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During the COVID-19 pandemic, hospitals did not allow caregiver visitation. Little is known about how caregivers' absence affected patients' care.This study aimed to describe visitation restrictions' impact on patients and their caregivers experiences.We used a sequential explanatory mixed-methods study design. First, we randomly selected 200 adult patients with cancer or heart failure hospitalized before (n = 100) and during visitor restrictions (n = 100) and abstracted data from the electronic medical record on communication between medical teams and caregivers and the topics discussed. Results from the quantitative analysis guided our thematic analysis of semi-structured interviews conducted with a subsample of patients hospitalized during visitor restrictions and their caregivers to understand the impact of visitor restrictions on their experiences.Compared to prerestrictions, caregivers under visitation restrictions communicated less frequently with the medical team (29% vs. 37% of hospitalized days; p = .04), fewer received discharge counseling (37% vs. 52%; p = .04), and disproportionately more had no contact with the medical team (36% vs. 17%; p .01). Video conferencing was documented for caregivers of only five patients. Qualitative analysis revealed that both caregivers and patients experienced emotional distress, increased conflict, and decreased perception of quality of care because of visitation restrictions.Hospital visitor restrictions significantly reduced caregivers' communication with patients' medical team, causing caregivers and patients emotional distress. Protocols that facilitate communication between caregivers and care teams may benefit caregivers who cannot be physically present at care facilities, including distance caregivers.

As palliative care (PC) programs rapidly grow and expand across settings, the need to measure, improve, and standardize high-quality PC has also grown. The electronic health record (EHR) is a key component of these efforts as a central hub of care delivery and a repository of patient and system data. Deliberate efforts to leverage the EHR for PC quality improvement (QI) can help PC programs and health systems improve care for patients with serious illnesses. This article, written by clinicians with experience in QI, informatics, and clinical program development, provides practical tips and guidance on EHR strategies and tools for QI and quality measurement.

Matrix completion based collaborative filtering is considered scalable and effective for online service link prediction (e.g., movie recommendation) but does not meet the challenges of link prediction in ecological networks. A unique challenge of ecological networks is that the observed data are subject to systematic imperfect detection , due to the difficulty of accurate field sampling. In this work, we propose a new framework customized for ecological bipartite network link prediction. Our approach starts with incorporating the Poisson $N$ N -mixture model, a widely used framework in statistical ecology for modeling imperfect detection of a single species in field sampling. Despite its extensive use for single species analysis, this model has never been considered for link prediction between different species, perhaps because of the complex nature of both link prediction and $N$ N -mixture model inference. By judiciously combining the Poisson $N$ N -mixture model with a probabilistic nonnegative matrix factorization (NMF) model in latent space, we propose an intuitive statistical model for the problem of interest. We also offer a scalable and convergence-guaranteed optimization algorithm to handle the associated maximum likelihood identification problem. Experimental results on synthetic data and two real-world ecological networks data are employed to validate our proposed approach.

Guidelines recommend goals of care conversations (GOCC) and specialty palliative care at the time of initiation of palliative inotropes for patients with advanced heart failure. The extent to which GOCC are occurring, the content of the GOCC, and the frequency of palliative care involvement is unknown.We conducted a retrospective chart review of all patients discharged from a tertiary medical center between October 2015 and April 2020 initiated on continuous palliative inotropes. We identified 53 patients who were discharged on continuous palliative inotropes. Most patients (46/53 [87%]) had a documented GOCC, including discussions around preferences for life-sustaining treatments, hospice, and/or prognosis. However, just more than one-half of the patients (55% [15/27]) with an ICD had a documented discussion regarding preferences for ICD deactivation. Patients seen by palliative care were more likely to have a documented discussion about ICD deactivation (68% vs 0%, P = .0098). The frequency of palliative care consultation and documented GOCC conversations increased over time.Although we observed overall high rates of documented GOCC, there is room for improving the rate of discussions with patients about whether they wish to have their ICD deactivated. Future research should test interventions to improve the frequency of ICD discussions.

