Modelling environmentally-mediated infectious diseases of humans: transmission dynamics of schistosomiasis in China.

Macroparasites of humans are sensitive to a variety of environmental variables, including temperature, rainfall and hydrology, yet current comprehension of these relationships is limited. Given the incomplete mechanistic understanding of environment-disease interactions, mathematical models that describe them have seldom included the effects of time-varying environmental processes on transmission dynamics and where they have been included, simple generic, periodic functions are usually used. Few examples exist where seasonal forcing functions describe the actual processes underlying the environmental drivers of disease dynamics. Transmission of human schistosomes, which involves multiple environmental stages, offers a model for applying our understanding of the environmental determinants of the viability, longevity, infectivity and mobility of these stages to controlling disease in diverse environments. Here, a mathematical model of schistosomiasis transmission is presented which incorporates the effects of environmental variables on transmission. Model dynamics are explored and several key extensions to the model are proposed

Integrative geospatial modeling: combining local and indigenous knowledge with geospatial applications for adaptive governance of invasive species and ecosystem services

Includes bibliographical references.2015 Summer.With an unprecedented rate of global change, diverse anthropogenic disturbances present growing challenges for coupled social-ecological systems. Biological invasions are one such disturbance known to cause negative impacts on biodiversity, ecosystem functioning and an array of other natural processes and human activities. Maps facilitated by advanced geospatial applications play a major role in resource management and conservation planning. However, local and indigenous knowledge are overwhelmingly left out of these conversations, despite the wealth of observational data held by resource-dependent communities and the potential negative impacts biological invasions have on local livelihoods. My integrative geospatial modeling research applied adaptive governance mechanisms of knowledge integration and co-production processes in concert with species distribution modeling tools to explore the potential threat of invasive plants to community-defined ecosystem services. Knowledge integration at the landscape scale in Alaska provided an important opportunity for re-framing risk assessment mapping to include Native Alaskan community concerns, and revealed the growing potential threat posed by invasive aquatic Elodea spp. to Chinook salmon (Oncorhynchus tshawytscha) and whitefish (Coregonus nelsonii) subsistence under current and future climate conditions. Knowledge integration and co-production at the local scale in northeastern Ethiopia facilitated shared learning between pastoral communities and researchers, leading to the discovery of invasive rubber vine (Cryptostegia grandiflora), which was previously unknown to my research team or a number of government and aid organizations working in the region, thus providing a potentially robust early detection and monitoring approach for an invasive plant that holds acute negative impacts on a number of endemic ecosystem service-providing trees. This work revealed knowledge integration and co-production processes and species distribution modeling tools to be complimentary, with invasive species acting as a useful boundary-spanning issue for bringing together diverse knowledge sources. Moreover, bridging and boundary-spanning organizations and individuals enhanced this rapid appraisal process by providing access to local and indigenous communities and fostered a level of built-in trust and legitimacy with them. Challenges to this work still remain, including effectively working at broad spatial and governance scales, sustaining iterative processes that involve communities in validating and critiquing model outputs, and addressing underlying power disparities between stakeholder groups. Top-down, discipline-specific approaches fail to adequately address the complexity of ecosystems or the needs of resource-dependent communities. My work lends evidence to the power of integrative geospatial modeling as a flexible transdisciplinary methodology for addressing conservation efforts in rural regions with mounting anthropogenic pressures at different spatial and governance scales

Numerical modeling of thermal bar and stratification pattern in Lake Ontario using the EFDC model

Thermal bar is an important phenomenon in large, temperate lakes like Lake Ontario. Spring thermal bar formation reduces horizontal mixing, which in turn, inhibits the exchange of nutrients. Evolution of the spring thermal bar through Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers, and lake bathymetry. The simulation is performed from April to July, 2011; on a 2-km grid. The numerical model has been calibrated by specifying: appropriate initial temperature and solar radiation attenuation coefficients. The existing evaporation algorithm in EFDC is updated to modified mass transfer approach to ensure correct simulation of evaporation rate and latent heatflux. Reasonable values for mixing coefficients are specified based on sensitivity analyses. The model simulates overall surface temperature profiles well (RMSEs between 1-2°C). The vertical temperature profiles during the lake mixed phase are captured well (RMSEs < 0.5°C), indicating that the model sufficiently replicates the thermal bar evolution process. An update of vertical mixing coefficients is under investigation to improve the summer thermal stratification pattern. Keywords: Hydrodynamics, Thermal BAR, Lake Ontario, GIS

