Modeling Fine-scale Avian Distributions and Densities with Multi-scale Models: Predicting the Past and Present

Models of a species’ distribution and models of a species’ spatially explicit density are valuable tools for conservation. They allow researchers to estimate changes in distributions, densities, and populations, based on changing environmental conditions. To trust such estimates, however, the quality of models is exceedingly important. Model performance can be affected by the scale at which the environment is characterized, species-specific characteristics such as prevalence and habitat specialization, or the quality of data collected. High-quality models of current distributions and densities can be used to assess the current status of species over a study extent. With environmental data extrapolated to the past or future, such models can be used to predict changes in species status through time. In Chapter 2, I developed and assessed multi-scale models of species distribution and abundance using the boosted regression tree algorithm. Multi-scale species distribution models consistently performed as well or better than best single-scale models. This multi-scale framework greatly reduces the number of models necessary to account for species’ response to environmental scale and avoids potential mischaracterization of important environmental scales. Hurdle models of abundance, however, performed poorly throughout the study. Fine-scale abundance is more difficult to model accurately than occurrence. Alternative methods may be more appropriate. In Chapter 3, I examined the comparative roles of species’ prevalence, sample size, and habitat specialization, on the performance of species distribution models. Although all three affected model performance, the role of prevalence was minor compared to the role of habitat specialization. I found a species-specific quadratic effect of sample size on model performance where generalist species required larger minimum sample sizes for accurate models. I then used my findings to recommend minimum sample sizes based on habitat specialization. In Chapter 4, I examined the use of occurrence and abundance data from the eBird database for fine-scale species distribution and density modeling. I compared the results of distribution and density models built on eBird data to the results of models built on highly standardized surveys from the Oregon 2020 project. To convert observed abundances in eBird to densities, I used estimates of detection probability from the Oregon 2020 data. Overall, the results of distribution and density models from the two datasets were very similar. I explore the reasons for differences and give guidelines for how to best use eBird data in fine-scale distribution and density modeling. In Chapter 5, I modeled current and historic distributions and densities of seven species of bird in the Willamette Valley, Oregon. I used reconstructed 1850s habitat data to hind-cast densities and distributions to pre-European-American settlement throughout the study area. I estimated current and historic populations for each species as well. I found population declines exceeding declines in suitable grassland and oak habitats for grassland and oak specialists. I found relatively stable populations and suitable habitat for riparian and edge specialists and increases in population and suitable habitat for species associated with coniferous forests. Hind-casted distributions and densities can provide a new baseline for the effects of anthropogenic habitat change on bird populations in the Willamette Valley. My research aims to better the understanding of species distribution and density modeling. I address important topics from the scale at which environmental variables should be characterized to the minimum sample sizes for accurate models and the species’ characteristics that affect that sample size. I build guidelines for fine-scale distribution and density modeling with the popular rapidly growing citizen science eBird database. Finally, I use fine-scale multi-scale models to estimate historic distributions and populations that provide a new baseline for change since European-American settlement

Teaching Bird Identification & Vocabulary with Twitter

Species identification is essential to biology, conservation, and management. The ability to focus on specific diagnostic characteristics of a species helps improve the speed and accuracy of identification. Birds are excellent subjects for teaching species identification because, in combination with their different shapes and sizes, their plumages have distinctive colors and patterns that vary characteristically from species to species. Bird feather tracts have specific names so that proper descriptions of colors and patterns on those tracts can improve the precision and conciseness of identification criteria. We use popular social media (Twitter) to engage students in an exercise designed to familiarize them with avian species identification and improve their use and comprehension of vocabulary. This exercise can be used in higher education for ornithology and other identification courses, as well as in primary education as a basic introduction to species and biodiversity.This is the publisher’s final pdf. The article is copyrighted by National Association of Biology Teachers and published by University of California Press. It can be found at: http://abt.ucpress.edu/Keywords: social media, social networking, Twitter, ornithology, engagement, networking site

Supplemental structured surveys and pre-existing detection models improve fine-scale density and population estimation with opportunistic community science data

