Lidar Remote Sensing Variables Predict Breeding Habitat of a Neotropical Migrant Bird

A topic of recurring interest in ecological research is the degree to which vegetation structure influences the distribution and abundance of species. Here we test the applicability of remote sensing, particularly novel use of waveform lidar measurements, for quantifying the habitat heterogeneity of a contiguous northern hardwoods forest in the northeastern United States. We apply these results to predict the breeding habitat quality, an indicator of reproductive output of a well-studied Neotropical migrant songbird, the Black-throated Blue Warbler (Dendroica caerulescens). We found that using canopy vertical structure metrics provided unique information for models of habitat quality and spatial patterns of prevalence. An ensemble decision tree modeling approach (random forests) consistently identified lidar metrics describing the vertical distribution and complexity of canopy elements as important predictors of habitat use over multiple years. Although other aspects of habitat were important, including the seasonality of vegetation cover, the canopy structure variables provided unique and complementary information that systematically improved model predictions. We conclude that canopy structure metrics derived from waveform lidar, which will be available on future satellite missions, can advance multiple aspects of biodiversity research, and additional studies should be extended to other organisms and regions

Response to fragmentation by avian communities is mediated by species traits

Aim The hypothesis that habitat fragmentation negatively influences biodiversity stems from island biogeography and metapopulation theory which predict negative impacts of decreasing patch size on richness and distribution patterns. Empirical support of this idea is weak in terrestrial systems, though tests of fragmentation effects are typically confounded with landscape composition and potentially obscured by imperfect detection. Here, we used multispecies occupancy models and a mensurative experimental design to test competing hypotheses about how forest fragmentation influences distributions of breeding forest bird species and communities. Location Southern Indiana, USA. Methods During the breeding seasons of 2011-2013, we recorded over 80,000 bird detections in 202 forest fragments using a sampling design that isolated the effects of patch size per se from the effects of forest amount within a 2 km radius, edge distance, local vegetation and sampling area. We modelled the effects of these covariates on distributions of individual species categorized by ecological trait groups (i.e., forest, forest interior or forest edge), and evaluated how forest loss and fragmentation impact species richness. Results Though our results indicated little effect of patch size on total species richness, decreasing patch size had a negative effect on interior species, and a positive effect on edge species. The effects of total forest amount were much more variable, and surprisingly had a negative influence on many species, particularly cavity nesters. Main conclusions Our results do not support theoretical predictions that forest patch size should positively influence bird species richness. However, composition of bird communities shifted towards edge species from interior species with decreasing patch size. Maintaining large forest patches is thus critical for supporting forest interior species, which tend to be of the greatest conservation concern

The Detergent Evaluation Methods and the Washing Machine(PART II)

AIC model selection table and associated coefficients for hermit warbler 2013 for all models combined. Column names for the model coefficients use the following notation: coefficient = parameter(covariate) and standard error = SEparameter(covariate). Parameter abbreviations are p = detection probability, psi = initial occupancy, col = colonization/settlement, ext = extinction/vacancy. Parameter(Int) refers to the intercept. ‘nPars’ is the number of parameters estimated in the model. Each model is ranked by its AIC score, which represents how well the model fits the data. A lower ∆AIC (delta) value is indicative of a better model. The probability that the model (of the models tested) would best explain the data is indicated by AICwt

Information-Mediated Allee Effects in Breeding Habitat Selection

Social information is used widely in breeding habitat selection and provides an efficient means for individuals to select habitat, but the population-level consequences of this process are not well explored. At low population densities, efficiencies may be reduced because there are insufficient information providers to cue high-quality habitat. This constitutes what we call an information-mediated Allee effect. We present the first general model for an information-mediated Allee effect applied to breeding habitat selection and unify personal and social information, Allee effects, and ecological traps into a common framework. In a second model, we consider an explicit mechanism of social information gathering through prospecting on conspecific breeding performance. In each model, we independently vary personal and social information use to demonstrate how dependency on social information may result in either weak or strong Allee effects that, in turn, affect population extinction risk. Abrupt transitions between outcomes can occur through reduced information transfer or small changes in habitat composition. Overall, information-mediated Allee effects may produce positive feedbacks that amplify population declines in species that are already experiencing environmentally driven stressors, such as habitat loss and degradation. Alternatively, social information has the capacity to rescue populations from ecological traps.This is the publisher’s final pdf. The published article is copyrighted by University of Chicago Press and can be found at: http://www.journals.uchicago.edu/doi/10.1086/683659. Dryad data available online at: http://dx.doi.org/10.5061/dryad.k4c21Keywords: Allee threshold, habitat selection, ecological trap, Allee effect, social informatio

Taxonomic voucher specimens for study of bee communities in intensively managed Douglas-fir forests in the Oregon Coast Range

