@article{wheeler_dietze_lebauer_peters_richardson_ross_thomas_zhu_bhat_munch_et al._2024, title={Predicting spring phenology in deciduous broadleaf forests: NEON phenology forecasting community challenge}, volume={345}, ISSN={["1873-2240"]}, DOI={10.1016/j.agrformet.2023.109810}, abstractNote={Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative's National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate when, where, and for what model type phenology forecast skill is highest. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts.}, journal={AGRICULTURAL AND FOREST METEOROLOGY}, author={Wheeler, Kathryn I. and Dietze, Michael C. and Lebauer, David and Peters, Jody A. and Richardson, Andrew D. and Ross, Arun A. and Thomas, R. Quinn and Zhu, Kai and Bhat, Uttam and Munch, Stephan and et al.}, year={2024}, month={Feb} }
 @article{montgomery_walden-schreiner_saffer_jones_seliger_worm_tateosian_shukunobe_kumar_meentemeyer_2023, title={Forecasting global spread of invasive pests and pathogens through international trade}, volume={14}, ISSN={["2150-8925"]}, url={https://doi.org/10.1002/ecs2.4740}, DOI={10.1002/ecs2.4740}, abstractNote={Abstract}, number={12}, journal={ECOSPHERE}, author={Montgomery, Kellyn and Walden-Schreiner, Chelsey and Saffer, Ariel and Jones, Chris and Seliger, Benjamin J. and Worm, Thom and Tateosian, Laura and Shukunobe, Makiko and Kumar, Sunil and Meentemeyer, Ross K.}, year={2023}, month={Dec} }
 @article{sanchez_galvis_cardenas_corzo_jones_machado_2023, title={Spatiotemporal relative risk distribution of porcine reproductive and respiratory syndrome virus in the United States}, volume={10}, ISSN={["2297-1769"]}, url={http://dx.doi.org/10.3389/fvets.2023.1158306}, DOI={10.3389/fvets.2023.1158306}, abstractNote={Porcine reproductive and respiratory syndrome virus (PRRSV) remains widely distributed across the U.S. swine industry. Between-farm movements of animals and transportation vehicles, along with local transmission are the primary routes by which PRRSV is spread. Given the farm-to-farm proximity in high pig production areas, local transmission is an important pathway in the spread of PRRSV; however, there is limited understanding of the role local transmission plays in the dissemination of PRRSV, specifically, the distance at which there is increased risk for transmission from infected to susceptible farms. We used a spatial and spatiotemporal kernel density approach to estimate PRRSV relative risk and utilized a Bayesian spatiotemporal hierarchical model to assess the effects of environmental variables, between-farm movement data and on-farm biosecurity features on PRRSV outbreaks. The maximum spatial distance calculated through the kernel density approach was 15.3 km in 2018, 17.6 km in 2019, and 18 km in 2020. Spatiotemporal analysis revealed greater variability throughout the study period, with significant differences between the different farm types. We found that downstream farms (i.e., finisher and nursery farms) were located in areas of significant-high relative risk of PRRSV. Factors associated with PRRSV outbreaks were farms with higher number of access points to barns, higher numbers of outgoing movements of pigs, and higher number of days where temperatures were between 4°C and 10°C. Results obtained from this study may be used to guide the reinforcement of biosecurity and surveillance strategies to farms and areas within the distance threshold of PRRSV positive farms.}, journal={FRONTIERS IN VETERINARY SCIENCE}, publisher={Frontiers Media SA}, author={Sanchez, Felipe and Galvis, Jason A. and Cardenas, Nicolas C. and Corzo, Cesar and Jones, Christopher and Machado, Gustavo}, year={2023}, month={Jun} }
 @article{saville_mcgrath_jones_polo_ristaino_2023, title={Understanding the Genotypic and Phenotypic Structure and Impact of Climate on <i>Phytophthora nicotianae</i> Outbreaks on Potato and Tomato in the Eastern United States}, volume={113}, ISSN={["1943-7684"]}, url={http://dx.doi.org/10.1094/phyto-11-22-0411-r}, DOI={10.1094/phyto-11-22-0411-r}, abstractNote={ Samples from potato fields with lesions with late blight-like symptoms were collected from eastern North Carolina in 2017 and the causal agent was identified as Phytophthora nicotianae. We have identified P. nicotianae in potato and tomato samples from North Carolina, Virginia, Maryland, Pennsylvania, and New York. Ninety-two field samples were collected from 46 fields and characterized for mefenoxam sensitivity, mating type, and simple sequence repeat genotype