Context With greater accessibility and processing power from online platforms, summaries of remotely sensed data are increasingly used in species distribution models (SDMs). Comparisons of the predictive power of these environmental variables could inform SDMs moving forward. Objectives We evaluated the performance of freely available Landsat data as predictor sets for SDMs. Our objectives were to 1) compare the performance of single season SDMs built on mean values of raw spectral bands, Tasseled Cap transformations, and eight different indices, including NDVI, 2) evaluate the performance gain with the addition of standard deviation, textural metrics, and additional seasons, and 3) compare the performance of SDMs built on these continuous spectral predictor sets to SDMs built on classified land cover data (e.g., percent forest cover). Methods We used statewide point counts to build multi-scale SDMs for 13 avian species across Oregon, USA. We compared the performance of SDMs built on each predictor set based on our objectives. Results Of the Landsat-derived predictor sets, SDMs built on raw spectral bands had the highest overall performance with nearly equivalent performance in Tasseled-Cap models. While performance gains from standard deviations, textural metrics, and additional seasons were minimal in raw-band and Tasseled-Cap models, gains were appreciable in single-index models. Classified land cover models performed equivalently to raw band models. Conclusions When predictive performance is paramount, means of raw Landsat bands are strong predictors for avian SDMs. When parsimonious variables are essential, SDMs of single indices (e.g., NDVI) greatly benefit from additional information, such as standard deviation., This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature's AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10980-022-01406-y

Satellite imagery has been widely used to map urbanization processes. To address the urgent need for urban landscape mapping that goes beyond urban footprint analysis, the local climate zone (LCZ) scheme has been increasingly used to reveal the urban forms and functions important to urban heat islands and micro-climates across the globe. As with most supervised classification strategies, proper application of training data is critical for the success of LCZ classification models. However, the collection and application of LCZ training areas brings with it two challenges that may affect mapping success. First, because digitizing training areas is a timeconsuming task, there is a broad effort in the LCZ mapping community to create a crowdsourced data collection among different experts. However, this strategy likely leads to inconsistencies in labels that could weaken models. Second, the LCZ labeling process typically involves the delineation of large zones from which multiple training samples are drawn, but those samples are likely spatially autocorrelated and lead to overly optimistic estimates of model accuracy. Although both effects - inconsistent labeling and spatial autocorrelation - are theoretically possible, it is unknown whether they substantially affect accuracy. We investigated both issues, specifically asking: (i) how do the discrepancies of LCZ labeling by different experts impact broad-scale LCZ mapping? (ii) to what extent does spatial correlation affect model prediction power? We used two classifiers (Random Forests and ResNets) to map eight metropolitan areas in the US into LCZs, comparing training areas drawn by different or consistent interpreters, and data splitting strategy using rules that allow or reduce spatial autocorrelation. We found large discrepancies among results built from crowdsourced training areas digitized by different experts; improving the consistency of labels can lead to substantial improvements in LCZ classification accuracy. Second, we found that spatial autocorrelation can boost the apparent accuracy of the classifier by 16% to 21%, leading to erroneous interpretation of mapping results. The two effects interplay as well: spatial auto correlation in the raw data can lead to an underestimation of the model's predictive error when modeling with crowdsourced training areas of high inconsistency. Due to the uncertainty in the labeling process and spatial autocorrelation in derived training data, broad-scale LCZ mapping results should be interpreted with caution. Published version

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) often receive burdensome care at end-of-life (EOL) and infrequently complete advance care planning (ACP). The surprise question (SQ) is a prognostic tool that may facilitate ACP. OBJECTIVE: To assess how well the SQ predicts mortality and prompts ACP for COPD patients. DESIGN: Retrospective cohort study. SUBJECTS: Patients admitted to the hospital for an acute exacerbation of COPD between July 2015 and September 2018. MAIN MEASURES: Emergency department (ED) and inpatient clinicians answered, “Would you be surprised if this patient died in the next 30 days (ED)/one year (inpatient)?” The primary outcome measure was the accuracy of the SQ in predicting 30-day and 1-year mortality. The secondary outcome was the correlation between SQ and ACP (palliative care consultation, documented goals-of-care conversation, change in code status, or completion of ACP document). KEY RESULTS: The 30-day SQ had a high specificity but low sensitivity for predicting 30-day mortality: sensitivity 12%, specificity 95%, PPV 11%, and NPV 96%. The 1-year SQ demonstrated better accuracy for predicting 1-year mortality: sensitivity 47%, specificity 75%, PPV 35%, and NPV 83%. After multivariable adjustment for age, sex, and prior 6-month admissions, 1-year SQ+ responses were associated with greater odds of 1-year mortality (OR 2.38, 95% CI 1.39–4.08) versus SQ-. One-year SQ+ patients were more likely to have a goals-of-care conversation (25% vs. 11%, p < 0.01) and complete an advance directive or POLST (46% vs. 23%, p < 0.01). After multivariable adjustment, SQ+ responses to the 1-year SQ were associated with greater odds of ACP receipt (OR 2.67, 95% CI 1.64–4.36). CONCLUSIONS: The 1-year surprise question may be an effective component of prognostication and advance care planning for COPD patients in the inpatient setting. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11606-020-06512-8.

