globally
<i>Current Trends in Wildlife Research</i>. Wildlife Research Monographs. Edited by Rafael Mateo, Beatriz Arroyo, and Jesus T. Garcia. Cham (Switzerland): Springer. 119.00 (ebook). x + 293 p.; ill.; no index. ISBN: 978-3-319-27910-7 (hc); 978-3-319-27912-1 (eb). 2016.
No full textEarly Settlement in Marginal Habitats on Santa Rosa Island, California
No full textAt low population density with abundant resource availability, the factors influencing human settlement patterns may differ from those that are important when per capita resources are more limited. For this reason, ecological models that may be supported by archaeological data for the majority of human occupation of an area may need modification or an alternative model for specific contexts of low population. This is the case on California’s Northern Channel Islands (NCI), the location of consistent human occupation since 13,000 years ago and permanent settlement since at least 8,000 years ago. The radiocarbon record from Santa Rosa Island, the second-largest of these islands, indicates that while settlement was largely influenced by the abundance of subsistence resources, including access to shellfish, fish, and water, this may not have been the case early in time, when population densities were low. In particular, I develop a methodology geared toward assessing population densities and the chronology of occupation on the leeward south side of Santa Rosa Island, between Johnson’s Lee and Ford Point. In this thesis, I note deviations from the predictions of previous Ideal Free Distribution (IFD) models used on the NCI for low population density, and develop an alternative satisficing model for that context. It incorporates the following data sets within the context of broader radiocarbon and faunal records established for settlement of Santa Rosa Island: (1) radiocarbon dates associated with occupation on the south side of the island; and (2) the faunal record at sites excavated from relatively low-ranked habitats on the island. The earliest available evidence for settlement of the south coast of the island dates to the beginning of the Early Period (7450 cal BP), with settlement in the region expanding to more sites over time. These dates appear to be anomalously early, with initial dates for settlement of the south predating many locations ranked higher in previous ecological models. This indicates that at low population densities when most or all habitats contained sufficient resources to comfortably support the inhabitants, settlement may not have been focused on areas with the most abundant resources. Consistent with the standard predictions of the IFD, however, settlement along the low-ranked south coast grows more slowly than at higher-ranked locations elsewhere on the island. This indicates that after this initial phase of occupation as human populations on Santa Rosa Island increased, settlement patterns no longer fit this satisficing model, but rather the IFD, as has been shown in previous studies. At the regional level, this project clarifies perspectives on large-scale patterns of settlement and mobility and human responses to major climatic shifts. It will also give insight for land-use adaptations in contexts globally in which low population densities are low and settlement appears not to follow expectations based on maximizing resource return
Characterizing Habitat and Densities of the Mojave Desert Tortoise (Gopherus agassizii) at Multiple Spatial Scales
No full textDetermining what environmental and anthropogenic factors have the greatest influence on the distribution of the Mojave desert tortoise (Gopherus agassizii) is key to improving population estimates and better understanding how this species uses their habitat around them. The Mojave Desert tortoise is a federally listed threated species and a key component to their delisting is ensuring that they are well distributed throughout their range. Currently range wide surveys of desert tortoises are conducted over large regions of suitable habitat with little consideration of the patchy nature of their distribution. Models were constructed to evaluate influences on desert tortoise densities across their range and are capable of informing conservation managers on where to conduct surveys in the future as well as what habitat to allocate for protection based on desert tortoise habitat preferences. At the range wide scale satellite data is readily available for input into models; however, when evaluating tortoise densities at smaller scales satellite derived remote sensing imagery proves to be too coarse for analyses at these scales. Remote sensing imagery derived from unmanned aerial vehicles (UAV) has recently become a viable option for obtaining data at these scales for various types of analyses. With high resolution imagery obtained with UAVs density models were constructed to evaluate influences on tortoise densities at local scales and with this I was also able to evaluate if tortoise habitat preferences differ amongst scales and regions. Detailed plant and soil data are collected at these small scales through field methods such as the Assessment Inventory and Monitoring (AIM) protocol but do not have the ability to capture the heterogeneity across the landscape; thus, UAV derived imagery has the potential to bridge the gap between satellite derived imagery and field collected data. Here I’ve shown that UAVs can capture a more accurate representation of shrub cover than field methods such as the AIM protocol. Shrub cover is important to Mojave desert tortoise habitat as it provides protection from high temperatures and predators. Though the UAV imagery proved useful for obtaining shrub cover, data collected through field methods will still be necessary for obtaining specific plant and soil data as well as for calibration with remotely sensed imagery. The density models constructed at both the range wide and local scales revealed that desert tortoises do show preferences in habitat selection and these preferences vary from region to region and amongst scales. On the other hand, more work is needed to improve the types of data available for collection with UAVs. These results demonstrate the importance of evaluating Mojave desert tortoise densities at different scales and with data collected through various different means
Integrating multiple sign types to improve occupancy estimation for inconspicuous species: a case study of American pika in the Pacific Northwest