Abstract Density and population estimates aid in conservation and stakeholder communication. While free and broadly available community science data can effectively inform species distribution models, they often lack the information necessary to estimate imperfect detection and area sampled, thus limiting their use in fine-scale density modeling. We used structured distance-sampling surveys to model detection probability and calculate survey-specific detection offsets in community science models. We estimated density and population for 16 songbird species under three frameworks: (1) a fixed framework that assumes perfect detection within a specified survey radius, (2) an independent framework that calculates offsets from an independent source, and (3) a calibration framework that calculates offsets from supplemental surveys. Within the calibration framework, we examined the effects of calibration dataset size and data pooling. Estimates of density and population size were consistently biased low in the fixed framework. The independent and calibration frameworks produced reliable estimates for some species, but biased estimates for others, indicating discrepancies in detection probability between structured and community science surveys. The calibration framework produced reliable population estimates with as few as 10 calibration surveys with positive detections. Data pooling dramatically decreased bias. This study provides conservationists and managers with a cost-effective method of estimating density and population

Idiosyncratic changes in spring arrival dates of Pacific Northwest migratory birds

Shifts in the timing of bird migration have been associated with climatic change and species traits. However, climatic change does not affect all species or geographic locations equally. Climate in the Pacific Northwest has shifted during the last century with mean temperatures increasing by 1 °C but little change in total annual precipitation. Few long-term data on migration phenology of birds are available in the Pacific Northwest. We analyzed trends in spring arrival dates from a site in the Oregon Coast Range where nearly daily inventories of birds were conducted in 24 of 29 years. Several species showed statistically significant shifts in timing of first spring arrivals. Six of 18 species occur significantly earlier now than during the initial phase of the study. One species arrives significantly later. Eleven show no significant shifts in timing. We associated trends in spring migration phenology with regional climatic variables, weather (precipitation and temperature), traits of species such as migration strategy, foraging behavior, diet, and habitat use, and regional trends in abundance as indexed by Breeding Bird Survey data. We found no set of variables consistently correlated with avian phenological changes. Post hoc analyses of additional climate variables revealed an association of migratory arrival dates across the 18 species with rainfall totals in northern California, presumably indicating that songbird arrival dates in Oregon are slowed by spring storm systems in California. When only the six species with the most strongly advancing arrival dates were analyzed, winter maximum temperatures in the preceding three winters appeared consistently in top models, suggesting a possible role for food availability early in spring to promote the survival and successful reproduction of the earliest-arriving birds. However, additional data on food availability and avian survival and reproductive success are required to test that hypothesis. Despite the appearance of some climate variables in top models, there remains a mismatch between strongly advancing arrival dates in some songbirds and a lack of clear directional change in those climate variables. We conclude that either some previously unrecognized variable or combination of variables has affected the timing of migration in some species but not others, or the appearance of statistically significant directional changes over time can occur without being driven by consistent environmental or species-specific factors

Dramatic Declines of Evening Grosbeak Numbers at a Spring Migration Stop-Over Site

Evening Grosbeak (Coccothraustes vespertinus) populations have been hypothesized to be in steep decline across North America. Data characterizing long-term changes are needed to quantify the magnitude of the declines. We surveyed grosbeaks at a spring migratory stop-over site in Corvallis, Oregon, USA, where birds gather annually during April and May to feast on elm (Ulmus spp.) seeds before departing to breeding sites. An estimate produced by a statistics professor in the 1970s indicated peak numbers were 150,000 to 250,000 birds. Our surveys in 2013–2015 found annually variable numbers, from a few hundred grosbeaks in the lowest year to less than five thousand birds in the highest year. If the original estimate is approximately true, Evening Grosbeak numbers have experienced dramatic declines, averaging −2.6%/year, over the last four decades. Our local observation of declines during spring aligns with declines documented in winter across North America by bird feeder studies and in summer by the Breeding Bird Survey. We explore potential explanations for the changes in population size, such as influences of spruce budworm outbreaks, disease, and decreased structural diversity of forests owing to harvest practices. We also consider the challenges of interpreting changes in abundance of species with exceptionally variable populations, especially if population fluctuations or cycles may have long periodicities. Finally, we call for additional planned surveys to track the numbers of this enigmatic and charismatic species

Dramatic Declines of Evening Grosbeak Numbers at a Spring Migration Stop-Over Site

