Biotic Predictors Research Articles

Modelling the nesting-habitat of threatened vulture species in the caucasus: An ecosystem approach to formalising environmental factors in species distribution models

Abiotic factors play an important role in species localisation, but biotic and anthropogenic predictors must also be considered in distribution modelling for models to be biologically meaningful. In this study, we formalised the biotic predictors of nesting sites for four threatened Caucasian vultures by including species distribution models (wild ungulates, nesting tree species) as biotic layers in the vulture Maxent models. Maxent was applied in the R dismo package and the best set of the model parameters were defined in the R ENMeval package. Performance metrics were continuous Boyce index, Akaike's information criterion, the area under receiver operating curve and true skill statistics. We also calculated and evaluated the null models. Kernel density estimation method was applied to assess the overlap of vulture ecological niches in the environmental space. The accessibility of anthropogenic food resources was estimated using the Path Distance measure that considers elevation gradient. The availability of pine forests (Scots Pine) and wild ungulates (Alpine Chamois and Caucasian Goat) contributed the most (29.6% and 34.3%) to Cinereous Vulture (Aegypius monachus) nesting site model. Wild ungulate distribution also contributed significantly (about 46%) to the Bearded Vulture (Gypaetus barbatus) model. This scavenger nests in the highlands of the Caucasus at a minimum distance of 5–10 ​km from anthropogenic facilities. In contrast, livestock as a food source was most important in colony distribution of Griffon Vulture (Gyps fulvus). The contribution of distances to settlements and agricultural facilities to the model was 45%. The optimal distance from Egyptian Vulture (Neophron percnopterus) nesting sites to settlements was only 3–10 ​km, to livestock facilities no more than 15 ​km with the factor contribution of about 57%. Excluding the wild ungulate availability, the ecological niches of studied vultures overlapped significantly. Despite similar foraging and nesting requirements, Caucasian vultures are not pronounced nesting and trophic competitors due to the abundance of nesting sites, anthropogenic food sources and successful niche sharing.

The Species Structure of Plankton Communities as a Response to Changes in the Trophic Gradient of the Mouth Areas of Large Tributaries to a Lowland Reservoir

The mouth areas of large rivers can serve as a good model of heterogeneity sites with a pronounced trophic gradient to assess the impact of the degree of eutrophication on different plankton communities. The aim of this research was to identify the possible response of the diversity indicators of phyto- and zooplankton communities to trophic gradients in the mouth area of two large tributaries of the reservoir, formed in the Middle Volga River (Russia). Both linear regression models and canonical correlation analysis (CCA) were used to assess the role of abiotic and biotic predictors in the structural organization of plankton communities and to assess the changes in the parameters of the species plankton community structure in the trophic gradient. It was found that the species diversity (Adjusted R2 = 0.116) and evenness (Adjusted R2 = 0.114) of phytoplankton significantly decreased with an increase in the degree of eutrophication, while the species diversity (Adjusted R2 = 0.059) and evenness (Adjusted R2 = 0.073) of zooplankton increased. According to the CCA models, electrical conductivity (EC) explained the largest proportion of the observed dispersion. The Trophic State Index (TSI) explained 3.0% of the total variance in the phytoplankton community species structure and 7.8% in the zooplankton one. The variation in phyto- and zooplankton dominant complexes generally corresponded to the well-known patterns of plankton species succession in the gradient of trophic conditions and can be considered as a classic manifestation of the cascade effect in the food chains of freshwater plankton communities. Our results highlight the necessity of studying the mouth river areas, as well as applying an integrated approach to investigating the response of plankton communities to eutrophication processes of continental water bodies.