No full textAs rapid global environmental change continues to affect populations through a range of mechanisms, a clear understanding of species responses to these threats is increasingly critical for conservation and management. Occupancy models have emerged as one of the most powerful tools to investigate regional population trends and dynamics, range shifts and habitat associations. Standard occupancy models are capable of producing unbiased estimates of occupancy and its environmental drivers by allowing for observation errors �" in particular, the failure to observe a target species when present (false-negative detection error). Use of indirect sign (e.g., scat, tracks) can vastly improve sample size and reduce costs for inconspicuous focal species, but can also introduce additional sources of observation error that may bias estimates of occupancy, including incorrect identification (false-positive detection error). Reliance on multiple unique sign types may introduce additional sources of bias if these sign types vary in re-liability or if their relative reliability changes under differing environmental conditions. A ‘multi-sign’ occupancy approach, which models the detection process separately for each unique sign type, may therefore improve our ability to generate unbiased estimates of occupancy and its environmental drivers for inconspicuous species like the American pika (Ochotona princeps). In this study, we modeled occupancy dynamics for American pika using multiple direct and indirect indicators of pika presence (fresh scat, fresh haypiles, pika calls and pika sightings) collected from 2010 to 2021 at five national parks in the Pacific Northwest. Furthermore, we investigated how estimates of pika occupancy trends and environmental drivers differ under three increasingly realistic representations of the pika observation process: (1) perfect detection (a common assumption for modeling pika occupancy), (2) standard occupancy model (single observation process with no possibility of false detection), (3) multi-sign with no false detections (non-false positive model), and (4) multi-sign with false detections (full model). For the multi-sign occupancy models, we modeled each observation process separately as a function of climatic and environmental covariates including substrate complexity, season, survey period and vegetation cover. In addition, we modeled each occupancy process (initial patch occupancy, colonization, and extinction) separately as a function of temperature, precipitation, forb, rock and shrub cover. Results from all three models indicated that annual occupancy across parks was relatively stable during our study period, ranging from 32% to 40% and exhibited a general increase from 2010 to 2014 and a weak decline from 2017 to 2021. All models also indicated that that forb, shrub and rock cover positively influenced colonization rates at the plot level and forb cover negatively influenced extinction. Consistent with previous research, detection rates were high in our study, averaging 82% across all study sites. False detection rates were higher than expected, averaging 6.8%. Both true and false detection probabilities exhibited substantial variation across the five national parks in our study, with true detection rates varying regionally from 77.1% to 92.4% and false positive rates varying from 11.7% to 2.8%. Estimates of occupancy processes and their environmental drivers were highly sensitive to different representations of the detection process. For example, initial occupancy rate for the non-false positive model was 8% higher than the full model, and the effect of forb cover was much stronger in the full model versus the perfect detection and non-false positive models. Overall, we demonstrated that a “multi-sign” approach to dynamic occupancy modeling and the inclusion of false detection errors have strong potential to generate more robust estimates of occupancy dynamics for inconspicuous species than standard occupancy modeling approaches
Geolocators as Tools for Inferring Waterfowl Movements and Breeding Phenology in the Pacific Flyway
No full textUnderstanding the geographical extent and timing of wildlife movements enables resource managers to meet the habitat needs of target species efficiently and effectively. Historically, information about waterfowl movements has been derived from mark-recovery data in which birds are trapped and marked with metal leg bands that are subsequently reported via hunter harvest. Such banding efforts typically yield two data points per individual (banding location and harvest location), and are poorly suited for estimating migration paths, stopover sites and timing of movements. Here, we use light-level geolocators �" which enable researchers to track individual locations on the basis of estimated daily twilight times �" to build a more complete understanding of the geography and timing of migratory movements for canvasbacks (Aythya valisineria) in the Pacific Flyway. During 2015-2017, 151 geolocators were placed on canvasbacks using two alternative attachment methods (leg-band vs. nasal-saddle mounts) during spring migration (February - March) near Reno, NV. Five of these geolocators (three males and two female) were successfully recovered from hunters. Four of the five tagged canvasbacks (2 males and 2 female) migrated to breeding sites in southern Canada (Alberta and Saskatchewan, via stopover sites in Utah and Montana), while one male migrated to a breeding site in Alaska. After initial capture in early spring, two canvasbacks (one Canada-breeding male and the Alaska-breeding male) remained near the capture site in Western Nevada or California for over a month (40 and 77 days, respectively), until resuming migration in late March or April. During spring migration, canvasbacks made an average of 3 stops, with an average stopover duration of 18±4 days. Three canvasbacks made a distinct molt migration after breeding. For fall migration, the canvasbacks made an average of 2.3±0.7 stops, lasting an average of 7.1±1.3 days, on their way to wintering sites in California’s Central Valley and San Francisco Bay area. Recovery of nasal saddle-mounted geolocators was significantly lower than leg band-mounted devices. This study demonstrates the value of geolocators for assessing year-round habitat use for waterfowl populations. This information complements standard band-recovery approaches, and enables waterfowl managers