Defining quantitative stream disturbance gradients and the additive role of habitat variation to explain macroinvertebrate taxa richness

Most studies dealing with the use of ecological indicators and other applied ecological research rely on some definition or concept of what constitutes least-, intermediate- and most-disturbed condition. Currently, most rigorous methodologies designed to define those conditions are suited to large spatial extents (nations, ecoregions) and many sites (hundreds to thousands). The objective of this study was to describe a methodology to quantitatively define a disturbance gradient for 40 sites in each of two small southeastern Brazil river basins. The assessment of anthropogenic disturbance experienced by each site was based solely on measurements strictly related to the intensity and extent of anthropogenic pressures. We calculated two indices: one concerned site-scale pressures and the other catchment-scale pressures. We combined those two indices into a single integrated disturbance index (IDI) because disturbances operating at both scales affect stream biota. The local- and catchment-scale disturbance indices were weakly correlated in the two basins (r = 0.21 and 0.35) and both significantly (p \u3c 0.05) reduced site EPT (insect orders Ephemeroptera, Plecoptera, Trichoptera) richness. The IDI also performed well in explaining EPT richness in the basin that presented the stronger disturbance gradient (R2 = 0.39, p \u3c 0.001). Natural habitat variability was assessed as a second source of variation in EPT richness. Stream size and microhabitats were the key habitat characteristics not related to disturbances that enhanced the explanation of EPT richness over that attributed to the IDI. In both basins the IDI plus habitat metrics together explained around 50% of EPT richness variation. In the basin with the weaker disturbance gradient, natural habitat explained more variation in EPT richness than did the IDI, a result that has implications for biomonitoring studies. We conclude that quantitatively defined disturbance gradients offer a reliable and comprehensive characterization of anthropogenic pressure that integrates data from different spatial scales

Importance of environmental factors for the richness and distribution of benthic macroinvertebrates in tropical headwater streams

An understanding of the interactions among local environmental factors (e.g., physical habitat and water quality) and aquatic assemblages is essential to conserve biodiversity in tropical and subtropical headwater streams. We evaluated the relative importance of multiple physical and chemical habitat variables that influence the richness of Ephemeroptera, Plecoptera, and Trichoptera (EPT) assemblages in wadeable Brazilian Cerrado (savanna) streams. We sampled macroinvertebrate assemblages and quantified physical and chemical habitat in 79 randomly selected sites in 2 Cerrado basins in southeastern Brazil. The environmental variables selected by multiple regression models (MLRs) via corrected Akaike Information Criteria (AICc) contributed significantly to variation in EPT taxon richness. The variance explained by physical-habitat variables was slightly greater in the Upper São Francisco Basin (adjusted R² = 0.53) than in the Upper Araguari Basin (adjusted R² = 0.46), and both were greater than the variance explained by a combined basin model (adjusted R² = 0.39). Physical-habitat variables were more important than water-quality variables in structuring EPT genera in streams with catchments dominated by agriculture or pasture land uses. Regional models can be improved by incorporating basin-specific information to refine biological assessments and to provide better understanding of the interactions that maintain biodiversity in stream networks.Keywords: Hydromorphology, Physical habitat, Cerrado headwater streams, EPT assemblages, Macroinvertebrate bioindicators, Stream conservatio

Anthropogenic disturbances alter the relationships between environmental heterogeneity and biodiversity of stream insects