Rainfall partitioning by vegetation in China: A quantitative synthesis

Rainfall partitioning into stemflow, throughfall, and interception loss by vegetation alters hydrological and biogeochemical fluxes between vegetation and soil. Here, we compiled a comprehensive dataset of rainfall partitioning by forests, shrublands, croplands, and grasslands in China from 287 peer reviewed papers (71 in English and 216 in Chinese). Based on this dataset, we summarized the best-fit functions reported for rainfall partitioning (in both mm and %) as a function of rainfall amount, as well as the rainfall thresholds for throughfall and stemflow initiation. We explored the pattern of the proportions of stemflow, throughfall, and interception loss of vegetation in China, and performed boosted regression trees (BRT) analysis to model the relative effects of cross-site biotic and abiotic predictors on each of the rainfall partitioning fluxes (%). Our results identified the scarcity of rainfall partitioning data, particularly for grasslands. A substantial variability of each rainfall partitioning flux (mm) could be explained solely by rainfall amount, with median R2 values of 0.91, 0.99, and 0.82 for stemflow, throughfall, and interception loss, respectively, and with linear functions most often reported as the best-fit functions. Significant differences (p < 0.0001) were detected in rainfall thresholds for initiating stemflow (median: 3.3 mm; interquartile range, IQR: 1.8–5.4 mm) and throughfall (median: 1.2 mm; IQR: 0.8–2.2 mm). Stemflow (%) had a median (IQR) of 2.7 % (1.2–6.0 %), and that value was 74.3 % (66.7–80.3 %) for throughfall (%) and 21.6 % (16.3–28.5 %) for interception loss (%), respectively. Significant differences were detected in the proportion of stemflow (p < 0.001) and throughfall (p < 0.01) between forests and shrublands, respectively; whereas no significant differences in the proportion of interception loss were found among vegetation types. BRT analysis indicated that of the eleven biotic and abiotic predictors examined, six were classified as significant predictors in determining stemflow (%) and interception loss (%), respectively, whereas throughfall (%) had four significant predictors. Non-linear partial effects of predictors on rainfall partitioning fluxes were prevalent. This study avails a global readership to the findings of a large cache of Chinese studies that have been inaccessible hitherto, providing a mechanistic understanding of the effects of cross-site biotic and abiotic predictors on rainfall partitioning fluxes.

Disentangling the contributions of biotic and abiotic predictors in the niche and the species distribution model of Trypanosoma cruzi, etiological agent of Chagas disease

The potential benefits of incorporating biotic, as well as abiotic, predictors in niche and species distribution models (SDMs), as well as how to achieve this, is still debated, with their interpretability and explanatory potential being particularly questioned. It is therefore important to stress test modelling methodologies that include biotic factors against use cases where there is ample knowledge of the potential biotic component of the niche. Relatively well studied and important vector-borne diseases offer just such an opportunity, where knowledge of the agents involved in the transmission cycle –vectors and hosts– can serve to calibrate and test the niche model and corresponding SDM. Here, we study the contributions of biotic –14 vectors, 459 potential hosts– and abiotic –258 climatic categories– predictors to the explanatory and predictive features of the niche and corresponding SDM for the etiological agent of Chagas disease, Trypanosoma cruzi, in Mexico. Using an established spatial data mining technique, we generate biotic, abiotic and biotic+abiotic niche and SDM models. We test our models by comparing predictions of the most important probable hosts of Chagas disease with a previously published list of confirmed hosts. We quantify, compare, and contrast the individual and total contributions of predictors to the niche and distribution of Chagas disease in Mexico. We assess the relative predictive potential of these variables to model performance, showing that models that include relevant biotic niche variables lead to more predictive, more ecologically realistic SDMs. Our research illustrates a useful general procedure for identifying and ranking potential biotic interactions and for assessing the relative importance of biotic and abiotic predictors. We conclude that the inclusion of both abiotic and biotic predictors in SDMs not only provides more predictive and accurate models but also models that are more understandable and explainable from an ecological niche perspective.

Beyond topo-climatic predictors: Does habitats distribution and remote sensing information improve predictions of species distribution models?

