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Citizen science biodiversity data present great opportunities for ecology and conservation across vast spatial and temporal scales. However, the opportunistic nature of these data lacks the sampling structure required by modeling methodologies that address a pervasive challenge in ecological data collection: imperfect detection, i.e., the likelihood of under-observing species on field surveys. Occupancy modeling is an example of an approach that accounts for imperfect detection by explicitly modeling the observation process separately from the biological process of habitat selection. This produces species distribution models that speak to the pattern of the species on a landscape after accounting for imperfect detection in the data, rather than the pattern of species observations corrupted by errors. To achieve this benefit, occupancy models require multiple surveys of a site across which the site's status (i.e., occupied or not) is assumed constant. Since citizen science data are not collected under the required repeated-visit protocol, observations may be grouped into sites post hoc. Existing approaches for constructing sites discard some observations and/or consider only geographic distance and not environmental similarity. In this study, we compare ten approaches for site construction in terms of their impact on downstream species distribution models for 31 bird species in Oregon, using observations recorded in the eBird database. We find that occupancy models built on sites constructed by spatial clustering algorithms perform better than existing alternatives. 

Geospatial problems often involve spatial autocorrelation and covariate shift, which violate the independent, identically distributed assumption underlying standard cross-validation. In this work, we establish a theoretical criterion for unbiased crossvalidation, introduce a preliminary categorization framework to guide practitioners in choosing suitable cross-validation strategies for geospatial problems, reconcile conflicting recommendations on best practices, and develop a novel, straightforward method with both theoretical guarantees and empirical success. 

Geostatistical learning problems are frequently characterized by spatial autocorrelation in the input features and/or the potential for covariate shift at test time. These realities violate the classical assumption of independent, identically distributed data, upon which most cross-validation algorithms rely in order to estimate the generalization performance of a model. In this paper, we present a theoretical criterion for unbiased cross-validation estimators in the geospatial setting. We also introduce a new cross-validation algorithm to evaluate models, inspired by the challenges of geospatial problems. We apply a framework for categorizing problems into different types of geospatial scenarios to help practitioners select an appropriate cross-validation strategy. Our empirical analyses compare cross-validation algorithms on both simulated and several real datasets to develop recommendations for a variety of geospatial settings. This paper aims to draw attention to some challenges that arise in model evaluation for geospatial problems and to provide guidance for users. 

The growth of biodiversity data sets generated by citizen scientists continues to accelerate. The availability of such data has greatly expanded the scale of questions researchers can address. Yet, error, bias, and noise continue to be serious concerns for analysts, particularly when data being contributed to these giant online data sets are difficult to verify. Counts of birds contributed to eBird, the world’s largest biodiversity online database, present a potentially useful resource for tracking trends over time and space in species’ abundances. We quantified counting accuracy in a sample of 1,406 eBird checklists by comparing numbers contributed by birders (N = 246) who visited a popular birding location in Oregon, USA, with numbers generated by a professional ornithologist engaged in a long-term study creating benchmark (reference) measurements of daily bird counts. We focused on waterbirds, which are easily visible at this site. We evaluated potential predictors of count differences, including characteristics of contributed checklists, of each species, and of time of day and year. Count differences were biased toward undercounts, with more than 75% of counts being below the daily benchmark value. Median count discrepancies were −29.1% (range: 0 to −42.8%; N = 20 species). Model sets revealed an important influence of each species’ reference count, which varied seasonally as waterbird numbers fluctuated, and of percent of species known to be present each day that were included on each checklist. That is, checklists indicating a more thorough survey of the species richness at the site also had, on average, smaller count differences. However, even on checklists with the most thorough species lists, counts were biased low and exceptionally variable in their accuracy. To improve utility of such bird count data, we suggest three strategies to pursue in the future. (1) Assess additional options for analytically determining how to select checklists that include less biased count data, as well as exploring options for correcting bias during the analysis stage. (2) Add options for users to provide additional information that helps analysts choose checklists, such as an option for users to tag checklists where they focused on obtaining accurate counts. (3) Explore opportunities to effectively calibrate citizen-science bird count data by establishing a formalized network of marquis sites where dedicated observers regularly contribute carefully collected benchmark data. 

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.