Prediction of water temperature metrics using spatial modelling in the Eastern and Western Cape, South Africa

Key aspects of a river’s temperature regime are described by magnitudes, timing and durations of thermal events, and frequencies of extreme exceedance events. To understand alterations to thermal regimes, it is necessary to describe thermal time series based on these statistics. Classification of sites based on their thermal met-rics, and understanding of spatial patterns of these thermal statistics, provides a powerful approach for comparing study sites against reference sites. Water tem-perature regime dynamics should be viewed regionally, where regional divisions have an inherent underpinning by an understanding of natural thermal variability. The aim of this research was to link key water temperature metrics to readi-lymapped environmental surrogates, and to produce spatial images of temperature metrics: 37 temperature metrics were derived for 12 months of sub-daily water temperatures at 90 sites in the Eastern Cape and Western Cape provinces, South Africa

Towards assessing impacts of alien plant infestations on river systems in the Southern Cape using cost-benefit analyses

Ecosystem resilience is key to the provision of dependable ecosystem goods and services, and it is generally accepted that ecosystem diversity helps to maintain sys-tem resilience. It is therefore reasonable to postulate that changes to the variables that drive species patterns will result in changes to ecosystem community structure and consequently negatively impact on system resilience. Alien vegetation in the riparian zone can impact on water temperatures, flow patterns, degree of shading, channel modification, and changes to natural sediment loads. Climate change is likely to exacerbate the problem both directly through its amplification of thermal extremes in aquatic systems, and indirectly through its impacts on dispersal patterns of alien invasive vegetation

Links between water temperatures, ecological responses and flow rates: a framework for establishing water temperature guidelines for the ecological reserve

Global ecosystems face unprecedented crises in habitat fragmentation, destruction and ultimately extinction (Groves, 2003), and of all the vary-ing ecological systems rivers are the most neglected and endangered (Groves, 2003; Driver, et al., 2005; Roux et al., 2005). The greatest threat to these systems is the loss or degradation of natural habitat and processes (Driver et al., 2005), and water temperatures, after flow vol-umes, are a primary abiotic driver of species patterns within river sys-tems. Stuckenberg (1969) highlighted the links between temperature, topography and faunal assemblages, while Rivers-Moore et al.(2004) highlights the major impacts of water temperatures on organisms, and illustrate how water temperatures are one of the primary environmental drivers structuring fish communities in the Sabie River, arguably the most icthyologically species-rich river in South Africa

Prediction of Wetland Hydrogeomorphic Type Using Morphometrics and Landscape Characteristics

Accurate spatial maps of wetlands are critical for regional conservation and rehabilitation assessments, yet this often remains an elusive target. Such maps ideally provide information on wetland occurrence and extent, hydrogeomorphic (HGM) type, and ecological condition/level of degradation. All three elements are needed to provide ancillary layers to support mapping from remote imagery and ground-truthing. Knowledge of HGM types is particularly important, because different types show different levels of sensitivity to degradation, and modeling accuracy for occurrence. Here, we develop and test a simple approach for predicting the most likely HGM type for mapped yet unattributed wetland polygons. We used a dataset of some 11,500 wetland polygons attributed by HGM types (floodplain, depression, seep, channeled, and un-channeled valley-bottom) from the Western Cape Province in South Africa. Polygons were attributed and described in terms of nine landscape metrics, at a sub-catchment scale. Using a combination of box-and-whisker plots and PCA, we identified four variables (groundwater depth, relief ratio, slope, and elevation) as being the most important variables in differentiating HGM types. We divided the data into equal parts for training and testing of a simple Bayesian network model. Model validation included field assessments. HGM types were most sensitive to elevation. Model predication was good, with error rates of only 32%. We conclude that this is a useful technique that can be widely applied using readily available data, for rapid classification of HGM types at a regional scale. © Copyright © 2020 Rivers-Moore, Kotze, Job and Mohanlal

How the freshwater biodiversity information system (FBIS) is supporting national freshwater fish conservation decisions in South Africa

DATA AVAILABILITY STATEMENT : The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material. Alternatively, the dataset can be accessed via the Freshwater Biodiversity Information System (freshwaterbiodiversity.org).In South Africa, anthropogenic pressures such as water over-abstraction, invasive species impacts, land-use change, pollution, and climate change have caused widespread deterioration of the health of river ecosystems. This comes at great cost to both people and biodiversity, with freshwater fishes ranked as the country’s most threatened species group. Effective conservation and management of South Africa’s freshwater ecosystems requires access to reliable and comprehensive biodiversity data. Despite the existence of a wealth of freshwater biodiversity data, access to these data has been limited. The Freshwater Biodiversity Information System (FBIS) was built to address this knowledge gap by developing an intuitive, accessible and reliable platform for freshwater biodiversity data in South Africa. The FBIS hosts high quality, high accuracy biodiversity data that are freely available to a wide range of stakeholders, including researchers, conservation practitioners and policymakers. We describe how the system is being used to provide freshwater fish data to a national conservation decision-support tool—The Department of Forestry, Fisheries, and the Environment (DFFE) National Environmental Screening Tool (NEST). The NEST uses empirical and modelled biodiversity data to guide Environmental Impact Assessment Practitioners in conducting environmental assessments of proposed developments. Occurrence records for 34 threatened freshwater fishes occurring in South Africa were extracted from the FBIS and verified by taxon specialists, resulting in 6 660 records being used to generate modelled and empirical national distribution (or sensitivity) layers. This represents the first inclusion of freshwater biodiversity data in the NEST, and future iterations of the tool will incorporate additional freshwater taxa. This case study demonstrates how the FBIS fills a pivotal role in the data-to-decision pipeline through supporting data-driven conservation and management decisions at a national level.Funding for the development of the Freshwater Biodiversity Information System (FBIS) was provided by the JRS Biodiversity Foundation Funding for the development of the Freshwater Biodiversity Information System (FBIS) was provided by the JRS Biodiversity Foundation. This work is based on the research supported in part by the National Research Foundation (NRF) of South Africa and the NRF-SAIAB DSI/ NRF Research Chair in Inland Fisheries and Freshwater Ecology.http://www.frontiersin.org/Environmental_Scienceam2024Zoology and EntomologySDG-14:Life below wate

