scholarly journals Fine-scale spatial patterns of wildlife disease are common and understudied

2020 ◽  
Author(s):  
Gregory F Albery ◽  
Amy R Sweeny ◽  
Daniel J Becker ◽  
Shweta Bansal
Keyword(s):  
Spatial Patterns ◽  
Spatial Data ◽  
Disease Ecology ◽  
Linear Models ◽  
Wild Animal ◽  
Environmental Drivers ◽  
Fine Scale ◽  
Wild Mammals ◽  
Transmission Modes ◽  
Susceptibility Effects

AbstractAll pathogens are heterogeneous in space, yet little is known about the prevalence and scale of this spatial variation, particularly in wild animal systems. To address this question, we conducted a broad literature search to identify datasets involving diseases of wild mammals in spatially distributed contexts. Across 31 such final datasets featuring 89 replicates and 71 host-parasite combinations, only 51% had previously been used to test spatial hypotheses. We analysed these datasets for spatial dependence within a standardised modelling framework using Bayesian linear models. We detected spatial autocorrelation in 44/89 model replicates (54%) across 21/31 datasets (68%), spread across parasites of all groups and transmission modes. Surprisingly, although larger sampling areas more easily detected spatial patterns, even some very small study areas (under 0.01km2) exhibited substantial spatial heterogeneity. Parasites of all transmission modes had easily detectable spatial patterns, implying that structured contact networks and susceptibility effects are likely as important in spatially structuring disease as are environmental drivers of transmission efficiency. Our findings imply that fine-scale spatial patterns of infection often manifest in wild animal systems, whether or not the aim of the study is to examine environmentally varying processes. Given the widespread nature of these findings, studies should more frequently record and analyse spatial data, facilitating development and testing of spatial hypotheses in disease ecology.

scholarly journals Fine-Scale Heterogeneity of a Cold-Water Coral Reef and Its Influence on the Distribution of Associated Taxa

2021 ◽  
Vol 8 ◽  
Author(s):  
David M. Price ◽  
Aaron Lim ◽  
Alexander Callaway ◽  
Markus P. Eichhorn ◽  
Andrew J. Wheeler ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Deep Sea ◽  
Cold Water ◽  
Biological Effects ◽  
Pair Correlation ◽  
Growth Patterns ◽  
Environmental Drivers ◽  
Point Pattern ◽  
Fine Scale ◽  
Water Coral

Benthic fauna form spatial patterns which are the result of both biotic and abiotic processes, which can be quantified with a range of landscape ecology descriptors. Fine- to medium-scale spatial patterns (<1–10 m) have seldom been quantified in deep-sea habitats, but can provide fundamental ecological insights into species’ niches and interactions. Cold-water coral reefs formed by Desmophyllum pertusum (syn. Lophelia pertusa) and Madrepora oculata are traditionally mapped and surveyed with multibeam echosounders and video transects, which limit the ability to achieve the resolution and/or coverage to undertake fine-scale, centimetric quantification of spatial patterns. However, photomosaics constructed from imagery collected with remotely operated vehicles (ROVs) are becoming a prevalent research tool and can reveal novel information at the scale of individual coral colonies. A survey using a downward facing camera mounted on a ROV traversed the Piddington Mound (Belgica Mound Province, NE Atlantic) in a lawnmower pattern in order to create 3D reconstructions of the reef with Structure-from-Motion techniques. Three high resolution orthorectified photomosaics and digital elevation models (DEM) >200 m2 were created and all organisms were geotagged in order to illustrate their point pattern. The pair correlation function was used to establish whether organisms demonstrated a clustered pattern (CP) at various scales. We further applied a point pattern modelling approach to identify four potential point patterns: complete spatial randomness (CSR), an inhomogeneous pattern influenced by environmental drivers, random clustered point pattern indicating biologically driven clustering and an inhomogeneous clustered point pattern driven by a combination of environmental drivers and biological effects. Reef framework presence and structural complexity determined inhabitant distribution with most organisms showing a departure from CSR. These CPs are likely caused by an affinity to local environmental drivers, growth patterns and restricted dispersion reproductive strategies within the habitat across a range of fine to medium scales. These data provide novel and detailed insights into fine-scale habitat heterogeneity, showing that non-random distributions are apparent and detectable at these fine scales in deep-sea habitats.

