This script provides code that demonstrates the general workflow and modeling framework used for analysis in the publication: Humphreys JM, Srygley RB, Lawton D, Hudson A, and Branson DH, 2022. Grasshoppers Exhibit Asynchrony and Spatial Non-Stationarity in Response to the El Nino/Southern and Pacific Decadal Oscillations (in review) For data and code to run this analysis, please see the project Open Science Framework website: https://osf.io/dmyhf/

Code showing analysis of grasshopper recruitment under preferential sampling and density dependence. Code and data: https://osf.io/ny4b5/

This code serves as an example of models and analyses developed to better understand spatial and temporal trends in the risk of avian influenza transmission between wild waterfowl and domestic poultry. This study is in early stages and should be considered preliminary and exploratory only.

Hare SVC Nov 21, 2019.

Example using the Global Historical Climatology to get rain data.

After removing the year 2000.

Addition of MI vs. WI.

Movement analyses are applied to evaluate the relationship of blue-winged teal (BWTE) behavioral states and migration timing to avian influenza (AI) surveillance and documented outbreaks. BWTE land cover associations and proximity to commercial poultry abundance are also assessed.

A spatial-temporal model is used to estimate timing of reservoir migration with each of four major flyways allowed to independently vary. Results are compared to commercial poultry abundance across the U.S. to assess Exposure of poultry to the AI host.

Accounting for outbreak clusters and potential reservoir movement in disease modeling: Joint spatial modeling of avian influenza and blue-winged teal.

Buffer domain at 15km

Influence of density dependence on body condition and fecundity.

Reduced domain

Large Domain

Joint Model for detection-occupancy with non-stationary spatial field and regionally varying environmental covariates.

Joint Model for detection-occupancy with non-stationary spatial field and regionally varying environmental covariates.

Joint Model for detection-occupancy with non-stationary spatial field and regionally varying environmental covariates.

Density dependent body weight, body fat, and fecundity.

Mallard ducks in South Carolina.

Spatio-temporal modeling of 30 year deer antler diameter data using space-time interaction.

Non-stationary spatial fields with physical barriers.

Accounting for outbreak clusters and potential vector movement in disease modeling: Joint spatial modeling of avian influenza and blue-winged teal.

Joint modeling of disease outbreaks and animal movement telemetry.

Edge density for select locations and buffer distances.

Edge Density Example

Cleaning spatial telemetry data

Function to perform a series of change-point analyses on telemetry data.

A Bayesian approach to spatial-temporal multi-species joint model for vegetation dynamics.

Joint-modeling of environmental, morphometric, and phylogenetic data

Joint-modeling of environmental, morphometric, and phylogenetic data

Comparing Bayesian and Likelihood trees.

Data Pre-processing for Lygodium microphyllum SDM. A Bayesian geostatistical approach to modelling global distributions of Lygodium microphyllum under projected climate warming.

Lygodium microphyllum (Old World Climbing Fern). Global distribution using the r-INLA SPDE approach.

Phyllotis Systematics

Applying the Simpson and Jacard similarity indices to compare mammal composition across Philippine Islands.