Species Distribution Models (SDM) represent a powerful tool to predict species’ habitat suitability on a landscape and fill the gap between truncated observation data and all possible locations. SDMs have been widely used in theoretical studies of species niches as well as in conservation applications. Here, we evaluated the impacts of predictors’ type on models’ performances and spatial predictions using 72 plant species belonging to six ecological groups at a regional scale in the area of Geneva (Switzerland). Twelve models were created using various combinations of high-resolution (25 m) explanatory variables including topography, pedology, climate, habitats and remote sensing data. Models integrating a combination of habitats and topopedo-climatic predictors had significantly higher performances, while remote sensing predictors showed low performances. Our results suggest that the number and the level of details of habitat predictors (broad or very precise) do not fundamentally affect prediction maps. However, selecting too few, overly simplified or exceedingly complex habitat predictors tend to lower models’ performances. The use of eight habitat categories complemented with eight topopedo-climatic predictors produced models with the highest performances. Ecological groups of species responded differently to models and while alpine and ruderal species have greater average performances due to a high affinity with topopedo-climatic predictors, wetlands’ species were less performant on average. These results underline the necessity of developing or having access to habitats distribution data especially in a conservation context. • Models with biotic and topo-climatic predictors show the highest performances. • Habitat predictors characteristics have little impact on prediction maps. • When used raw, NDVI and NDWI do not replace biotic information. • Ecological groups of species responded differently to models. • The creation of LULC maps ensure better conservation practices.

Riparian cover buffers the effects of abiotic and biotic predictors of leaf decomposition in subtropical streams

Riparian cover buffers the effects of abiotic and biotic predictors of leaf decomposition in subtropical streams

Differential effects of soil trophic networks on microbial decomposition activity in mountain ecosystems

Soil trophic networks are key to biogeochemical cycles, in particular decomposition. However, few studies have yet quantified how microbial decomposition activity along environmental gradients is jointly driven by bacteria, fungi, and their respective consumers. Here, we quantified these direct and indirect effects on decomposition and contrasted them between forests and open habitats using multiple elevational gradients in the French Alps.While environmental control on microbial decomposition activity was comparable in the two habitats, the pathways and strengths of biotic predictors strongly differed. The fungal channel composition played a moderate role in forests, while the bacterial channel composition was critical in open habitats. Importantly, we found trophic regulation by consumers to be a key modulator of the direct environmental effects on decomposition in open habitats. These results highlight the need to integrate trophic regulation when predicting future ecosystem functioning.

Observation-based global soil heterotrophic respiration indicates underestimated turnover and sequestration of soil carbon by terrestrial ecosystem models.

Soil heterotrophic respiration (Rh ) refers to the flux of CO2 released from soil to atmosphere as a result of organic matter decomposition by soil microbes and fauna. As one of the major fluxes in the global carbon cycle, large uncertainties still exist in the estimation of global Rh , which further limits our current understanding of carbon accumulation in soils. Here, we applied a Random Forest algorithm to create a global data set of soil Rh , by linking 761 field observations with both abiotic and biotic predictors. We estimated that global Rh was 48.8 ± 0.9Pg C year-1 for 1982-2018, which was 16% less than the ensemble mean (58.6 ± 9.9Pg C year-1 ) of 16 terrestrial ecosystem models. By integrating our observational Rh with independent soil carbon stock data sets, we obtained a global mean soil carbon turnover time of 38.3 ± 11 year. Using observation-based turnover times as a constraint, we found that terrestrial ecosystem models simulated faster carbon turnovers, leading to a 30% (74 Pg C) underestimation of terrestrial ecosystem carbon accumulation for the past century, which was especially pronounced at high latitudes. This underestimation is equivalent to 45% of the total carbon emissions (164 Pg C) caused by global land-use change at the same time. Our analyses highlight the need to constrain ecosystem models using observation-based and locally adapted Rh values to obtain reliable projections of the carbon sink capacity of terrestrial ecosystems.