BackgroundFear of cancer recurrence (FCR) is an important cause of suffering for cancer survivors, and both empirical evidence and theoretical models suggest that prognostic uncertainty plays a causal role in its development. However, the relationship between prognostic uncertainty and FCR is incompletely understood.ObjectiveTo explore the relationship between prognostic uncertainty and FCR among patients with ovarian cancer (OC).DesignA qualitative study was conducted utilizing individual in-depth interviews with a convenience sample of patients with epithelial ovarian cancer who had completed first-line treatment with surgery and/or chemotherapy. Semi-structured interviews explored participants’ (1) understanding of their prognosis; (2) experiences, preferences, and attitudes regarding prognostic information; and (3) strategies for coping with prognostic uncertainty. Inductive qualitative analysis and line-by-line software-assisted coding of interview transcripts was conducted to identify key themes and generate theoretical insights on the relationship between prognostic uncertainty and FCR.ResultsThe study sample consisted of 21 participants, nearly all of whom reported experiencing significant FCR, which they traced to an awareness of the possibility of a bad outcome. Some participants valued and pursued prognostic information as a means of coping with this awareness, suggesting that prognostic uncertainty causes FCR. However, most participants acknowledged fundamental limits to both the certainty and value of prognostic information, and engaged in various strategies aimed not at reducing but constructing and maintaining prognostic uncertainty as a means of sustaining hope in the possibility of a good outcome. Participants’ comments suggested that prognostic uncertainty, fear, and hope are connected by complex, bi-directional causal pathways mediated by processes that allow patients to cope with, construct, and maintain their uncertainty. A provisional dual-process theoretical model was developed to capture these pathways.ConclusionAmong patients with OC, prognostic uncertainty is both a cause and an effect of FCR—a fear-inducing stimulus and a hope-sustaining response constructed and maintained through various strategies. More work is needed to elucidate the relationships between prognostic uncertainty, fear, and hope, to validate and refine our theoretical model, and to develop interventions to help patients with OC and other serious illnesses to achieve an optimal balance between these states.

