Shortcuts for biomonitoring programs of stream ecosystems: Evaluating the taxonomic, numeric, and cross-taxa congruence in phytoplankton, periphyton, zooplankton, and fish assemblages

Different biological groups can be used for monitoring aquatic ecosystems because they can respond to variations in the environment. However, the evaluation of different bioindicators may demand multiple financial resources and time, especially when abundance quantification and species-level identification are required. In this study, we evaluated whether taxonomic, numerical resolution and cross-taxa can be used to optimize costs and time for stream biomonitoring in Central Brazil (Cerrado biome). For this, we sampled different biological groups (fish, zooplankton, phytoplankton, and periphyton) in stream stretches distributed in a gradient of land conversion dominated by agriculture and livestock. We used the Mantel and Procrustes analyses to test the association among different taxonomic levels (species to class), the association between incidence and abundance data (numerical resolution), and biological groups. We also assessed the relative effect of local environmental and spatial predictors on different groups. The taxonomic levels and numerical resolutions were strongly correlated in all taxonomic groups (r > 0.70). We found no correlations among biological groups. Different sets of environmental variables were the most important to explain the variability in species composition of distinct biological groups. Thus, we conclude that monitoring the streams in this region using bioindicators is more informative through higher taxonomic levels with occurrence data than abundance. However, different biological groups provide complementary information, reinforcing the need for a multi-taxa approach in biomonitoring.

Spatial epidemiological study of the distribution, clustering, and risk factors associated with early COVID-19 mortality in Mexico

COVID-19 is a respiratory disease caused by SARS-CoV-2, which has significantly impacted economic and public healthcare systems worldwide. SARS-CoV-2 is highly lethal in older adults (>65 years old) and in cases with underlying medical conditions, including chronic respiratory diseases, immunosuppression, and cardio-metabolic diseases, including severe obesity, diabetes, and hypertension. The course of the COVID-19 pandemic in Mexico has led to many fatal cases in younger patients attributable to cardio-metabolic conditions. Thus, in the present study, we aimed to perform an early spatial epidemiological analysis for the COVID-19 outbreak in Mexico. Firstly, to evaluate how mortality risk from COVID-19 among tested individuals (MRt) is geographically distributed and secondly, to analyze the association of spatial predictors of MRt across different states in Mexico, controlling for the severity of the disease. Among health-related variables, diabetes and obesity were positively associated with COVID-19 fatality. When analyzing Mexico as a whole, we identified that both the percentages of external and internal migration had positive associations with early COVID-19 mortality risk with external migration having the second-highest positive association. As an indirect measure of urbanicity, population density, and overcrowding in households, the physicians-to-population ratio has the highest positive association with MRt. In contrast, the percentage of individuals in the age group between 10 to 39 years had a negative association with MRt. Geographically, Quintana Roo, Baja California, Chihuahua, and Tabasco (until April 2020) had higher MRt and standardized mortality ratios, suggesting that risks in these states were above what was nationally expected. Additionally, the strength of the association between some spatial predictors and the COVID-19 fatality risk varied by zone.

Does where you live influence what you are made of? Spatial correlates of chemical traits across commonly occurring boreal plants

ContextSpatially explicit drivers of foliar chemical traits link plants to ecosystem processes to reveal landscape functionality. Specifically, foliar elemental, stoichiometric, and phytochemical (ESP) compositions represent key indicator traits.ObjectivesHere, we investigate the spatial drivers of foliar ESP at the species level and across species at the trait level for five commonly occurring boreal forest understory plants.MethodsOn the island of Newfoundland, Canada, we collected foliar material from four chronosequenced forest grids. Using response variables of foliar elemental (C, N, P, percent and quantity), stoichiometric (C:N, C:P, N:P), and phytochemical (terpenoids) composition, we tested multiple competing hypotheses using spatial predictors of land cover (e.g., coniferous, deciduous, mixedwood), productivity (e.g., enhanced vegetation index), biotic (e.g., stand age/height, canopy closure) and abiotic (e.g., elevation, aspect, slope) factors.ResultsWe found evidence to support spatial relationships of foliar ESP for most species (mean R2 = 0.22, max = 0.65). Spatial variation in elemental quantity traits of C, N, P were related to land cover along with biotic and abiotic factors for 2 of 5 focal species. Notably, foliar C, C:P, and sesquiterpene traits between different species were related to abiotic factors. Similarly, foliar terpenoid traits between different species were related to a combination of abiotic and biotic factors (mean R2 = 0.26).ConclusionsSpatial-trait relationships mainly occur at the species level, with some commonalities occurring at the trait level. By linking foliar ESP traits to spatial predictors, we can map plant chemical composition patterns that influence landscape-scale ecosystem processes.