Biotic predictors improve species distribution models for invasive plants in Western U.S. Forests at high but not low spatial resolutions

Invasions by non-native plants threaten forest health and sustainability. The ability to predict areas at greatest risk to invasion is essential for informing both monitoring and management of invasive species. Species distribution models (SDMs) are often used to identify environmental correlates of a species’ occurrence and predict geographic areas that may be suitable for its presence and are commonly constructed using solely abiotic predictors. However, mounting evidence implies that not including biotic predictors in SDMs may yield less accurate models at some resolutions typical of landscape-scale models, although this possibility has rarely been evaluated in invasive plants. In this study, we determined whether including descriptors of the biotic environment improved the accuracy of SDMs built at five decreasing spatial resolutions for infestations of five common invasive plants in forests of California, Oregon, and Washington, USA and described environmental correlates of each species’ presence.Predictors of occurrence often echoed those identified in previous studies of the focal species, indicating that our models accurately identified important environmental drivers of occurrence. Including biotic predictors in the SDMs consistently improved model accuracy only at the highest resolution we examined, which may be due to the spatial scale at which biotic interactions primarily act to constrain species’ distributions, the particular biotic predictors we used in our models, or correlations between attributes of the abiotic and biotic environment. This finding suggests that, while the practice of building SDMs using abiotic predictors alone may generally yield models whose accuracy does not differ substantially from those that also include biotic predictors, the effects of biotic interactions on the distribution of invasive plants in forests may be detectable at larger scales than previously thought.

Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China

The accurate estimation of forest biomass is crucial for supporting climate change mitigation efforts such as sustainable forest management. Although traditional regression models have been widely used to link stand biomass with biotic and abiotic predictors, this approach has several disadvantages, including the difficulty in dealing with data autocorrelation, model selection, and convergence. While machine learning can overcome these challenges, the application remains limited, particularly at a large scale with consideration of climate variables. This study used the random forests (RF) algorithm to estimate stand aboveground biomass (AGB) and total biomass (TB) of larch (Larix spp.) plantations in north and northeast China and quantified the contributions of different predictors. The data for modelling biomass were collected from 445 sample plots of the National Forest Inventory (NFI). A total of 22 independent variables (6 stand and 16 climate variables) were used to develop and train climate-sensitive stand biomass models. Optimization of hyper parameters was implemented using grid search and 10-fold cross-validation. The coefficient of determination (R2) and root mean square error (RMSE) of the RF models were 0.9845 and 3.8008 t ha−1 for AGB, and 0.9836 and 5.1963 t ha−1 for TB. The cumulative contributions of stand and climate factors to stand biomass were &gt;98% and &lt;2%, respectively. The most crucial stand and climate variables were stand volume and annual heat-moisture index (AHM), with relative importance values of &gt;60% and ~0.25%, respectively. The partial dependence plots illustrated the complicated relationships between climate factors and stand biomass. This study illustrated the power of RF for estimating stand biomass and understanding the effects of stand and climate factors on forest biomass. The application of RF can be useful for mapping of large-scale carbon stock.

Reactive nitrogen restructures and weakens microbial controls of soil N2O emissions

The global surplus of reactive nitrogen (Nr) in agricultural soils is accelerating nitrous oxide (N2O) emission rates, and may also strongly influence the microbial controls of this greenhouse gas resulting in positive feedbacks that further exacerbate N2O emissions. Yet, the link between legacy effects of Nr on microbial communities and altered regulation of N2O emissions is unclear. By examining soils with legacies of Nr-addition from 14 field experiments with different edaphic backgrounds, we show that increased potential N2O production is associated with specific phylogenetic shifts in communities of frequently occurring soil microbes. Inputs of Nr increased the complexity of microbial co-association networks, and altered the relative importance of biotic and abiotic predictors of potential N2O emissions. Our results provide a link between the microbial legacy of Nr addition and increased N2O emissions by demonstrating that biological controls of N2O emissions were more important in unfertilized soils and that these controls are weakened by increasing resource levels in soil.

