136 research outputs found
A Statistical Social Network Model for Consumption Data in Food Webs
We adapt existing statistical modeling techniques for social networks to
study consumption data observed in trophic food webs. These data describe the
feeding volume (non-negative) among organisms grouped into nodes, called
trophic species, that form the food web. Model complexity arises due to the
extensive amount of zeros in the data, as each node in the web is predator/prey
to only a small number of other trophic species. Many of the zeros are regarded
as structural (non-random) in the context of feeding behavior. The presence of
basal prey and top predator nodes (those who never consume and those who are
never consumed, with probability 1) creates additional complexity to the
statistical modeling. We develop a special statistical social network model to
account for such network features. The model is applied to two empirical food
webs; focus is on the web for which the population size of seals is of concern
to various commercial fisheries.Comment: On 2013-09-05, a revised version entitled "A Statistical Social
Network Model for Consumption Data in Trophic Food Webs" was accepted for
publication in the upcoming Special Issue "Statistical Methods for Ecology"
in the journal Statistical Methodolog
Latent Causal Socioeconomic Health Index
This research develops a model-based LAtent Causal Socioeconomic Health
(LACSH) index at the national level. We build upon the latent health factor
index (LHFI) approach that has been used to assess the unobservable
ecological/ecosystem health. This framework integratively models the
relationship between metrics, the latent health, and the covariates that drive
the notion of health. In this paper, the LHFI structure is integrated with
spatial modeling and statistical causal modeling, so as to evaluate the impact
of a continuous policy variable (mandatory maternity leave days and
government's expenditure on healthcare, respectively) on a nation's
socioeconomic health, while formally accounting for spatial dependency among
the nations. A novel visualization technique for evaluating covariate balance
is also introduced for the case of a continuous policy (treatment) variable. We
apply our LACSH model to countries around the world using data on various
metrics and potential covariates pertaining to different aspects of societal
health. The approach is structured in a Bayesian hierarchical framework and
results are obtained by Markov chain Monte Carlo techniques.Comment: 31 pages. arXiv admin note: substantial text overlap with
arXiv:1911.0051
Spatiotemporal Modeling of Nursery Habitat Using Bayesian Inference: Environmental Drivers of Juvenile Blue Crab Abundance
Nursery grounds provide conditions favorable for growth and survival of juvenile fish and crustaceans through abundant food resources and refugia, and enhance secondary production of populations. While small-scale studies remain important tools to assess nursery value of structured habitats and environmental factors, targeted applications that unify survey data over large spatial and temporal scales are vital to generalize inference of nursery function, identify highly productive regions, and inform management strategies. Using 21 years of spatio-temporally indexed survey data (i.e., water chemistry, turbidity, blue crab, and predator abundance) and GIS information on potential nursery habitats (i.e., seagrass, salt marsh, and unvegetated shallow bottom), we constructed five Bayesian hierarchical models with varying spatial and temporal dependence structures to infer variation in nursery habitat value for young juveniles (20–40 mm carapace width) of the blue crab Callinectes sapidus within three tributaries (James, York and Rappahannock Rivers) in lower Chesapeake Bay. Out-of-sample predictions of juvenile blue crab counts from a model considering fully nonseparable spatiotemporal dependence outperformed predictions from simpler models. Salt marsh surface area and turbidity were the strongest determinants of crab abundance (positive association in both cases). Highest crab abundances occurred near the turbidity maximum where relative salt marsh area was greatest. Relative seagrass area, which has been emphasized as the most valuable nursery in studies conducted at small spatial scales, was not associated with high crab abundance within the three tributaries. Hence, salt marshes should be considered a key nursery habitat for the blue crab, even where extensive seagrass beds occur. The patterns between juvenile blue crab abundance and environmental variables also indicated that identification of nurseries should be based on investigations at broad spatial and temporal scales incorporating multiple potential nursery habitats, and based on statistical analyses that address spatial and temporal statistical dependence
Enhancing assessments of blue carbon stocks in marsh soils using Bayesian mixed-effects modeling with spatial autocorrelation — proof of concept using proxy data
Our paper showcases the potential gain in scientific insights about blue carbon stocks (or total organic carbon) when additional rigor, in the form of a spatial autocorrelation component, is formally incorporated into the statistical model for assessing the variability in carbon stocks. Organic carbon stored in marsh soils, or blue carbon (BC), is important for sequestering carbon from the atmosphere. The potential for marshes to store carbon dioxide, mitigating anthropogenic contributions to the atmosphere, makes them a critical conservation target, but efforts have been hampered by the current lack of robust methods for assessing the variability of BC stocks at different geographic scales. Statistical model-based extrapolation of information from soil cores to surrounding tidal marshes, with rigorous uncertainty estimates, would allow robust characterization of spatial variability in many unsampled coastal habitats
Assessing the Health of Richibucto Estuary with the Latent Health Factor Index
The ability to quantitatively assess the health of an ecosystem is often of
great interest to those tasked with monitoring and conserving ecosystems. For
decades, research in this area has relied upon multimetric indices of various
forms. Although indices may be numbers, many are constructed based on
procedures that are highly qualitative in nature, thus limiting the
quantitative rigour of the practical interpretations made from these indices.