using microsatellites. Thirty-two percent of the isolates were the A1 mating type, while 53% were the A2 mating type. In six cases, both A1 and A2 mating types were detected in the same field in the same year. All isolates tested were sensitive to mefenoxam. Two genetic groups were discerned based on STRUCTURE analysis: one included samples from North Carolina and Maryland, and one included samples from all five states. The data suggest two different sources of inoculum from the field sites sampled. Multiple haplotypes within a field and the detection of both mating types in close proximity suggests that P. nicotianae may be reproducing sexually in North Carolina. There was a decrease in the average number of days with weather suitable for late blight, from 2012 to 2016 and 2017 to 2021 in all of the North Carolina counties where P. nicotianae was reported. P. nicotianae is more thermotolerant than P. infestans and grows at higher temperatures (25 to 35°C) than P. infestans (18 to 22°C). Late blight outbreaks have decreased in recent years and first reports of disease are later, suggesting that the thermotolerant P. nicotianae may cause more disease as temperatures rise due to climate change. }, number={8}, journal={PHYTOPATHOLOGY}, publisher={Scientific Societies}, author={Saville, Amanda C. and McGrath, Margaret T. and Jones, Chris and Polo, John and Ristaino, Jean B.}, year={2023}, month={Aug}, pages={1506–1514} }
 @article{jones_skrip_seliger_jones_wakie_takeuchi_petras_petrasova_meentemeyer_2022, title={Spotted lanternfly predicted to establish in California by 2033 without preventative management}, volume={5}, ISSN={["2399-3642"]}, url={https://doi.org/10.1038/s42003-022-03447-0}, DOI={10.1038/s42003-022-03447-0}, abstractNote={Abstract}, number={1}, journal={COMMUNICATIONS BIOLOGY}, author={Jones, Chris and Skrip, Megan M. and Seliger, Benjamin J. and Jones, Shannon and Wakie, Tewodros and Takeuchi, Yu and Petras, Vaclav and Petrasova, Anna and Meentemeyer, Ross K.}, year={2022}, month={Jun} }
 @article{galvis_jones_prada_corzo_machado_2022, title={The between-farm transmission dynamics of porcine epidemic diarrhoea virus: A short-term forecast modelling comparison and the effectiveness of control strategies}, volume={69}, ISSN={["1865-1682"]}, url={https://doi.org/10.1111/tbed.13997}, DOI={10.1111/tbed.13997}, abstractNote={A limited understanding of the transmission dynamics of swine disease is a significant obstacle to prevent and control disease spread. Therefore, understanding between-farm transmission dynamics is crucial to developing disease forecasting systems to predict outbreaks that would allow the swine industry to tailor control strategies. Our objective was to forecast weekly Porcine Epidemic Diarrhea virus (PEDV) outbreaks by generating maps to identify current and future PEDV high-risk areas, and simulating the impact of control measures. Three epidemiological transmission models were developed and compared: a novel epidemiological modelling framework was developed specifically to model disease spread in swine populations, PigSpread, and two models built on previously developed ecosystems; SimInf (a stochastic disease spread simulations) and PoPS (Pest or Pathogen Spread). The models were calibrated on true weekly PEDV outbreaks from three spatially related swine production companies. Prediction accuracy across models was compared using the receiver operating characteristic area under the curve (AUC). Model outputs had a general agreement with observed outbreaks throughout the study period. PoPS had an AUC of 0.80, followed by PigSpread with 0.71, and SimInf had the lowest at 0.59. Our analysis estimates that the combined strategies of herd closure, controlled exposure of gilts to live viruses (feedback) and on-farm biosecurity reinforcement reduced the number of outbreaks. On average, 76% to 89% reduction was seen in sow farms, while in gilt development units (GDU) was between 33% to 61% when deployed to sow and GDU farms located in probabilistic high-risk areas. Our multi-model forecasting approach can be used to prioritize surveillance and intervention strategies for PEDV and other diseases potentially leading to more resilient and healthier pig production systems.}, number={2}, journal={TRANSBOUNDARY AND EMERGING DISEASES}, publisher={Wiley}, author={Galvis, Jason A. and Jones, Chris M. and Prada, Joaquin M. and Corzo, Cesar A. and Machado, Gustavo}, year={2022}, month={Mar}, pages={396–412} }
