Network analysis of infectious disease in wildlife can reveal traits or individuals critical to pathogen transmission and help inform disease management strategies. However, estimates of contact between animals are notoriously difficult to acquire. Researchers commonly use telemetry technologies to identify animal associations, but such data may have different sampling intervals and often captures a small subset of the population. The objectives of this study were to outline best practices for telemetry sampling in network studies of infectious disease by determining (a) the consequences of telemetry sampling on our ability to estimate network structure, (b) whether contact networks can be approximated using purely spatial contact definitions and (c) how wildlife spatial configurations may influence telemetry sampling requirements. We simulated individual movement trajectories for wildlife populations using a home range‐like movement model, creating full location datasets and corresponding ‘complete’ networks. To mimic telemetry data, we created ‘sample’ networks by subsampling the population (10%–100% of individuals) with a range of sampling intervals (every minute to every 3 days). We varied the definition of contact for sample networks, using either spatiotemporal or spatial overlap, and varied the spatial configuration of populations (random, lattice or clustered). To compare complete and sample networks, we calculated seven network metrics important for disease transmission and assessed mean ranked correlation coefficients and percent error between complete and sample network metrics. Telemetry sampling severely reduced our ability to calculate global node‐level network metrics, but had less impact on local and network‐level metrics. Even so, in populations with infrequent associations, high intensity telemetry sampling may still be necessary. Defining contact in terms of spatial overlap generally resulted in overly connected networks, but in some instances, could compensate for otherwise coarse telemetry data. By synthesizing movement and disease ecology with computational approaches, we characterized trade‐offs important for using wildlife telemetry data beyond ecological studies of individual movement, and found that careful use of telemetry data has the potential to inform network models. Thus, with informed application of telemetry data, we can make significant advances in leveraging its use for a better understanding and management of wildlife infectious disease.
- Award ID(s):
- 1922758
- NSF-PAR ID:
- 10291041
- Date Published:
- Journal Name:
- Forest ecology and management
- Volume:
- 489
- Issue:
- 2021
- ISSN:
- 0378-1127
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract A critical decision in landscape genetic studies is whether to use individuals or populations as the sampling unit. This decision affects the time and cost of sampling and may affect ecological inference. We analyzed 334 Columbia spotted frogs at 8 microsatellite loci across 40 sites in northern Idaho to determine how inferences from landscape genetic analyses would vary with sampling design. At all sites, we compared a proportion available sampling scheme (PASS), in which all samples were used, to resampled datasets of 2–11 individuals. Additionally, we compared a population sampling scheme (PSS) to an individual sampling scheme (ISS) at 18 sites with sufficient sample size. We applied an information theoretic approach with both restricted maximum likelihood and maximum likelihood estimation to evaluate competing landscape resistance hypotheses. We found that PSS supported low‐density forest when restricted maximum likelihood was used, but a combination model of most variables when maximum likelihood was used. We also saw variations when AIC was used compared to BIC. ISS supported this model as well as additional models when testing hypotheses of land cover types that create the greatest resistance to gene flow for Columbia spotted frogs. Increased sampling density and study extent, seen by comparing PSS to PASS, showed a change in model support. As number of individuals increased, model support converged at 7–9 individuals for ISS to PSS. ISS may be useful to increase study extent and sampling density, but may lack power to provide strong support for the correct model with microsatellite datasets. Our results highlight the importance of additional research on sampling design effects on landscape genetics inference.
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Abstract Aim Efforts to predict the responses of soil fungal communities to climate change are hindered by limited information on how fungal niches are distributed across environmental hyperspace. We predict the climate sensitivity of North American soil fungal assemblage composition by modelling the ecological niches of several thousand fungal species.
Location One hundred and thirteen sites in the United States and Canada spanning all biomes except tropical rain forest.
Major Taxa Studied Fungi.
Time Period 2011–2018.
Methods We combine internal transcribed spacer (ITS) sequences from two continental‐scale sampling networks in North America and cluster them into operational taxonomic units (OTUs) at 97% similarity. Using climate and soil data, we fit ecological niche models (ENMs) based on logistic ridge regression for all OTUs present in at least 10 sites (
n = 8597). To describe the compositional turnover of soil fungal assemblages over climatic gradients, we introduce a novel niche‐based metric of climate sensitivity, the Sørensen climate sensitivity index. Finally, we map climate sensitivity across North America.Results ENMs have a mean out‐of‐sample predictive accuracy of 73.8%, with temperature variables being strong predictors of fungal distributions. Soil fungal climate niches clump together across environmental space, which suggests common physiological limits and predicts abrupt changes in composition with respect to changes in climate. Soil fungi in North American climates are more likely to be limited by cold and dry conditions than by warm and wet conditions, and ectomycorrhizal fungi generally tolerate colder temperatures than saprotrophic fungi. Sørensen climate sensitivity exhibits a multimodal distribution across environmental space, with a peak in climates corresponding to boreal forests.