18 pages, 5 figures, supplementary data https://doi.org/10.1016/j.biocon.2021.109175.-- The data supporting the findings of this study are available in the Supplementary Materials (Appendix 3–5, Table A3-A5). Raw datasets (where available) and results summary tables for each analysis of human mobility and empirical datasets are deposited in a github repository: https://github.com/rjcommand/PAN-Environment, The global lockdown to mitigate COVID-19 pandemic health risks has altered human interactions with nature. Here, we report immediate impacts of changes in human activities on wildlife and environmental threats during the early lockdown months of 2020, based on 877 qualitative reports and 332 quantitative assessments from 89 different studies. Hundreds of reports of unusual species observations from around the world suggest that animals quickly responded to the reductions in human presence. However, negative effects of lockdown on conservation also emerged, as confinement resulted in some park officials being unable to perform conservation, restoration and enforcement tasks, resulting in local increases in illegal activities such as hunting. Overall, there is a complex mixture of positive and negative effects of the pandemic lockdown on nature, all of which have the potential to lead to cascading responses which in turn impact wildlife and nature conservation. While the net effect of the lockdown will need to be assessed over years as data becomes available and persistent effects emerge, immediate responses were detected across the world. Thus initial qualitative and quantitative data arising from this serendipitous global quasi-experimental perturbation highlights the dual role that humans play in threatening and protecting species and ecosystems. Pathways to favorably tilt this delicate balance include reducing impacts and increasing conservation effectiveness, The Canada Research Chairs program provided funding for the core writing team. Field research funding was provided by A.G. Leventis Foundation; Agence Nationale de la Recherche, [grant number ANR-18-32–0010CE-01 (JCJC PEPPER)]; Agencia Estatal de Investigaci; Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação (ARDITI), [grant number M1420-09-5369-FSE-000002]; Alan Peterson; ArcticNet; Arkadaşlar; Army Corp of Engineers; Artificial Reef Program; Australia's Integrated Marine Observing System (IMOS), National Collaborative; Research Infrastructure Strategy (NCRIS), University of Tasmania; Australian Institute of Marine Science; Australian Research Council, [grant number LP140100222]; Bai Xian Asia Institute; Batubay Özkan; BC Hydro Fish and Wildlife Compensation Program; Ben-Gurion University of the Negev; Bertarelli Foundation; Bertarelli Programme in Marine Science; Bilge Bahar; Bill and Melinda Gates Foundation; Biology Society of South Australia; Boston University; Burak Över; California State Assembly member Patrick O'Donnell; California State University Council on Ocean Affairs, Science & Technology; California State University Long Beach; Canada Foundation for Innovation (Major Science Initiative Fund and funding to Oceans Network Canada), [grant number MSI 30199 for ONC]; Cape Eleuthera Foundation; Centre National d'Etudes Spatiales; Centre National de la Recherche Scientifique; Charles Darwin Foundation, [grant number 2398]; Colombian Institute for the Development of Science and Technology (COLCIENCIAS), [grant number 811–2018]; Colombian Ministry of Environment and Sustainable Development, [grant number 0041–2020]; Columbia Basin Trust; Commission for Environmental Cooperation; Cornell Lab of Ornithology; Cultural practices and environmental certification of beaches, Universidad de la Costa, Colombia, [grant number INV.1106–01–002-15, 2020–21]; Department of Conservation New Zealand; Direction de l'Environnement de Polynésie Française; Disney Conservation Fund; DSI-NRF Centre of; Excellence at the FitzPatrick Institute of African Ornithology; Ecology Project International; Emin Özgür; Environment and Climate Change Canada; European Community: RTD programme - Species Support to Policies; European Community's Seventh Framework Programme; European Union; European Union's Horizon 2020 research and innovation programme, Marie Skłodowska-Curie, [grant number 798091, 794938]; Faruk Eczacıbaşı; Faruk Yalçın Zoo; Field research funding was provided by King Abdullah University of Science and Technology; Fish and Wildlife Compensation Program; Fisheries and Oceans Canada; Florida Fish and Wildlife Conservation Commission, [grant numbers FWC-12164, FWC-14026, FWC-19050]; Fondo Europeo de Desarrollo Regional; Fonds québécois de la recherche nature et technologies; Foundation Segré; Fundação para a Ciência e a Tecnologia (FCT Portugal); Galapagos National Park Directorate research, [grant number PC-41-20]; Gordon and Betty Moore Foundation, [grant number GBMF9881 and GBMF 8072]; Government of Tristan da Cunha; Habitat; Conservation Trust Foundation; Holsworth Wildlife Research Endowment; Institute of Biology of the Southern Seas, Sevastopol, Russia; Instituto de Investigación de Recursos Biológicos Alexander von Humboldt; Instituto Nacional de Pesquisas Espaciais (INPE), Brazil; Israeli Academy of Science's Adams Fellowship; King Family Trust; Labex, CORAIL, France; Liber Ero Fellowship; LIFE (European Union), [grant number LIFE16 NAT/BG/000874]; María de Maeztu Program for Units of Excellence in R&D; Ministry of Science and Innovation, FEDER, SPASIMM,; Spain, [grant number FIS2016–80067-P (AEI/FEDER, UE)]; MOE-Korea, [grant number 2020002990006]; Mohamed bin Zayed Species Conservation Fund; Montreal Space for Life; National Aeronautics and Space Administration (NASA) Earth and Space Science Fellowship Program; National Geographic Society, [grant numbers NGS-82515R-20]; National Natural Science Fund of China; National Oceanic and Atmospheric Administration; National Parks Board, Singapore; National Science and Technology Major Project of China; National Science Foundation, [grant number DEB-1832016]; Natural Environment Research Council of the UK; Natural Sciences and Engineering Research Council of Canada (NSERC), Alliance COVID-19 grant program, [grant numbers ALLRP 550721–20, RGPIN-2014-06229 (year: 2014), RGPIN-2016-05772 (year: 2016)]; Neiser Foundation; Nekton Foundation; Network of Centre of Excellence of Canada: ArcticNet; North Family Foundation; Ocean Tracking Network; Ömer Külahçıoğlu; Oregon State University; Parks Canada Agency (Lake Louise, Yoho, and Kootenay Field Unit); Pew Charitable Trusts; Porsim Kanaf partnership; President's International Fellowship Initiative for postdoctoral researchers Chinese Academy of Sciences, [grant number 2019 PB0143]; Red Sea Research Center; Regional Government of the Azores, [grant number M3.1a/F/025/2015]; Regione Toscana; Rotary Club of Rhinebeck; Save our Seas Foundation; Science & Technology (CSU COAST); Science City Davos, Naturforschende Gesellschaft Davos; Seha İşmen; Sentinelle Nord program from the Canada First Research Excellence Fund; Servizio Foreste e Fauna (Provincia Autonoma di Trento); Sigrid Rausing Trust; Simon Fraser University; Sitka Foundation; Sivil Toplum Geliştirme Merkezi Derneği; South African National Parks (SANParks); South Australian Department for Environment and Water; Southern California Tuna Club (SCTC); Spanish Ministry for the Ecological Transition and the Demographic Challenge; Spanish Ministry of Economy and Competitiveness; Spanish Ministry of Science and Innovation; State of California; Sternlicht Family Foundation; Suna Reyent; Sunshine Coast Regional Council; Tarea Vida, CEMZOC, Universidad de Oriente, Cuba, [grant number 10523, 2020]; Teck Coal; The Hamilton Waterfront Trust; The Ian Potter Foundation, Coastwest, Western Australian State NRM; The Red Sea Development Company; The Wanderlust Fund; The Whitley Fund; Trans-Anatolian Natural Gas Pipeline; Tula Foundation (Hakai Institute); University of Arizona; University of Pisa; US Fish and Wildlife Service; US Geological Survey; Valencian Regional Government; Vermont Center for Ecostudies; Victorian Fisheries Authority; VMRC Fishing License Fund; and Wildlife Warriors Worldwide, With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S