 @article{gaydos_jones_jones_millar_petras_petrasova_mitasova_meentemeyer_2021, title={Evaluating online and tangible interfaces for engaging stakeholders in forecasting and control of biological invasions}, volume={31}, ISSN={["1939-5582"]}, url={https://doi.org/10.1002/eap.2446}, DOI={10.1002/eap.2446}, abstractNote={Abstract}, number={8}, journal={ECOLOGICAL APPLICATIONS}, publisher={Wiley}, author={Gaydos, Devon A. and Jones, Chris M. and Jones, Shannon K. and Millar, Garrett C. and Petras, Vaclav and Petrasova, Anna and Mitasova, Helena and Meentemeyer, Ross K.}, year={2021}, month={Sep} }
 @article{jones_jones_petrasova_petras_gaydos_skrip_takeuchi_bigsby_meentemeyer_2021, title={Iteratively forecasting biological invasions with PoPS and a little help from our friends}, volume={19}, ISSN={["1540-9309"]}, url={http://dx.doi.org/10.1002/fee.2357}, DOI={10.1002/fee.2357}, abstractNote={Ecological forecasting has vast potential to support environmental decision making with repeated, testable predictions across management‐relevant timescales and locations. Yet resource managers rarely use co‐designed forecasting systems or embed them in decision making. Although prediction of planned management outcomes is particularly important for biological invasions to optimize when and where resources should be allocated, spatial–temporal models of spread typically have not been openly shared, iteratively updated, or interactive to facilitate exploration of management actions. We describe a species‐agnostic, open‐source framework – called the Pest or Pathogen Spread (PoPS) Forecasting Platform – for co‐designing near‐term iterative forecasts of biological invasions. Two case studies are presented to demonstrate that iterative calibration yields higher forecast skill than using only the earliest‐available data to predict future spread. The PoPS framework is a primary example of an ecological forecasting system that has been both scientifically improved and optimized for real‐world decision making through sustained participation and use by management stakeholders.}, number={7}, journal={FRONTIERS IN ECOLOGY AND THE ENVIRONMENT}, publisher={Wiley}, author={Jones, Chris M. and Jones, Shannon and Petrasova, Anna and Petras, Vaclav and Gaydos, Devon and Skrip, Megan M. and Takeuchi, Yu and Bigsby, Kevin and Meentemeyer, Ross K.}, year={2021}, month={Jun} }
 @article{ristaino_anderson_bebber_brauman_cunniffe_fedoroff_finegold_garrett_gilligan_jones_et al._2021, title={The persistent threat of emerging plant disease pandemics to global food security}, volume={118}, ISSN={["0027-8424"]}, url={https://doi.org/10.1073/pnas.2022239118}, DOI={10.1073/pnas.2022239118}, abstractNote={Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover, and evolution of new pathogen lineages. In order to tackle these grand challenges, a new set of tools that include disease surveillance and improved detection technologies including pathogen sensors and predictive modeling and data analytics are needed to prevent future outbreaks. Herein, we describe an integrated research agenda that could help mitigate future plant disease pandemics.}, number={23}, journal={PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA}, publisher={Proceedings of the National Academy of Sciences}, author={Ristaino, Jean B. and Anderson, Pamela K. and Bebber, Daniel P. and Brauman, Kate A. and Cunniffe, Nik J. and Fedoroff, Nina V and Finegold, Cambria and Garrett, Karen A. and Gilligan, Christopher A. and Jones, Christopher M. and et al.}, year={2021}, month={Jun} }
 @article{petrasova_gaydos_petras_jones_mitasova_meentemeyer_2020, title={Geospatial simulation steering for adaptive management}, volume={133}, url={https://doi.org/10.1016/j.envsoft.2020.104801}, DOI={10.1016/j.envsoft.2020.104801}, abstractNote={Spatio-temporal simulations are becoming essential tools for decision makers when forecasting future conditions and evaluating effectiveness of alternative decision scenarios. However, lack of interactive steering capabilities limits the value of advanced stochastic simulations for research and practice. To address this gap we identified conceptual challenges associated with steering stochastic, spatio-temporal simulations and developed solutions that better represent the realities of decision-making by allowing both reactive and proactive, spatially-explicit interventions. We present our approach, in a participatory modeling case study engaging stakeholders in developing strategies to contain the spread of a tree disease in Oregon, USA. Using intuitive interfaces, implemented through web-based and tangible platforms, stakeholders explored management options as the simulation progressed. Spatio-temporal steering allowed them to combine currently used management practices into novel adaptive management strategies, which were previously difficult to test and assess, demonstrating the utility of interactive simulations for decision-making.}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Petrasova, Anna and Gaydos, Devon A. and Petras, Vaclav and Jones, Chris M. and Mitasova, Helena and Meentemeyer, Ross K.}, year={2020}, month={Nov}, pages={104801} }