Evening Grosbeak (Coccothraustes vespertinus) populations have been hypothesized to be in steep decline across North America. Data characterizing long-term changes are needed to quantify the magnitude of the declines. We surveyed grosbeaks at a spring migratory stop-over site in Corvallis, Oregon, USA, where birds gather annually during April and May to feast on elm (Ulmus spp.) seeds before departing to breeding sites. An estimate produced by a statistics professor in the 1970s indicated peak numbers were 150,000 to 250,000 birds. Our surveys in 2013–2015 found annually variable numbers, from a few hundred grosbeaks in the lowest year to less than five thousand birds in the highest year. If the original estimate is approximately true, Evening Grosbeak numbers have experienced dramatic declines, averaging −2.6%/year, over the last four decades. Our local observation of declines during spring aligns with declines documented in winter across North America by bird feeder studies and in summer by the Breeding Bird Survey. We explore potential explanations for the changes in population size, such as influences of spruce budworm outbreaks, disease, and decreased structural diversity of forests owing to harvest practices. We also consider the challenges of interpreting changes in abundance of species with exceptionally variable populations, especially if population fluctuations or cycles may have long periodicities. Finally, we call for additional planned surveys to track the numbers of this enigmatic and charismatic species

StatEcoNet: Statistical Ecology Neural Networks for Species Distribution Modeling

This paper focuses on a core task in computational sustainability and statistical ecology: species distribution modeling (SDM). In SDM, the occurrence pattern of a species on a landscape is predicted by environmental features based on observations at a set of locations. At first, SDM may appear to be a binary classification problem, and one might be inclined to employ classic tools (e.g., logistic regression, support vector machines, neural networks) to tackle it. However, wildlife surveys introduce structured noise (especially under-counting) in the species observations. If unaccounted for, these observation errors systematically bias SDMs. To address the unique challenges of SDM, this paper proposes a framework called StatEcoNet. Specifically, this work employs a graphical generative model in statistical ecology to serve as the skeleton of the proposed computational framework and carefully integrates neural networks under the framework. The advantages of StatEcoNet over related approaches are demonstrated on simulated datasets as well as bird species data. Since SDMs are critical tools for ecological science and natural resource management, StatEcoNet may offer boosted computational and analytical powers to a wide range of applications that have significant social impacts, e.g., the study and conservation of threatened species

Isolating the Role of Corticosterone in the Hypothalamic-Pituitary-Gonadal Transcriptomic Stress Response

Investigation of the negative impacts of stress on reproduction has largely centered around the effects of the adrenal steroid hormone, corticosterone (CORT), and its influence on a system of tissues vital for reproduction-the hypothalamus of the brain, the pituitary gland, and the gonads (the HPG axis). Research on the action of CORT on the HPG axis has predominated the stress and reproductive biology literature, potentially overshadowing other influential mediators. To gain a more complete understanding of how elevated CORT affects transcriptomic activity of the HPG axis, we experimentally examined its role in male and female rock doves (Columba livia). We exogenously administrated CORT to mimic circulating levels during the stress response, specifically 30 min of restraint stress, an experimental paradigm known to increase circulating CORT in vertebrates. We examined all changes in transcription within each level of the HPG axis as compared to both restraint-stressed birds and vehicle-injected controls. We also investigated the differential transcriptomic response to CORT and restraint-stress in each sex. We report causal and sex-specific effects of CORT on the HPG transcriptomic stress response. Restraint stress caused 1567 genes to uniquely differentially express while elevated circulating CORT was responsible for the differential expression of 304 genes. Only 108 genes in females and 8 in males differentially expressed in subjects that underwent restraint stress and those who were given exogenous CORT. In response to elevated CORT and restraint-stress, both sexes shared the differential expression of 5 genes, KCNJ5, CISH, PTGER3, CEBPD, and ZBTB16, all located in the pituitary. The known functions of these genes suggest potential influence of elevated CORT on immune function and prolactin synthesis. Gene expression unique to each sex indicated that elevated CORT affected more gene transcription in females than males (78 genes versus 3 genes, respectively). To our knowledge, this is the first study to isolate the role of CORT in HPG genomic transcription during a stress response. We present an extensive and openly accessible view of the role corticosterone in the HPG transcriptomic stress response. Because the HPG system is well conserved across vertebrates, these data have the potential to inspire new therapeutic strategies for reproductive dysregulation in multiple vertebrate systems, including our own