The spatial–temporal relationship of blue‐winged teal to domestic poultry: Movement state modelling of a highly mobile avian influenza host

Abstract

1. Migratory waterfowl facilitate long-distance dispersal of zoonotic pathogens and are increasingly recognized as contributing to the geographic spread of avian influenza viruses (AIVs). AIVs are globally distributed and have the potential to produce highly contagious poultry disease, economically impact both large-scale and backyard poultry producers, and raise the spectre of epidemics and pandemics in human populations.2. Because migratory waterfowl behaviour varies across multiple spatial and temporal scales, the timing and distribution of wild bird AIV introductions to poultry are also heterogeneous in time and space. To help reduce economic impacts to

Wild Bird Densities and Landscape Variables Predict Spatial Patterns in HPAI Outbreak Risk across The Netherlands

“…Previous studies mostly tried to explain the variation in the occurrence of HPAIV introductions with environmental variables, such as distance to waterways and vegetation index [ 18 , 19 , 33 ]. Others also included surveillance or telemetry data to track the movements of a selection of wild bird species in a certain time period [ 17 , 29 , 32 , 34 , 35 , 36 ], but most studies did not include detailed count data on wild bird species distribution to analyze HPAIV introductions on poultry farms. Environmental variables can be considered as a proxy for habitat suitability for wild birds, and were, in our study, less suitable predictors than the densities of the actual bird species.…”

“…Besides the spatial distribution of wild birds, seasonality and the arrival of migratory birds also play a role in the prediction of HPAI outbreak risk [ 20 , 30 , 36 , 37 , 38 ]. For example, Velkers et al (2020) found that the timing of peak densities of Anatidae species observed around Dutch farms coincided with the timing of outbreaks [ 20 ].…”

Behavioral states in space and time: understanding landscape use by an invasive mammal

“…Based on United States Department of Agriculture culling records, remote‐sensing camera photographs, and public reports during and after our study, we estimated that this sample represented 20–25% of the wild pig population in this study area. To model all behavioral states concurrently (3 states identified by the gap statistic method for optimal clustering), we used the stochastic partial differential equation method (Lindgren et al 2011, Krainski et al 2018) and constructed a 3‐level joint model with shared spatial components as described by Humphreys et al (2021). This approach enabled each behavioral state to be evaluated within a dedicated model level while also accounting for spatial relationships.…”

The SPDE approach for Gaussian and non-Gaussian fields: 10 years and still running

“…As an initial illustration of the method's practical relevance, we here present an incomplete list of recent applications. In the time-period of May-Sep 2021, we find applications of the SPDE-approach to Gaussian fields in astronomy (Levis et al, 2021), health (Mannseth et al, 2021;Scott, 2021;Moses et al, 2021;Bertozzi-Villa et al, 2021;Moraga et al, 2021;Asri and Benamirouche, 2021), engineering (Zhang et al, 2021), theory (Ghattas and Willcox, 2021;Sanz-Alonso and Yang, 2021a;Lang and Pereira, 2021;Bolin and Wallin, 2021), environmetrics Beloconi et al, 2021;Vandeskog et al, 2021a;Wang and Zuo, 2021;Wright et al, 2021;Gómez-Catasús et al, 2021;Valente and Laurini, 2021b;Bleuel et al, 2021;Florêncio et al, 2021;Valente and Laurini, 2021a;Hough et al, 2021), econometrics (Morales and Laurini, 2021;Maynou et al, 2021), agronomy (Borges da Silva et al, 2021), ecology (Martino et al, 2021;Sicacha-Parada et al, 2021;Williamson et al;Bell et al, 2021;Humphreys et al;Xi et al, 2021;Fecchio et al), urban planning (Li, 2021), imaging (Aquino et al, 2021), modelling of forest fires (Taylor et al; Lindenmayer et al), fisheries (Babyn et al, 2021;van Woesik and Cacciapaglia, 2021;Jarvis et al, 2021;…”