 @article{tonini_jones_miranda_cobb_sturtevant_meentemeyer_2018, title={Modeling epidemiological disturbances in LANDIS-II}, volume={41}, ISSN={["1600-0587"]}, url={http://dx.doi.org/10.1111/ecog.03539}, DOI={10.1111/ecog.03539}, abstractNote={Forest landscape simulation models (FLSMs) – often used to understand and project forest dynamics over space and time in response to environmental disturbance – have rarely included realistic epidemiological processes of plant disease transmission and impacts. Landscape epidemiological models, by contrast, frequently treat forest ecosystems as static or make simple assumptions regarding ecosystem change following disease. Here we present the Base Epidemiological Disturbance Agent (EDA) extension that allows users of the LANDIS‐II FLSM to simulate forest pathogen spread and host mortality within a spatially explicit forest simulation. EDA enables users to investigate forest pathogen spread and impacts over large landscapes (> 105 ha) and long time periods. We evaluate the model extension using Phytophthora ramorum as a case study of an invasive plant pathogen causing emerging infectious disease and considerable tree mortality in California. EDA will advance the utility of LANDIS‐II and forest disease modeling in general.}, number={12}, journal={ECOGRAPHY}, author={Tonini, Francesco and Jones, Chris and Miranda, Brian R. and Cobb, Richard C. and Sturtevant, Brian R. and Meentemeyer, Ross K.}, year={2018}, month={Dec}, pages={2038–2044} }
 @article{where’s woolly? an integrative use of remote sensing to improve predictions of the spatial distribution of an invasive forest pest the hemlock woolly adelgid_2015, url={http://dx.doi.org/10.1016/j.foreco.2015.09.013}, DOI={10.1016/j.foreco.2015.09.013}, abstractNote={Non-native pests and pathogens present serious challenges to the management of forested ecosystems around the world. Early detection of pest and pathogen invasions may allow timely control and prevention methods to be implemented. Species distribution models (SDMs) and remote sensing (RS) methods have both been used effectively to determine locations of pest and pathogen damage. However, previous work integrating these two methods has rarely used RS metrics that have biological meaning. We use RS difference indices that show changes in forest cover from defoliation in order to map Hemlock Woolly Adelgid (HWA), Adelges tsugae, locations using MaxEnt in the Delaware Water Gap National Recreation Area (DWGNRA). Brightness, greenness, wetness, and Normalized Difference Vegetation Index (NDVI) were calculated from Landsat Thematic Mapper (TM) images for December 1982 and 2010. A difference for each index was created by subtracting the 1982 value from the 2010 value. We compared two models, one using difference indices and the other using 2010 indices along with other ancillary data layers, to determine if the more complicated but more biologically relevant difference indices were necessary for improved model performance. Variables with low importance were removed from both models, leaving NDVI, Wetness, soil, and elevation in the two final models. The difference model had an improvement in accuracy of three percent, across a number of threshold values. Despite this small difference in accuracy, however, the infected area predicted by the difference model (5.1% of total area) was approximately ½ of that predicted by the single year model (9.6% of total area). These results suggest that using remote sensing difference indices improves model accuracy slightly in terms of errors of omission, but also decreases predicted area of forest infestation by about 50%, suggesting that errors of commission would be substantially reduced using the difference approach. This method can provide forest managers more accurate information on the best locations to sample and treat.}, journal={Forest Ecology and Management}, year={2015}, month={Dec} }