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., Comment: To appear in the Proceeding of the 35th AAAI Conference on Artificial Intelligence (AAAI-21); Added supplemental material

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 errors in a sample of 1406 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 waterbird 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. When only checklists that actually reported a species known to be present were included, median count errors 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, lower counting errors. 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. One is to assess additional options for analytically determining how to select checklists that have the highest probability of including less biased count data, as well as exploring options for correcting bias during the analysis stage. Another is to 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. We also recommend exploration of 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.

Wild flowering plants and their wild insect visitors are of great importance for pollination. Montane meadows are biodiversity hotspots for flowering plants and pollinators, but they are contracting due to tree invasion. This study quantified flowering plants and their flower-visitor species in montane meadows in the western Cascade Range of Oregon. Species diversity in small, isolated meadows was expected to be lower and nested relative to large meadows. Alternatively, landform features may influence richness and spatial turnover. Flowering plants and their visitors were sampled in summers of 2011–2017 in twelve montane meadows with varying soil moisture. All flowering plants and all flower-visitors were recorded during five to seven 15 min watches in ten 3 × 3 m plots in each meadow and year. A total of 178 flowering plant species, 688 flower-visitor species and 137,916 interactions were identified. Richness of flower-visitors was related to meadow patch size, but neither plant nor flower-visitor richness was related to isolation measured as meadow area within 1000 m. Species in small meadows were not nested subsets of those in large meadows. Species replacement accounted for more than 78% of dissimilarity between meadows and was positively related to differences in soil moisture. Although larger meadows contained more species, landform features have influenced meadow configuration, persistence, and soil moisture, contributing to high plant and insect species diversity. Hence, conservation and restoration of a variety of meadow types may promote landscape diversity of wild plants and pollinators.