Main Conclusions The boreal forest occupies an especially precarious region of environmental space for the composition of soil fungal assemblages in North America, as even small degrees of warming could trigger large compositional changes characterized mainly by an influx of warm‐adapted species.
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Site description. This data package consists of data obtained from sampling surface soil (the 0-7.6 cm depth profile) in black mangrove (Avicennia germinans) dominated forest and black needlerush (Juncus roemerianus) saltmarsh along the Gulf of Mexico coastline in peninsular west-central Florida, USA. This location has a subtropical climate with mean daily temperatures ranging from 15.4 °C in January to 27.8 °C in August, and annual precipitation of 1336 mm. Precipitation falls as rain primarily between June and September. Tides are semi-diurnal, with 0.57 m median amplitudes during the year preceding sampling (U.S. NOAA National Ocean Service, Clearwater Beach, Florida, station 8726724). Sea-level rise is 4.0 ± 0.6 mm per year (1973-2020 trend, mean ± 95 % confidence interval, NOAA NOS Clearwater Beach station). The A. germinans mangrove zone is either adjacent to water or fringed on the seaward side by a narrow band of red mangrove (Rhizophora mangle). 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The sites consisted of the Salt Springs (28.3298°, -82.7274°), Energy Marine Center (28.2903°, -82.7278°), and Green Key (28.2530°, -82.7496°) sites on the Gulf of Mexico coastline in Pasco County, Florida, USA. At each site, we established three plot pairs, each consisting of one saltmarsh plot and one mangrove plot. Plots were 50 m^2 in size. Plots pairs within a site were separated by 230-1070 m, and the mangrove and saltmarsh plots composing a pair were 70-170 m apart. All plot pairs consisted of directly adjacent patches of mangrove forest and J. roemerianus saltmarsh, with the mangrove forests exhibiting a closed canopy and a tree architecture (height 4-6 m, crown width 1.5-3 m). Mangrove plots were located at approximately the midpoint between the seaward edge (water-mangrove interface) and landward edge (mangrove-marsh interface) of the mangrove zone. Saltmarsh plots were located 20-25 m away from any mangrove trees and into the J. roemerianus zone (i.e., landward from the mangrove-marsh interface). Plot pairs were coarsely similar in geomorphic setting, as all were located on the Gulf of Mexico coastline, rather than within major sheltering formations like Tampa Bay, and all plot pairs fit the tide-dominated domain of the Woodroffe classification (Woodroffe, 2002, "Coasts: Form, Process and Evolution", Cambridge University Press), given their conspicuous semi-diurnal tides. There was nevertheless some geomorphic variation, as some plot pairs were directly open to the Gulf of Mexico while others sat behind keys and spits or along small tidal creeks. Our use of a plot-pair approach is intended to control for this geomorphic variation. 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Abstract 1. The occurrence and distributions of wildlife populations and communities are shifting as a result of global changes. To evaluate whether these shifts are negatively impacting biodiversity processes, it is critical to monitor the status, trends and effects of environmental variables on entire communities. However, modelling the dynamics of multiple species simultaneously can require large amounts of diverse data, and few modelling approaches exist to simultaneously provide species and community‐level inferences.
2. We present an ‘integrated community occupancy model’ (ICOM) that unites principles of data integration and hierarchical community modelling in a single framework to provide inferences on species‐specific and community occurrence dynamics using multiple data sources. The ICOM combines replicated and nonreplicated detection–nondetection data sources using a hierarchical framework that explicitly accounts for different detection and sampling processes across data sources. We use simulations to compare the ICOM to previously developed hierarchical community occupancy models and single species integrated distribution models. We then apply our model to assess the occurrence and biodiversity dynamics of foliage‐gleaning birds in the White Mountain National Forest in the northeastern USA from 2010 to 2018 using three independent data sources.
3. Simulations reveal that integrating multiple data sources in the ICOM increased precision and accuracy of species and community‐level inferences compared to single data source models, although benefits of integration were dependent on the information content of individual data sources (e.g. amount of replication). Compared to single species models, the ICOM yielded more precise species‐level estimates. Within our case study, the ICOM had the highest out‐of‐sample predictive performance compared to single species models and models that used only a subset of the three data sources.
4. The ICOM provides more precise estimates of occurrence dynamics compared to multi‐species models using single data sources or integrated single‐species models. We further found that the ICOM had improved predictive performance across a broad region of interest with an empirical case study of forest birds. The ICOM offers an attractive approach to estimate species and biodiversity dynamics, which is additionally valuable to inform management objectives of both individual species and their broader communities.
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Accepted Manuscript1.0
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