The statistical modelling approach to construct the latent health factor index
(LHFI) was recently developed to express ecological data, collected to
construct conventional multimetric health indices, in a rigorous quantitative
model that integrates qualitative features of ecosystem health and preconceived
ecological relationships among such features. This hierarchical modelling
approach allows (a) statistical inference of health for observed sites and (b)
prediction of health for unobserved sites, all accompanied by formal
uncertainty statements. Thus far, the LHFI approach has been demonstrated and
validated on freshwater ecosystems. The goal of this paper is to adapt this
approach to modelling estuarine ecosystem health, particularly that of the
previously unassessed system in Richibucto in New Brunswick, Canada. Field data
correspond to biotic health metrics that constitute the AZTI marine biotic
index (AMBI) and abiotic predictors preconceived to influence biota. We also
briefly discuss related LHFI research involving additional metrics that form
the infaunal trophic index (ITI). Our paper is the first to construct a
scientifically sensible model to rigorously identify the collective explanatory
capacity of salinity, distance downstream, channel depth, and silt-clay content
--- all regarded a priori as qualitatively important abiotic drivers ---
towards site health in the Richibucto ecosystem.Comment: On 2013-05-01, a revised version of this article was accepted for
publication in PLoS One. See Journal reference and DOI belo
Influences of Host Community Characteristics on Borrelia burgdorferi Infection Prevalence in Blacklegged Ticks
Lyme disease is a major vector-borne bacterial disease in the USA. The disease is caused by Borrelia burgdorferi, and transmitted among hosts and humans, primarily by blacklegged ticks (Ixodes scapularis). The ~25 B. burgdorferi genotypes, based on genotypic variation of their outer surface protein C (ospC), can be phenotypically separated as strains that primarily cause human diseases—human invasive strains (HIS)—or those that rarely do. Additionally, the genotypes are non-randomly associated with host species. The goal of this study was to examine the extent to which phenotypic outcomes of B. burgdorferi could be explained by the host communities fed upon by blacklegged ticks. In 2006 and 2009, we determined the host community composition based on abundance estimates of the vertebrate hosts, and collected host-seeking nymphal ticks in 2007 and 2010 to determine the ospC genotypes within infected ticks. We regressed instances of B. burgdorferi phenotypes on site-specific characteristics of host communities by constructing Bayesian hierarchical models that properly handled missing data. The models provided quantitative support for the relevance of host composition on Lyme disease risk pertaining to B. burgdorferi prevalence (i.e. overall nymphal infection prevalence, or NIPAll) and HIS prevalence among the infected ticks (NIPHIS). In each year, NIPAll and NIPHIS was found to be associated with host relative abundances and diversity. For mice and chipmunks, the association with NIPAll was positive, but tended to be negative with NIPHIS in both years. However, the direction of association between shrew relative abundance with NIPAll or NIPHIS differed across the two years. And, diversity (H') had a negative association with NIPAll, but positive association with NIPHIS in both years. Our analyses highlight that the relationships between the relative abundances of three primary hosts and the community diversity with NIPAll, and NIPHIS, are variable in time and space, and that disease risk inference, based on the role of host community, changes when we examine risk overall or at the phenotypic level. Our discussion focuses on the observed relationships between prevalence and host community characteristics and how they substantiate the ecological understanding of phenotypic Lyme disease risk.DB: Burroughs Welcome Fund
(1012376) - http://www.bwfund.org, Lyme
Research Alliance - http://www.
lymeresearchalliance.org, Bay Area Lyme
Foundation - http://www.bayarealyme.org, National
Institutes of Health (AI076342, AI097137) - https://
www.niaid.nih.gov. PES: USDA National Institute of
Food and Agriculture Hatch project (accession number 1005333), “Evolutionary Diversity &
Biogeographic Pattern, Reflecting Ecological &
Anthropogenic Forces,” through the New Jersey
Agricultural Experiment Station, Hatch project
NJ17160, https://nifa.usda.gov/. RSO:
Environmental Protection Agency (STAR Grant
83377601) to RSO and F. Keesing and the National
Science Foundation-National Institutes of Health
joint program in the Ecology of Infectious Diseases
(EF0813035) to F. Keesing and RS
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