AimsSpecies distributions result from both biotic and abiotic interactions across large spatial scales. The interplay of these interactions as climate changes quickly has been understudied, particularly in herbivorous insects. Here, we investigate the relative impacts these influences on the putative northern range expansion of the giant swallowtail butterfly in North America.LocationNorth America.Time period1959-2018.Major taxa studiedEastern Giant swallowtail, Papilio cresphontes (Lepidoptera: Papilionidae); common hop tree, Ptelea trifoliata; common prickly ash, Zanthoxylum americanum; southern prickly ash, Zanthoxylum clava-herculis (Saphidales: Rutaceae).MethodsWe used data from museum collections and citizen science repositories to generate species distribution models. Distribution models were built for each species over two time periods (T1 = 1959-1999; T2 = 2000-2018).ResultsModels for P. cresphontes and associated host plants had high predictive accuracy on spatially-explicit test data (AUC 0.810-0.996). Occurrence data align with model outputs, providing strong evidence for a northward range expansion in the last 19 years (T2) by P. cresphontes. Host plants have shifted in more complex ways, and result in a change in suitable habitat for P. cresphontes in its historic range. P. cresphontes has a northern range which now closely aligns with its most northern host plant - continued expansion northward is unlikely, and historic northern range limits were likely determined by abiotic, not biotic, factors.Main conclusionsBiotic and abiotic factors have driven the rapid northern range expansion in the giant swallowtail butterfly across North America in the last 20 years. A number of bioclimatic variables are correlated with this expansion, notably an increase in mean annual temperature and minimum winter temperature. We predict a slowing of northward range expansion in the next 20-50 years as butterflies are now limited by the range of host plants, rather than abiotic factors.

1. Dynamic occupancy models are popular for estimating dynamic distribution rates (colonization and extinction) from repeated presence/absence surveys of unmarked animals. This approach assumes closure among repeated samples within primary periods, allowing estimation of dynamic rates between these periods. However, the impact of temporary emigration (reversible changes in sampling availability) on dynamic rate estimates, has not been tested. 2. Using simulated data, we investigated the degree to which temporary emigration could mislead researchers interested in quantifying dynamics. We then compared results from three avian point count datasets to evaluate the likelihood that temporary emigration confounds estimates of dynamics for 19 species under a popular sampling protocol. 3. Simulated experiments indicated that when secondary periods were open to temporary emigration, presence of dynamics was correctly identified ≥ 95.1% of the time, and dynamic rate estimates were accurate. However, dynamic rate estimates were biased when secondary periods were closed to temporary emigration. In empirical datasets, dynamic occupancy models had greater support than closed models for all species when secondary sampling periods occurred in immediate succession (i.e., 3 samples within 10 minutes); however, our results suggest that this is because these estimates were heavily influenced by temporary emigration. When counts within a primary period were separated by 24-48 hours, we found evidence of dynamics for less than half of these species. We recommend an alternative sampling approach that allows accurate estimation of dynamic rates when temporary emigration is of no interest, and introduce a novel model for estimating both processes simultaneously in rare cases where they are both of biological interest. 4. Concern for violating the occupancy modeling closure assumption has led to widespread recommendations that samples within primary periods be conducted extremely close in time. However, this may not be the best approach when interest is in quantifying dynamic rates. While dynamic occupancy models provide estimates of ‘colonization’ and ‘extinction,’ these values do not inherently represent dynamics unless temporary emigration has been explicitly modeled, or accounted for with sampling design. Naivete to this fact can result in incorrect conclusions about biological processes. This article is protected by copyright. All rights reserved.