High resolution species distribution and abundance models cannot predict separate shrub datasets in adjacent Arctic fjords

AbstractAimImproving species distribution models (SDMs) and species abundance models (SAMs) of woody shrubs is critical for predicting biodiversity changes in the Arctic, which is experiencing especially high warming rates. Yet, it remains relatively unexplored if SDMs and SAMs can explain local scale patterns. We aim to identify predictor differences for the distribution versus abundance of two widespread Arctic shrub species with high resolution models and to compare validation approaches to assess the models’ predictive abilities.LocationNuup Kangerlua (NK) and Kangerluarsunnguaq (K), two adjacent fjords in Southwest Greenland.MethodsWe conducted two separate field surveys in either fjord to construct high resolution (~90 m) SDMs and SAMs forBetula nanaandSalix glauca, analysing the predictive influences of local scale climate, topography and soil moisture indicators. We then alternatively trained and validated models in either NK or K fjord and compared these results with the common split‐sample validation approach. Finally, we assessed if including local scale biotic predictors improves SAM performance.ResultsTemperature extremes and precipitation best predicted the distributions of both species, whereas insolation and soil moisture indicators best predicted abundances. Compared to split‐sample validation, both SDM and SAM performance was substantially reduced with separate survey validation. Regardless of validation approach, models performed poor to moderately well, and including local scale biotic parameters improved SAM performance.Main conclusionsSubstantial differences in model performance between validation approaches highlight the usefulness of using a separate survey for validating model predictive performance. We discuss various factors that might have caused poor model performance, such as not capturing all relevant predictors or enough local scale heterogeneity in predictor or response variables. We emphasise the need to include predictors relevant at the spatial scale of study, such as local scale biotic interactions, for improved predictions at high spatial resolution.

Biotic and abiotic predictors of potential N2O emissions from denitrification in Irish grasslands soils: A national-scale field study

Large-scale information regarding nitrous oxide (N2O) emissions is needed as an evidence base to underpin land use policy and mitigation approaches. However, the highly variable rates of denitrification make the prediction of N2O emission demanding. Here, we evaluated the role of abiotic and biotic factors on the potential denitrification of Irish soils, in order to identify the key factors regulating potential N2O emissions at a large scale. To do so, we collected 136 soil samples from 32 sites across Ireland, and characterised the soil physico-chemical properties, the prokaryotic and fungal community composition, the abundance of N-cycling genes and evaluated the soil potential nitrification, denitrification and end product N2O/(N2O + N2). We found large differences in soil potential denitrification between sites (up to 41.5 mg N2O–N kg−1 soil day−1) with most of the emissions released in the form of N2O rather than N2. Soils with highest potential nitrification rates also exhibited the highest potential denitrification rates, and similar parameters were linked to both processes. The factors most predictive of soil potential denitrification were soil physico-chemical properties and the prokaryotic community composition. Soil phosphorus content was as important for predicting potential denitrification as was pH and total nitrogen. Soil microbial community structure, rather than denitrifier abundance, was an important predictor of the potential denitrification and the end-product N2O/(N2O + N2). The prokaryotic community composition was more strongly associated with denitrification rates and the resulting end-products than fungal communities. Increased relative abundance of the prokaryotic phyla Actinobacteriota and Crenarchaeota, were positively correlated to complete denitrification. Altogether, these results lay the foundation for a better understanding of the key factors regulating the potential denitrification in soils and identify important properties that enhance prediction of the potential denitrification at larger scales.

Drivers of diversification in freshwater gastropods vary over deep time.

