KrigR—a tool for downloading and statistically downscaling climate reanalysis data

Kusch, Erik; Davy, Richard

doi:10.1088/1748-9326/ac48b3

Cited by 26 publications

(25 citation statements)

References 37 publications

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“…Beyond understanding the dynamics of disease vector populations and disease ecology applications, the application of kriging in conjunction with organismal life cycles will be useful in the context of conservation planning (Kusch & Davy 2022). The ability to examine highly localized and variable conditions that precede ecological events and the distributions of seasonal phenological phenomena Park et al 2021) will have broad applicability across ecological questions and ecosystems.…”

Section: Discussionmentioning

confidence: 99%

Precipitation's complicated role in driving the abundance of an emerging disease vector in an urban, arid landscape

Newman

Feng

Walker³

et al. 2022

Preprint

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Understanding drivers of disease vectors’ population dynamics is a pressing challenge for human health, however, for short-lived organisms like mosquitoes, landscape-scale models must account for the highly local and rapid scale of their life cycle. Aedes aegypti, a vector of multiple emerging diseases, has been increasing in abundance in desert population centers, where water from precipitation could be a limiting factor. To explain this apparent paradox, we examined daily precipitation and Ae. aegypti abundances at >660 trapping locations per year for 3 years in the urbanized Maricopa County (metropolitan Phoenix), Arizona, USA. Through kriging of weather station data, we connected daily precipitation to subsequent trapped abundances of mosquitoes, and determined the timing and amount of precipitation that result in thresholds of interference with mosquito abundance. Large rainfall events resulted in no trapped mosquitoes 6-8 and 13-14 days later, while 10% of all mosquitoes were trapped in long, precipitation-free periods.

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Section: Discussionmentioning

confidence: 99%

Precipitation's complicated role in driving the abundance of an emerging disease vector in an urban, arid landscape

Newman

Feng

Walker³

et al. 2022

Preprint

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“…Plant disease epidemiologists have long recognized the triad of host, pathogen, and environment, but have been hampered by the lack of high-quality environmental data at the spatial scale of their field studies. Access to environmental, agronomic, and weather data at finer spatial scales than the on-ground weather station networks has typically been out of reach of the applied agricultural researcher or has been proprietary ( 27), but the recent increase in available API scripts is breaking down the barriers (28,29). Plant disease epidemiological studies making use of downscaled gridded data, from one or multiple sources, are infrequent (30)(31)(32).…”

Section: Discussion a Workflow For The Integration Of Environmental D...mentioning

confidence: 99%

“…In the case of soil moisture, the downscaled soil temperature was used as the covariate in the kriging model. The kriging process was performed in R version 4.2 (53) using the 'krigR' package (29). A flow chart of the weather data processing is displayed in Fig.…”

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confidence: 99%

From reanalysis data to inference: a framework for linking environment to plant disease epidemics at the regional scale

Alves¹,

Shah²,

Dillard³

et al. 2022

Preprint

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Traditional linear models can be too simplistic for capturing the myriad of interactions that occur among the triad of plant, pathogen and environment that results in plant disease epidemics. Tree-based machine learning (ML) algorithms are an attractive modeling solution because they automatically capture interactions, but work best when trained on a large input predictor matrix. In this study, multiple environmental and soil factors were collated from freely available gridded datasets and downscaled to better match the locations of snap bean fields evaluated for white mold across the central and western New York (NY) landscape. Functional data analysis of downscaled weather time series relative to planting was used to extract succinct summaries associated with disease occurrence. Summaries were input into a data matrix for training an ensemble tree model (XGBoost) fitted to prevalence of white mold. Environmental variables were ranked based on the contribution of their SHapley Additive exPlanations values to the fitted model. Nonlinear effects of weather and soil variables within the flowering period were detected. Most unexpectedly, air and soil temperatures at planting and in the weeks thereafter were associated with disease, which is not manifest until at least 40 days later. This study used a workflow of downscaled, gridded weather data, predictor selection, and ML model interpretation to enhance the understanding of environmental effects at a regional scale on one of the most prevalent and economically important diseases of snap beans. The proposed conceptual framework is broadly applicable to the regional prediction of other plant diseases.

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“…Methodology used here to obtain ERA5(‐Land) data and statistical interpolation thereof has been made available by Kusch and Davy (2022).…”

Section: Methodsmentioning

confidence: 99%

Vegetation‐memory effects and their association with vegetation resilience in global drylands

2022

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There is a growing concern that future ecosystem-disturbance dynamics will exceed ecological tipping points and cause nonlinear shifts in ecosystem states. Examples of non-linear shifts in ecosystem states include regime shifts from tropical forest to savannas (Senf & Seidl, 2018), from coral-dominated to algae-dominated coral reefs (Nyström & Folke, 2001), and eutrophication and acidification processes in shallow lake ecosystems (Spears et al., 2017). Such regime shifts can have major local and global impacts with consequences for

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KrigR—a tool for downloading and statistically downscaling climate reanalysis data

Cited by 26 publications

References 37 publications

Precipitation's complicated role in driving the abundance of an emerging disease vector in an urban, arid landscape

Precipitation's complicated role in driving the abundance of an emerging disease vector in an urban, arid landscape

From reanalysis data to inference: a framework for linking environment to plant disease epidemics at the regional scale

Vegetation‐memory effects and their association with vegetation resilience in global drylands

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