Summary Occupancy models are employed in species distribution modelling to account for imperfect detection during field surveys. While this approach is popular in the literature, problems can occur when estimating the model parameters. In particular, the maximum likelihood estimates can exhibit bias and large variance for data sets with small sample sizes, which can result in estimated occupancy probabilities near 0 and 1 (‘boundary estimates’). In this paper, we explore strategies for estimating parameters based on maximizing a penalized likelihood. Penalized likelihood methods augment the usual likelihood with a penalty function that encodes information about what parameter values are undesirable. We introduce penalties for occupancy models that have analogues in ridge regression and Bayesian approaches, and we compare them to a penalty developed for occupancy models in prior work. We examine the bias, variance and mean squared error of parameter estimates obtained from each method on synthetic data. Across all of the synthetic data sets, the penalized estimation methods had lower mean squared error than the maximum likelihood estimates. We also provide an example of the application of these methods to point counts of avian species. Penalized likelihood methods show similar improvements when tested using empirical bird point count data. We discuss considerations for choosing among these methods when modelling occupancy. We conclude that penalized methods may be of practical utility for fitting occupancy models with small sample sizes, and we are releasing R code that implements these methods.

Data collection, storage, analysis, visualization, and dissemination are changing rapidly due to advances in new technologies driven by computer science and universal access to the internet. These technologies and web connections place human observers front and center in citizen science-driven research and are critical in generating new discoveries and innovation in such fields as astronomy, biodiversity, and meteorology. Research projects utilizing a citizen science approach address scientific problems at regional, continental, and even global scales otherwise impossible for a single lab or even a small collection of academic researchers. Here we describe eButterfly an integrative checklist-based butterfly monitoring and database web-platform that leverages the skills and knowledge of recreational butterfly enthusiasts to create a globally accessible unified database of butterfly observations across North America. Citizen scientists, conservationists, policy makers, and scientists are using eButterfly data to better understand the biological patterns of butterfly species diversity and how environmental conditions shape these patterns in space and time. eButterfly in collaboration with thousands of butterfly enthusiasts has created a near real-time butterfly data resource producing tens of thousands of observations per year open to all to share and explore.

Aim Assessing the influence of land cover in species distribution modelling is limited by the availability of fine-resolution land-cover data appropriate for most species responses. Remote-sensing technology offers great potential for predicting species distributions at large scales, but the cost and required expertise are prohibitive for many applications. We test the usefulness of freely available raw remote-sensing reflectance data in predicting species distributions of 40 commonly occurring bird species in western Oregon. Location Central Coast Range, Cascade and Klamath Mountains Oregon, USA. Methods Information on bird observations was collected from 4598 fixed-radius point counts. Reflectance data were obtained using 30-m resolution Landsat imagery summarized at scales of 150, 500, 1000 and 2000 m. We used boosted regression tree (BRT) models to analyse relationships between distributions of birds and reflectance values and evaluated prediction performance of the models using area under the receiver operating characteristic curve (AUC) values. Results Prediction success of models using all reflectance values was high (mean AUC = 0.79 ± 0.10 SD). Further, model performance using individual reflectance bands exceeded those that used only Normalized Difference Vegetation Index (NDVI). The relative influence of band 4 predictors was highest, indicating the importance of variables associated with vegetation biomass and photosynthetic activity. Across spatial scales, the average influence of predictors at the 2000 m scale was greatest. Main Conclusions We demonstrate that unclassified remote-sensing imagery can be used to produce species distribution models with high prediction success. Our study is the first to identify general patterns in the usefulness of spectral reflectances for species distribution modelling of multiple species. We conclude that raw Landsat Thematic Mapper data will be particularly useful in species distribution models when high-resolution predictions are required, including habitat change detection studies, identification of fine-scale biodiversity hotspots and reserve design.

Identifying drivers of infectious disease patterns and impacts at the broadest scales of organisation is one of the most crucial challenges for modern science, yet answers to many fundamental questions remain elusive. These include what factors commonly facilitate transmission of pathogens to novel host species, what drives variation in immune investment among host species, and more generally what drives global patterns of parasite diversity and distribution? Here we consider how the perspectives and tools of macroecology, a field that investigates patterns and processes at broad spatial, temporal and taxonomic scales, are expanding scientific understanding of global infectious disease ecology. In particular, emerging approaches are providing new insights about scaling properties across all living taxa, and new strategies for mapping pathogen biodiversity and infection risk. Ultimately, macroecology is establishing a framework to more accurately predict global patterns of infectious disease distribution and emergence.