scholarly journals The fine-scale landscape of immunity and parasitism in a wild ungulate population

2018 ◽  
Author(s):  
Gregory F. Albery ◽  
Daniel J. Becker ◽  
Fiona Kenyon ◽  
Daniel H. Nussey ◽  
Josephine M. Pemberton
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Cervus Elaphus ◽  
Disease Ecology ◽  
Fasciola Hepatica ◽  
Common Source ◽  
Fine Scale ◽  
Animal Populations ◽  
Wide Range

AbstractSpatial heterogeneity in parasite susceptibility and exposure is a common source of confounding variation in disease ecology studies. However, it is not known whether spatial autocorrelation acts on immunity in particular at small scales, within wild animal populations, and whether this predicts spatial patterns in infection. Here we used a well-mixed wild population of individually recognised red deer (Cervus elaphus) inhabiting a heterogeneous landscape to investigate fine-scale spatial patterns of immunity and parasitism. We noninvasively collected 842 faecal samples from 141 females with known ranging behaviour over two years. We quantified total and helminth-specific mucosal antibodies and counted propagules of three gastrointestinal helminth taxa. These data were analysed with linear mixed models using the Integrated Nested Laplace Approximation (INLA), using a Stochastic Partial Differentiation Equation approach (SPDE) to control for and quantify spatial autocorrelation. We also investigated whether spatial patterns of immunity and parasitism changed seasonally. We discovered substantial spatial heterogeneity in general and helminth-specific antibody levels and parasitism with two helminth taxa, all of which exhibited contrasting seasonal variation in their spatial patterns. Notably, strongyle nematode intensity did not align with density hotspots, while Fasciola hepatica intensity appeared to be strongly influenced by the presence of wet grazing. In addition, antibody hotspots did not correlate with distributions of any parasites. Our results suggest spatial heterogeneity may be an important factor affecting immunity and parasitism in a wide range of study systems. We discuss these findings with regards to the design of sampling regimes and public health interventions, and suggest that disease ecology studies investigate spatial heterogeneity more regularly to enhance their results, even when examining small geographic areas.

scholarly journals The Fine-Scale Landscape of Immunity and Parasitism in a Wild Ungulate Population

2019 ◽  
Vol 59 (5) ◽  
pp. 1165-1175 ◽  
Author(s):  
Gregory F Albery ◽  
Daniel J Becker ◽  
Fiona Kenyon ◽  
Daniel H Nussey ◽  
Josephine M Pemberton
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Cervus Elaphus ◽  
Disease Ecology ◽  
Fasciola Hepatica ◽  
Common Source ◽  
Fine Scale ◽  
Animal Populations ◽  
Wide Range

Abstract Spatial heterogeneity in susceptibility and exposure to parasites is a common source of confounding variation in disease ecology studies. However, it is not known whether spatial autocorrelation acts on immunity at small scales, within wild animal populations, and whether this predicts spatial patterns in infection. Here we used a well-mixed wild population of individually recognized red deer (Cervus elaphus) inhabiting a heterogeneous landscape to investigate fine-scale spatial patterns of immunity and parasitism. We noninvasively collected 842 fecal samples from 141 females with known ranging behavior over 2 years. We quantified total and helminth-specific mucosal antibodies and counted propagules of three gastrointestinal helminth taxa. These data were analyzed with linear mixed models using the Integrated Nested Laplace Approximation, using a Stochastic Partial Differentiation Equation approach to control for and quantify spatial autocorrelation. We also investigated whether spatial patterns of immunity and parasitism changed seasonally. We discovered substantial spatial heterogeneity in general and helminth-specific antibody levels and parasitism with two helminth taxa, all of which exhibited contrasting seasonal variation in their spatial patterns. Notably, Fasciola hepatica intensity appeared to be strongly influenced by the presence of wet grazing areas, and antibody hotspots did not correlate with distributions of any parasites. Our results suggest that spatial heterogeneity may be an important factor affecting immunity and parasitism in a wide range of study systems. We discuss these findings with regards to the design of sampling regimes and public health interventions, and suggest that disease ecology studies investigate spatial heterogeneity more regularly to enhance their results, even when examining small geographic areas.