Understanding how pollinators respond to anthropogenic land use is key to conservation of biodiversity and ecosystem services, but few studies have addressed this topic in coniferous forests, particularly those managed intensively for wood production. This study reports on voucher material generated as part of Zitomer et al. (2023), that assessed changes in wild bee communities with time since harvest in 60 intensively managed Douglas-fir (Pseudotsuga menziesii) stands in the Oregon Coast Range across a gradient in stand age spanning a typical harvest rotation (0-37 years post-harvest). We additionally assessed relationships of bee diversity and community composition to relevant habitat features, including availability of floral resources and nest sites, understory vegetation characteristics, and composition of the surrounding landscape. Specimens were collected using a combination of passive sampling methods-blue vane traps and white, blue, and yellow bowl traps- and hand-netting and were identified to the lowest possible taxonomic level by A.R. Moldenke and L.R. Best. Four hundred and ten taxonomic voucher specimens were deposited into the Oregon State Arthropod Collection (Accession# OSAC_AC_2023_01_09-001-01) to serve as a reference for future research

第829回千葉医学会例会・第8回千葉精神科集談会

AIC model selection table and associated coefficients for Hammond's flycatcher 2012 for all models combined. Column names for the model coefficients use the following notation: coefficient = parameter(covariate) and standard error = SEparameter(covariate). Parameter abbreviations are p = detection probability, psi = initial occupancy, col = colonization/settlement, ext = extinction/vacancy. Parameter(Int) refers to the intercept. ‘nPars’ is the number of parameters estimated in the model. Each model is ranked by its AIC score, which represents how well the model fits the data. A lower ∆AIC (delta) value is indicative of a better model. The probability that the model (of the models tested) would best explain the data is indicated by AICwt

The Role of the L1 in the L2 Classroom

The use of the L1 in L2 classrooms has historically been a controversial issue. Research over the years has greatly influenced the perspectives of the L1 and its purposes in the L2 classroom. In this paper, I will review the traditional views of L1 usage in the L2 classroom as well as discuss the major research studies which have brought new light upon the L1 and its influences on L2 acquisition. The focus of this paper will then shift to the pedagogical implications of such findings and how these findings affect my decisions as a teacher with respect to L1 usage in my classroom

Bird-vegetation associations in thinned and unthinned young Douglas-fir forests 10 years after thinning

Quantitative associations between animals and vegetation have long been used as a basis for conservation and management, as well as in formulating predictions about the influence of resource management and climate change on populations. A fundamental assumption embedded in the use of such correlations is that they remain relatively consistent over time. However, this assumption of stationarity has been rarely tested – even for forest birds, which are frequently considered to be ‘indicator species’ in management operations. We investigated the temporal dynamics of bird-vegetation relationships in young Douglas-fir (Pseudotsuga menziesii) forests over more than a decade following initial anthropogenic disturbance (commercial thinning). We modeled bird occurrence or abundance as a function of vegetation characteristics for eight common bird species for each of six breeding seasons following forest thinning. Generally, vegetation relationships were highly inconsistent in magnitude across years, but remained positive or negative within species. For 3 species, relationships that were initially strong dampened over time. For other species, strength of vegetation association was apparently stochastic. These findings indicate that caution should be used when interpreting weak bird-vegetation relationships found in short-term studies and parameterizing predictive models with data collected over the short term.Keywords: Disturbance, Long-term experiment, Response variability, Young Douglas-fir forests, Bird-vegetation associations, Forest thinnin

A species-centered approach for uncovering generalities in organism responses to habitat loss and fragmentation

Theoretical models predict strong influences of habitat loss and fragmentation on species distributions and demography, but empirical studies have shown relatively inconsistent support across species and systems. We argue that species' responses to landscape-scale habitat loss and fragmentation are likely to appear less idiosyncratic if it is recognized that species perceive the same landscapes in different ways. We present a new quantitative approach that uses species distribution models (SDMs) to measure landscapes (e.g. patch size, isolation, matrix amount) from the perspective of individual species. First, we briefly summarize the few efforts to date demonstrating that once differences in habitat distributions are controlled, consistencies in species' responses to landscape structure emerge. Second, we present a detailed example providing step-by-step methods for application of a species-centered approach using freely available land-cover data and recent statistical modeling approaches. Third, we discuss pitfalls in current applications of the approach and recommend avenues for future developments. We conclude that the species-centered approach offers considerable promise as a means to test whether sensitivity to habitat loss and fragmentation is mediated by phylogenetic, ecological, and life-history traits. Cross-species generalities in responses to habitat loss and fragmentation will be challenging to uncover unless landscape mosaics are defined using models that reflect differing species-specific distributions, functional connectivity, and domains of scale. The emergence of such generalities would not only enhance scientific understanding of biotic processes driving fragmentation effects, but would allow managers to estimate species sensitivities in new regions.this study was supported by US National Science Foundation grants (NSF-ARC-0941748 and DEB-1050954

Leveraging biochemical reactions to unravel functional impacts of cancer somatic variants affecting protein interaction interfaces [version 3; peer review: 2 approved]

Background: Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. Methods: We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures. Results: We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes (i.e. survival time) in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes (e.g. GPCR signalling pathways) containing enriched reactions, providing support for new off-label therapeutic options. Conclusions: In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types (e.g. post-translational modifications) collected for many types of disease