Unravelling the drivers of species diversification through geological time is of crucial importance for our understanding of long-term evolutionary processes. Numerous studies have proposed different sets of biotic and abiotic controls of speciation and extinction rates, but typically they were inferred for a single, long geological time frame. However, whether the impact of biotic and abiotic controls on diversification changes over time is poorly understood. Here, we use a large fossil dataset, a multivariate birth–death model and a comprehensive set of biotic and abiotic predictors, including a new index to quantify tectonic complexity, to estimate the drivers of diversification for European freshwater gastropods over the past 100 Myr. The effects of these factors on origination and extinction are estimated across the entire time frame as well as within sequential time windows of 20 Myr each. Our results find support for temporal heterogeneity in the factors associated with changes in diversification rates. While the factors impacting speciation and extinction rates vary considerably over time, diversity-dependence and topography are consistently important. Our study highlights that a high level of heterogeneity in diversification rates is best captured by incorporating time-varying effects of biotic and abiotic factors.

Development of a Seafloor Community Classification for the New Zealand Region Using a Gradient Forest Approach

To support ongoing marine spatial planning in New Zealand, a numerical environmental classification using Gradient Forest models was developed using a broad suite of biotic and high-resolution environmental predictor variables. Gradient Forest modeling uses species distribution data to control the selection, weighting and transformation of environmental predictors to maximise their correlation with species compositional turnover. A total of 630,997 records (39,766 unique locations) of 1,716 taxa living on or near the seafloor were used to inform the transformation of 20 gridded environmental variables to represent spatial patterns of compositional turnover in four biotic groups and the overall seafloor community. Compositional turnover of the overall community was classified using a hierarchical procedure to define groups at different levels of classification detail. The 75-group level classification was assessed as representing the highest number of groups that captured the majority of the variation across the New Zealand marine environment. We refer to this classification as the New Zealand “Seafloor Community Classification” (SCC). Associated uncertainty estimates of compositional turnover for each of the biotic groups and overall community were also produced, and an added measure of uncertainty – coverage of the environmental space – was developed to further highlight geographic areas where predictions may be less certain owing to low sampling effort. Environmental differences among the deep-water New Zealand SCC groups were relatively muted, but greater environmental differences were evident among groups at intermediate depths in line with well-defined oceanographic patterns observed in New Zealand’s oceans. Environmental differences became even more pronounced at shallow depths, where variation in more localised environmental conditions such as productivity, seafloor topography, seabed disturbance and tidal currents were important differentiating factors. Environmental similarities in New Zealand SCC groups were mirrored by their biological compositions. The New Zealand SCC is a significant advance on previous numerical classifications and includes a substantially wider range of biological and environmental data than has been attempted previously. The classification is critically appraised and considerations for use in spatial management are discussed.

Chytrid fungus in amphibians from the lowland Brazilian Amazon.

Infectious diseases are one of the main threats to biodiversity. The fungus Batrachochytrium dendrobatidis (Bd) is associated with several amphibian losses around the globe, and environmental conditions may dictate the success of pathogen spread. The Brazilian Amazon has been considered climatically unsuitable for chytrid fungus, but additional information on Bd dynamics in this ecoregion is still lacking. We sampled 462 amphibians (449 anurans, 4 caudatans and 9 caecilians), representing 57 species from the Brazilian Amazon, and quantified Bd infections using qPCR. We tested whether abiotic variables predicted the risk of Bd infections, and tested for relationships between biotic variables and Bd. Finally, we experimentally tested the effects of Bd strains CLFT 156 and CLFT 102 (from the southern and northern Atlantic Forest, respectively) on Atelopus manauensis. We detected higher Bd prevalence than those previously reported for the Brazilian Amazon, and positive individuals in all 3 orders of amphibians sampled. Both biotic and abiotic predictors were related to prevalence, and no variable explained infection load. Moreover, we detected higher Bd prevalence in forested than open areas, while the host's reproductive biology was not a factor. We detected higher mortality in the experimental group infected with CLFT 156, probably because this strain was isolated from a region characterized by discrepant climatic conditions (latitudinally more distant) when compared with the host's sampling site in Amazon. The lowland Brazilian Amazon is still underexplored and future studies targeting all amphibian orders are essential to better understand Bd infection dynamics in this region.