to ensure that the spatial and temporal distributions of individuals are identified so that habitat conservation efforts reflect the full annual habitat use cycle.Avian ecologists typically undertake costly and time intensive nest surveys to estimate key parameters such as nesting propensity, clutch size, nest success and hatching success. However, nesting surveys can be prohibitively difficult and/or costly in many cases. Less time consuming and invasive tools to supplement reproductive parameter estimates are needed. However, use of geolocators to infer reproductive parameters has not been rigorously validated. We attached light level geolocators to wood ducks (Aix sponsa) in northern Nevada, in a population that also was intensively monitored via artificial nest boxes. To designate each day as either nesting or non-nesting from geolocator data, we used a Bayesian mixture model, enabling estimation of life-history traits such as nesting propensity, annual nesting attempts, incubation duration and nest success. We also attempted to distinguish among nest prospecting, laying, incubation, and brood-rearing behaviors using an unsupervised time-series clustering algorithm, and to further estimate clutch size and nest fate. Using artificial nest box monitoring as validation data, we confirmed the accuracy of geolocator-derived estimates of the number and timing of nesting attempts and nest success. We were unable to confirm the validity of geolocator-derived estimates of clutch size (number of laying days). Using geolocator data, we estimated a nesting propensity of 0.92, with 87% apparent nest success for surviving females (100% success in natural cavities vs. 73% success in artificial nest boxes). Our clustering assignments showed some ability to discriminate among nesting behaviors, but were unable to effectively predict clutch size or nest fate. Our study is the first to rigorously validate the use of incubation length for predicting success of individual nests. We contend that widespread deployment of geolocators on waterfowl and other birds would enable estimates of reproductive parameters for species and populations not amenable to standard nesting surveys, after controlling for nesting season adult female survival
Using population ecology to inform the conservation of Nevada’s rare plants
No full textPopulation ecology is a fundamental tenet of conservation biology. Estimating population size is a necessary first step in assessing species vulnerability, and to the subsequent examination of population viability, prediction of extinction risks, and evaluation of conservation objectives. Population censuses and demographic monitoring are important tools in illuminating overall population trajectories a species face, as well as reveal the mechanisms behind those trends. Plant species are facing a global biodiversity crisis, with almost two in five species of vascular plants at risk for extinction. Rare plants make up a nontrivial portion of that global diversity, but are disproportionately threatened by global change. Therefore, it is critical to effectively estimate population size as well as understand the drivers of rare plant populations in order to protect biodiversity globally. However, because species are often aggregated across landscapes, precisely estimating total population size can be surprisingly challenging. Incorporating this spatial heterogeneity into estimates of population size is necessary to increase confidence in population estimates. Methods in design-based approaches have attempted this by altering sampling designs to account for heterogeneity, however model-based approaches could also be useful to estimate population size of heterogeneously distributed species but have so far not been examined. In chapter 1, I tested the ability of a model-based approach to accurately estimate population size. I simulated several heterogeneous landscapes with various levels of autocorrelation and then sampled those landscapes with differing scenarios of sampling effort. I then used a Gaussian process model in a Bayesian framework to make predictions of population density in unsampled areas. I found that I was successfully able to recover total population size of landscapes that had high to mid-levels of autocorrelation regardless of sampling effort, but decreased sampling effort lead to wider uncertainty around population estimates. These results highlight the promise for model-based approaches utility in estimating population size of sessile species with heterogenous distributions. Future research should combine design and model-based approaches to increase precision of population size estimates.
Understanding the drivers of population trends is important in conservation of rare species, as well as in the ability to anticipate threats in how already vulnerable species may respond to global change. Both disturbance and climate have been shown to affect the population dynamics of plants, but the interaction between the two has been minimally explored in the context of rare alpine plants. In chapter 2, I quantify how ski resort impacts and climate effect the population growth and demographics of a rare alpine endemic, Draba asterophora, across a long-term study conducted from 2010-2024. Although populations in ski areas have experienced past declines as a direct result of ski resort activities, I found that population trends of Draba asterophora in ski areas were not different from those in less developed areas over our study period. On the other hand, the best supported climate model showed negative effects of winter warming and late snowmelt, and positive effects of summer precipitation on population trends, with no interactions with ski area impacts. We observed that these populations are often driven by high survival, with low recruitment. Lastly, we found that despite population growth rates recovering following periods of decline circa 2015-2020, population sizes never fully recovered by the end of the study period. Our results highlight the risk that rare alpine plants face, where populations may be unlikely to recover from increasingly common unfavorable climatic periods
Poems on several occasions. By John Bennet, a journeyman shoemaker [electronic resource].
No full textWith a list of subscribers.Imprint punc. conf. by IEN (NA)Electronic reproduction.English Short Title Catalog,Reproduction of original from British Library
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