Partitioning the impact of environmental drivers and species interactions in dynamic aquatic communities

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Musters, C. J. M., Ieromina, O., Barmentlo, S. H., Hunting, E. R., Schrama, M., Cieraad, E., Vijver, M. G., & van Bodegom, P. M. Partitioning the impact of environmental drivers and species interactions in dynamic aquatic communities. Ecosphere, 10(11), (2019): e02910, doi:10.1002/ecs2.2910.Temperate aquatic communities are highly diverse and seasonally variable, due to internal biotic processes and environmental drivers, including human‐induced stressors. The impact of drivers on species abundance is supposed to differ fundamentally depending on whether populations are experiencing limitations, which may shift over the season. However, an integrated understanding of how drivers structure communities seasonally is currently lacking. In order to partition the effect of drivers, we used random forests to quantify interactions between all taxa and environmental factors using macrofaunal data from 18 agricultural ditches sampled over two years. We found that, over the agricultural season, taxon abundance became increasingly better predicted by the abundances of co‐occurring taxa and nutrients compared to other abiotic factors, including pesticides. Our approach provides fundamental insights in community dynamics and highlights the need to consider changes in species interactions to understand the effects of anthropogenic stressors.The authors are grateful to B. Schaub of Water Board Rijnland for his help, E. Gertenaar for assistance in the fieldwork, M. Wouterse for DOC measurements, and B. Koese for help with taxonomic identification of macrofaunal samples. CM designed the study, did the statistical modeling and analyses, and wrote the draft paper; OI did field sampling and taxonomic identification and constructed the datasets; OI and HB structured the data; EH, MS, ES, MV, and PvB contributed to the study design and the conceptual improvement of the manuscript; all authors substantially revised the subsequent drafts

Towards high fidelity mapping of global inland water quality using earth observation data

This body of work aims to contribute advancements towards developing globally applicable water quality retrieval models using Earth Observation data for freshwater systems. Eutrophication and increasing prevalence of potentially toxic algal blooms among global inland water bodies have become a major ecological concersn and require direct attention. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products. This study provides one of the first assessments of the state-ofthe-art for trophic status (chlorophyll-a) retrievals for small water bodies using Sentinel-3 Ocean and Land Color Imager (OLCI). Multiple fieldwork campaigns were undertaken for the collection of common aquatic biogeophysical and bio-optical parameters that were used to validate current atmospheric correction and chlorophyll-a retrieval algorithms. The study highlighted the difficulties of obtaining robust retrieval estimates from a coarse spatial resolution sensor from highly variable eutrophic water bodies. Atmospheric correction remains a difficult challenge to operational freshwater monitoring, however, the study further validated previous work confirming applicability of simple, empirically derived retrieval algorithms using top-of-atmosphere data. The apparent scarcity of paired in-situ optical and biogeophysical data for productive inland waters also hinders our capability to develop and validate robust retrieval algorithms. Radiative transfer modeling was used to fill this gap through the development of a novel synthetic dataset of top-of-atmosphere and bottom-of-atmosphere reflectances, which attempts to encompass the immense natural optical variability present in inland waters. Novel aspects of the synthetic dataset include: 1) physics-based, two-layered, size and type specific phytoplankton IOPs for mixed eukaryotic/cyanobacteria 6 assemblages, 2) calculations of mixed assemblage chl-a fluorescence, 3) modeled phycocyanin concentration derived from assemblage based phycocyanin absorption, 4) and paired sensor-specific TOA reflectances which include optically extreme cases and contribution of green vegetation adjacency. The synthetic bottom-of-atmosphere reflectance spectra were compiled into 13 distinct optical water types similar to those discovered using in-situ data. Inspection showed similar relationships and ranges of concentrations and inherent optical properties of natural waters. This dataset was used to calculate typical surviving water-leaving signal at top-of-atmosphere, as well as first order calculations of the signal-to-noise-ratio (SNR) for the various optical water types, a first for productive inland waters, as well as conduct a sensitivity analysis of cyanobacteria detection from top-of-atmosphere. Finally, the synthetic dataset was used to train and test four state-of-the-art machine learning architectures for multi-parameter retrieval and cross-sensor capability. Initial results provide reliable estimates of water quality parameters and inherent optical properties over a highly dynamic range of water types, at various spectral and spatial sensor resolutions. It is hoped the results of this work incrementally improves inland water Earth observation on multiple aspects of the forward and inverse modelling process, and provides an improvement in our capabilities for routine, global monitoring of inland water quality