Biotic and Abiotic Predictors of Potential N2o Emissions from Denitrification in Irish Grasslands Soils: A National-Scale Field Study

Biotic and Abiotic Predictors of Potential N2o Emissions from Denitrification in Irish Grasslands Soils: A National-Scale Field Study

Disentangling the Contributions of Biotic and Abiotic Predictors in the Niche and the Species Distribution Model of Trypanosoma Cruzi, Etiological Agent of Chagas Disease

Disentangling the Contributions of Biotic and Abiotic Predictors in the Niche and the Species Distribution Model of Trypanosoma Cruzi, Etiological Agent of Chagas Disease

Vegetation characteristics control local sediment and nutrient retention on but not underneath vegetation in floodplain meadows.

Sediment and nutrient retention are essential ecosystem functions that floodplains provide and that improve river water quality. During floods, the floodplain vegetation retains sediment, which settles on plant surfaces and the soil underneath plants. Both sedimentation processes require that flow velocity is reduced, which may be caused by the topographic features and the vegetation structure of the floodplain. However, the relative importance of these two drivers and their key components have rarely been both quantified. In addition to topographic factors, we expect vegetation height and density, mean leaf size and pubescence, as well as species diversity of the floodplain vegetation to increase the floodplain’s capacity for sedimentation. To test this, we measured sediment and nutrients (carbon, nitrogen and phosphorus) both on the vegetation itself and on sediment traps underneath the vegetation after a flood at 24 sites along the River Mulde (Germany). Additionally, we measured biotic and topographic predictor variables. Sedimentation on the vegetation surface was positively driven by plant biomass and the height variation of the vegetation, and decreased with the hydrological distance (total R2 = 0.56). Sedimentation underneath the vegetation was not driven by any vegetation characteristics but decreased with hydrological distance (total R2 = 0.42). Carbon, nitrogen and phosphorus content in the sediment on the traps increased with the total amount of sediment (total R2 = 0.64, 0.62 and 0.84, respectively), while C, N and P on the vegetation additionally increased with hydrological distance (total R2 = 0.80, 0.79 and 0.92, respectively). This offers the potential to promote sediment and especially nutrient retention via vegetation management, such as adapted mowing. The pronounced signal of the hydrological distance to the river emphasises the importance of a laterally connected floodplain with abandoned meanders and morphological depressions. Our study improves our understanding of the locations where floodplain management has its most significant impact on sediment and nutrient retention to increase water purification processes.

Environmental drivers of breeding sites in blackfly species of medical and veterinary importance in eastern Spain.

Geographical distribution and abundance of the pupae of six blackfly species of medical and veterinary concern were studied in eastern Spain according to three different sets of explanatory variables including in-stream variables, both (i) abiotic (i.e., physicochemical) and (ii) biotic (i.e., richness and abundance of either taxonomically or ecologically close related taxa), as well as (iii) meteorological and landscape variables. The results showed specific habitat requirements for pupation in Simulium (Boophthora) erythrocephalum (De Geer, 1776) and Simulium (Wilhelmia) equinum (Linnaeus, 1758), two of the six species studied regarding elevation and temperature. While the rest of the species showed a certain degree of ecological overlap, co-occurrence was in general low, which suggested that antagonistic biotic factors may be important in structuring blackfly assemblages. In effect, biotic predictors explained a high proportion (50%-70%) of the variability in the abundance of the pupae of the most generalist blackfly species, although further studies are needed to disentangle the sign of interspecific interactions. At the landscape level, S. (W.) equinum and S. (W.) pseudequinum Séguy, 1921 breeding habitats were associated with the presence of pig farms, and S. (Simulium) reptans (Linnaeus, 1758) and S. (B.) erythrocephalum with the presence of cattle.