We present a new method for modeling fMRI time series data called Hidden Process Models (HPMs). Like several earlier models for fMRI analysis, Hidden Process Models assume that the observed data is generated by a sequence of underlying mental processes that may be triggered by stimuli. HPMs go beyond these earlier models by allowing for processes whose timing may be unknown, and that might not be directly tied to specific stimuli. HPMs provide a principled, probabilistic framework for simultaneously learning the contribution of each process to the observed data, as well as the timing and identities of each instantiated process. They also provide a framework for evaluating and selecting among competing models that assume different numbers and types of underlying mental processes. We describe the HPM framework and its learning and inference algorithms, and present experimental results demonstrating its use on simulated and real fMRI data. Our experiments compare several models of the data using cross-validated data log-likelihood in an fMRI study involving overlapping mental processes whose timings are not fully known.

Over the past decade, functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful new instrument to collect vast quantities of data about activity in the human brain. A typical fMRI experiment can produce a three-dimensional image related to the human subject's brain activity every half second, at a spatial resolution of a few millimeters. As in other modern empirical sciences, this new instrumentation has led to a flood of new data, and a corresponding need for new data analysis methods. We describe recent research applying machine learning methods to the problem of classifying the cognitive state of a human subject based on fRMI data observed over a single time interval. In particular, we present case studies in which we have successfully trained classifiers to distinguish cognitive states such as (1) whether the human subject is looking at a picture or a sentence, (2) whether the subject is reading an ambiguous or non-ambiguous sentence, and (3) whether the word the subject is viewing is a word describing food, people, buildings, etc. This learning problem provides an interesting case study of classifier learning from extremely high dimensional (105 features), extremely sparse (tens of training examples), noisy data. This paper summarizes the results obtained in these three case studies, as well as lessons learned about how to successfully apply machine learning methods to train classifiers in such settings.

To avoid ecological collapse, we must manage Earth's ecosystems sustainably. Viewed as a control problem, the two central challenges of ecosystem management are to acquire a model of the system that is sufficient to guide good decision making and then optimize the control policy against that model. This paper describes three efforts aimed at addressing the first of these challenges—machine learning methods for modeling ecosystems. The first effort focuses on automated quality control of environmental sensor data. Next, we consider the problem of learning species distribution models from citizen science observational data. Finally, we describe a novel approach to modeling the migration of birds. A major challenge for all of these methods is to scale up to large, spatially-distributed systems.

Identifying ecological patterns across broad spatial and temporal extents requires novel approaches and methods for acquiring, integrating and modeling massive quantities of diverse data. For example, a growing number of research projects engage continent-wide networks of volunteers (‘citizen-scientists') to collect species occurrence data. Although these data are information rich, they present numerous challenges in project design, implementation and analysis, which include: developing data collection tools that maximize data quantity while maintaining high standards of data quality, and applying new analytical and visualization techniques that can accurately reveal patterns in these data. Here, we describe how advances in data-intensive science provide accurate estimates in species distributions at continental scales by identifying complex environmental associations.

Citizen scientists, who are volunteers from the community that participate as field assistants in scientific studies [3], enable research to be performed at much larger spatial and temporal scales than trained scientists can cover. Species distribution modeling [6], which involves understanding species-habitat relationships, is a research area that can benefit greatly from citizen science. The eBird project [16] is one of the largest citizen science programs in existence. By allowing birders to upload observations of bird species to an online database, eBird can provide useful data for species distribution modeling. However, since birders vary in their levels of expertise, the quality of data submitted to eBird is often questioned. In this paper, we develop a probabilistic model called the Occupancy-Detection-Expertise (ODE) model that incorporates the expertise of birders submitting data to eBird. We show that modeling the expertise of birders can improve the accuracy of predicting observations of a bird species at a site. In addition, we can use the ODE model for two other tasks: predicting birder expertise given their history of eBird checklists and identifying bird species that are difficult for novices to detect.

We introduce Hidden Process Models (HPMs), a class of probabilistic models for multivariate time series data. The design of HPMs has been motivated by the challenges of modeling hidden cognitive processes in the brain, given functional Magnetic Resonance Imaging (fMRI) data. fMRI data is sparse, high-dimensional, non-Markovian, and often involves prior knowledge of the form "hidden event A occurs n times within the interval [t,t′]." HPMs provide a generalization of the widely used General Linear Model approaches to fMRI analysis, and HPMs can also be viewed as a subclass of Dynamic Bayes Networks.