Fine‐scale mapping of deep‐sea habitat‐forming species densities reveals taxonomic specific environmental drivers

2021 ◽  
Author(s):  
Jennifer A. Dijkstra ◽  
Kristen Mello ◽  
Derek Sowers ◽  
Mashkoor Malik ◽  
Les Watling ◽  
...  
Keyword(s):  
Deep Sea ◽  
Environmental Drivers ◽  
Fine Scale ◽  
Fine Scale Mapping

Log-Linear Models with Dependent Spatial Data

1983 ◽  
Vol 15 (6) ◽  
pp. 801-813 ◽  
Author(s):  
B Fingleton
Keyword(s):  
Spatial Data ◽  
Statistical Models ◽  
Linear Models ◽  
Square Lattice ◽  
Categorical Variables ◽  
Systematic Sampling ◽  
Pairwise Correlations ◽  
Independent Observations ◽  
Intersection Points ◽  
Log Linear

Log-linear models are an appropriate means of determining the magnitude and direction of interactions between categorical variables that in common with other statistical models assume independent observations. Spatial data are often dependent rather than independent and thus the analysis of spatial data by log-linear models may erroneously detect interactions between variables that are spurious and are the consequence of pairwise correlations between observations. A procedure is described in this paper to accommodate these effects that requires only very minimal assumptions about the nature of the autocorrelation process given systematic sampling at intersection points on a square lattice.

scholarly journals Reproductive performance links to fine-scale spatial patterns of female grey seal relatedness

2001 ◽  
Vol 268 (1468) ◽  
pp. 711-717 ◽  
Author(s):  
P. P. Pomeroy ◽  
J. Worthington Wilmer ◽  
W. Amos ◽  
S. D. Twiss
Keyword(s):  
Spatial Patterns ◽  
Reproductive Performance ◽  
Fine Scale ◽  
Grey Seal

scholarly journals Spatial Patterns of Brown Rot Epidemics and Development of Microsatellite Markers for Analyzing Fine-Scale Genetic Structure of Monilinia fructicola Populations Within Peach Tree Canopies

2012 ◽  
Vol 13 (1) ◽  
pp. 28 ◽  
Author(s):  
S. E. Everhart ◽  
A. Askew ◽  
L. Seymour ◽  
T. C. Glenn ◽  
H. Scherm
Keyword(s):  
Microsatellite Markers ◽  
Spatial Patterns ◽  
Spatial Dynamics ◽  
Brown Rot ◽  
Fruit Rot ◽  
Monilinia Fructicola ◽  
Peach Tree ◽  
Fine Scale ◽  
Tree Canopies ◽  
Diseased Fruit

To better understand the fine-scale spatial dynamics of brown rot disease and corresponding fungal genotypes, we analyzed three-dimensional spatial patterns of pre-harvest fruit rot caused by Monilinia fructicola in individual peach tree canopies and developed microsatellite markers for canopy-level population genetics analyses. Using a magnetic digitizer, high-resolution maps of fruit rot development in five representative trees were generated, and M. fructicola was isolated from each affected fruit. To characterize disease aggregation, nearestneighbor distances among symptomatic fruit were calculated and compared with appropriate random simulations. Within-canopy disease aggregation correlated negatively with the number of diseased fruit per tree (r = −0.827, P = 0.0009), i.e., aggregation was greatest when the number of diseased fruit was lowest. Sixteen microsatellite primers consistently amplified polymorphic regions in a geographically diverse test population of 47 M. fructicola isolates. None of the test isolates produced identical multilocus genotypes, and the number of alleles per locus ranged from 2 to 16. We are applying these markers to determine fine-scale population structure of the pathogen within and among canopies. Accepted for publication 23 May 2012. Published 23 July 2012.

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