Modelling the impact of health-related variables, age, migration, and socio-economic factors in the geographical distribution of early tested case-fatality risks associated with COVID-19 in Mexico

ABSTRACTCOVID-19 is a respiratory disease caused by SARS-CoV-2, which has significantly impacted economic and public healthcare systems world-wide. SARS-CoV-2 is highly lethal in older adults (>65 years old) and in cases with underlying medical conditions including chronic respiratory diseases, immunosuppression, and cardio-metabolic diseases including severe obesity, diabetes, and hypertension. The course of the COVID-19 pandemic in Mexico has led to many fatal cases in younger patients attributable to cardio-metabolic conditions. Here, we aimed to perform an early spatial epidemiological analysis for the COVID-19 outbreak in Mexico to evaluate how tested case-fatality risks (t-CFRs) are geographically distributed and to explore spatial predictors of early t-CFRs considering the variation of their impact on COVID-19 fatality across different states in Mexico, controlling for the severity of the disease. As results, considering health related variables; diabetes and obesity were highly associated with COVID-19 fatality. We identified that both external and internal migration had an important impact over early COVID-19 risks in Mexico, with external migration having the second highest impact when analyzing Mexico as a whole. Physicians-to-population ratio, as a representation of urbanity, population density, and overcrowding households, has the highest impact on t-CFRs, whereas the age group of 10 to 39 years was associated with lower risks. Geographically, the states of Quintana Roo, Baja California, Chihuahua, and Tabasco had higher t-CFRs and relative risks comparing with a national standard, suggesting that risks in these states were above of what was nationally expected; additionally, the strength of the association between some spatial predictors and the COVID-19 fatality risks variates by zone depending on the predictor.

Predicting probabilities of streamflow intermittency across a temperate mesoscale catchment

The fields of eco-hydrological modelling and extreme flow prediction and management demand detailed information of streamflow intermittency and its corresponding landscape controls. Innovative sensing technology for monitoring of streamflow intermittency in perennial rivers and intermittent reaches improves data availability, but reliable maps of streamflow intermittency are still rare. We used a large dataset of streamflow intermittency observations and a set of spatial predictors to create logistic regression models to predict the probability of streamflow intermittency for a full year as well as wet and dry periods for the entire 247 km2 Attert catchment in Luxembourg. Similar climatic conditions across the catchment permit a direct comparison of the streamflow intermittency among different geological and pedological regions. We used 15 spatial predictors describing land cover, track (road) density, terrain metrics, soil and geological properties. Predictors were included as local-scale information, represented by the local value at the catchment outlet and as integral catchment information calculated as the mean catchment value over all pixels upslope of the catchment outlet. The terrain metrics catchment area and profile curvature were identified in all models as the most important predictors, and the model for the wet period was based solely on these two predictors. However, the model for the dry period additionally comprises soil hydraulic conductivity and bedrock permeability. The annual model with the most complex predictor set contains the predictors of the dry-period model plus the presence of tracks. Classifying the spatially distributed streamflow intermittency probabilities into ephemeral, intermittent and perennial reaches allows the estimation of stream network extent under various conditions. This approach, based on extensive monitoring and statistical modelling, is a first step to provide detailed spatial information for hydrological modelling as well as management practice.

Zooplankton community beta diversity in an Amazonian floodplain lake

Understanding the mechanisms that generate organism distribution patterns from the beta diversity perspective can assist in environmental monitoring strategies. In this study, we emphasized the limnic zooplankton due to the ability of these organisms to respond quickly to environmental variations. Therefore, we evaluated the following questions: (i) Do different regions of the same lake have the same importance in contributing to beta diversity? (ii) Do beta diversity and its components vary over the hydrological cycle? (iii) What is the importance of local and spatial predictors in beta diversity and its components? (iv) Do beta diversity and its components show a consistent pattern throughout the hydrological cycle? We found that the contribution of different sites to diversity was more associated with regions with low abundance and richness of organisms values, such as the littoral and igarapes, which shows the relevance of these areas for biological monitoring and for the delimitation of priority areas for the zooplankton diversity conservation. Despite the peculiarities of each hydrological period and regarding beta diversity components, we verified a species substitution and differences in abundance pattern in the lake. We also found low concordance patterns between the periods and low environmental and spatial variables prediction on beta diversity patterns.