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Predictive modelling of wetland occurrence in KwaZulu-Natal, South Africa

The global trend of transformation and loss of wetlands through conversion to other land uses has deleterious effects on surrounding ecosystems, and there is a resultant increasing need for the conservation and preservation of wetlands. Improved mapping of wetland locations is critical to achieving objective regional conservation goals, which depends on accurate spatial knowledge. Current approaches to mapping wetlands through the classification of satellite imagery typically under-represents actual wetland area; the importance of ancillary data in improving accuracy in mapping wetlands is therefore recognised. In this study, we compared two approaches – Bayesian networks and logistic regression – to predict the likelihood of wetland occurrence in KwaZulu-Natal, South Africa. Both approaches were developed using the same data set of environmental surrogate predictors. We compared and verified model outputs using an independent test data set, with analyses including receiver operating characteristic curves and area under the curve (AUC). Both models performed similarly (AUC>0.84), indicating the suitability of a likelihood approach for ancillary data for wetland mapping. Results indicated that high wetland probability areas in the final model outputs correlated well with known wetland systems and wetland-rich areas in KwaZulu-Natal. We conclude that predictive models have the potential to improve the accuracy of wetland mapping in South Africa by serving as valuable ancillary data. &nbsp

Relationships between reference site quality and baetid mayfly assemblages in mountainous streams of the Luvuvhu catchment, South Africa

With water quality deteriorating rapidly at a global scale, river sections suited to serve as reference sites are being increasingly lost. It thus becomes critical to develop rapid methods to confirm that previously monitored sites continue meet the requirements of reliable reference sites. In the absence of pristine sites, 9 near-natural sites, as defined by the Kleynhans (1996) classification, were used as reference sites for the Luvuvhu River catchment to compare the quality of physico-chemical factors against a biological metric. Baetid mayfly community structure at a site was chosen as an index of water quality, since this family is common in all types of freshwaters, highly diverse and adapted to unpolluted running water. Baetid larvae were sampled monthly from stones-in-current biotopes across 9 sites for over 1 year, between December 2016 and January 2018. A Spearman’s correlation test was used to evaluate the relationship between physico-chemical factors and identify redundant variables. Water quality standards were measured against the national water quality guidelines for aquatic ecosystems. We used a generalized linear model to determine the effect of physico-chemical variables on baetid species, and canonical correspondence analysis to show the relationships between baetid species, sites, and physico-chemical variables. A total of 3 039 individuals belonging to 12 mayfly species were recorded. Our findings indicated that while the physico-chemical factors were highly variable, they were within favourable ranges to reflect reference site conditions. While water temperature was the most important driver of baetid community structure in general, as it negatively affected their abundances, a subset of species (Pseudoponnota sp., Pseudocloeon sp., Acanthiops varius and Demoulinia crassi) showed clear responses to changes in TDS and stream width. We conclude that specific baetid species show good potential as biological indicators of reference sites and chronic water temperature stress, making assessment of reference sites easier

Trophic overlap between fish and riparian spiders: potential impacts of an invasive fish on terrestrial consumers

Table S1. Characteristics of the 12 sites during the month of sampling.Table S2. Invertebrate resource isotope values and results of ANOVA’s testing for differences between resources and sites.Table S3. Carbon and nitrogen stable isotope values of fish and spiders used in the study.Studies on resource sharing and partitioning generally consider species that occur in the same habitat. However, subsidies between linked habitats, such as streams and riparian zones, create potential for competition between populations which never directly interact. Evidence suggests that the abundance of riparian consumers declines after fish invasion and a subsequent increase in resource sharing of emerging insects. However, diet overlap has not been investigated. Here, we examine the trophic niche of native fish, invasive fish, and native spiders in South Africa using stable isotope analysis. We compared spider abundance and diet at upstream fishless and downstream fish sites and quantified niche overlap with invasive and native fish. Spider abundance was consistently higher at upstream fishless sites compared with paired downstream fish sites, suggesting that the fish reduced aquatic resource availability to riparian consumers. Spiders incorporated more aquatic than terrestrial insects in their diet, with aquatic insects accounting for 45–90% of spider mass. In three of four invaded trout rivers, we found that the average proportion of aquatic resources in web-building spider diet was higher at fishless sites compared to fish sites. The probability of web-building and ground spiders overlapping into the trophic niche of invasive brown and rainbow trout was as high as 26 and 51%, respectively. In contrast, the probability of spiders overlapping into the trophic niche of native fish was always less than 5%. Our results suggest that spiders share resources with invasive fish. In contrast, spiders had a low probability of trophic overlap with native fish indicating that the traits of invaders may be important in determining their influence on ecosystem subsidies. We have added to the growing body of evidence that invaders can have crossecosystem impacts and demonstrated that this can be due to niche overlap.The DST-NRF Centre of Excellence in Invasion Biologyhttp://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2045-7758am2017Zoology and Entomolog