Highlights • EH plays a more important role in biodiversity when anthropogenic disturbance is high. • Within a stream site, EH does not affect beta diversity of aquatic insects. • Model selection approach pinpointed the most ecologically meaningful EH metrics. • Managing EH requires knowledge of how disturbances drive biological indicators.The effects of anthropogenic disturbance on multiple facets of biodiversity are poorly understood. In this study, we worked with the hypothesis that anthropogenic disturbances affect the relationship between environmental heterogeneity (EH) and biodiversity. We used a model selection approach to test three predictions. P1: The greater the level of anthropogenic disturbance, the weaker will be the relationship between EH and both taxonomic and functional alpha diversities. P2: The sign and strength of correlations between EH metrics and both taxonomic and functional alpha diversities will depend on the level of anthropogenic disturbance. P3: Taxonomic and functional beta diversities will not respond to the EH gradient. We sampled 76 stream sites in the Brazilian Neotropical savanna and collected insect of the orders Ephemeroptera, Plecoptera and Trichoptera to measure taxonomic and functional alpha and beta diversities. For P1, we did not find a trend of decreasing strength of this relationship with increasing disturbance. Results confirmed P2. Spatial flow diversity was positively correlated to taxonomic and functional alpha diversities in least-disturbed sites. Bankfull height variation was negatively correlated to taxonomic and functional alpha diversities in moderately-disturbed sites. Thalweg depth variation was positively correlated to taxonomic and functional alpha diversities in most-disturbed sites. Results partially confirmed P3 because taxonomic and functional beta diversities correlated with EH metrics in most-disturbed sites. We conclude that the biodiversity-EH relationship is not the same at all levels of anthropogenic disturbance, a finding that has implications for biomonitoring and ecosystem management

Is the diet of a typical shredder related to the physical habitat of headwater streams in the Brazilian Cerrado?

Macroinvertebrates are important for processing leaf detritus in temperate streams, but studies about their role in tropical streams often present conflicting results. Via digestive tract analyses, we assessed the diets of Phylloicus sp. larvae (Trichoptera: Calamoceratidae), collected from streams of two southeastern Brazil river basins (Araguari, Sao Francisco). We classified gut contents as coarse particulate organic matter (CPOM), fine particulate organic matter (FPOM), algae, animal tissue, vascular plant tissue and mineral material. We hypothesized that the diets of Phylloicus larvae would be related to the physical habitat of the streams (e.g., riparian vegetation, organic matter availability and morphological characteristics), larval size and river basin. Although FPOM content predominated in both basins, we found greater CPOM content in Phylloicus larvae of Upper Sao Francisco sites, and this food item was related to greater riparian vegetation canopy density. The FPOM content was greater in larvae of Upper Araguari sites, and this food item was correlated with greater instream brush cover. Algae, animal tissue, vascular plant tissue and mineral material were very rare in the digestive tracts, and therefore could not be explained. These results indicate the importance of riparian vegetation structure in modulating feeding habitats of macroinvertebrates. We conclude that the Phylloicus larvae had more flexibility in what they eat than we might expect based on their traditional classification as shredders. Therefore, trusting in published classifications, ignoring regional or local differences, may be inaccurate. Instead, regional studies of feeding habits are needed for accurate classifications of invertebrate taxa into trophic guilds

The relative influence of catchment and site variables on fish and macroinvertebrate richness in cerrado biome streams

Landscape and site-scale data analyses aid the interpretation of biological data and thereby help us develop more cost-effective natural resource management strategies. Our study focused on environmental influences on stream assemblages and we evaluated how three classes of environmental variables (geophysical landscape, land use and cover, and site habitat), influence fish and macroinvertebrate assemblage richness in the Brazilian Cerrado biome. We analyzed our data through use of multiple linear regression (MLR) models using the three classes of predictor variables alone and in combination. The four MLR models explained dissimilar amounts of benthic macroinvertebrate taxa richness (geophysical landscape R² ≈ 35%, land use and cover R² ≈ 28%, site habitat R² ≈ 36%, and combined R² ≈ 51%). For fish assemblages, geophysical landscape, land use and cover, site habitat, and combined models explained R² ≈ 28%, R² ≈ 10%, R² ≈ 31%, and R² ≈ 47% of the variability in fish species richness, respectively. We conclude that (1) environmental variables differed in the degree to which they explain assemblage richness, (2) the amounts of variance in assemblage richness explained by geophysical landscape and site habitat were similar, (3) the variables explained more variability in macroinvertebrate taxa richness than in fish species richness, and (4) all three classes of environmental variables studied were useful for explaining assemblage richness in Cerrado headwater streams. These results help us to understand the drivers of assemblage patterns at regional scales in tropical areas.Keywords: Anthropogenic pressures, Biological diversity, Multiple linear regression models, Brazil, Savanna, Physical habitat structure, Streams, Landscape ecology, Partition of varianceKeywords: Anthropogenic pressures, Biological diversity, Multiple linear regression models, Brazil, Savanna, Physical habitat structure, Streams, Landscape ecology, Partition of varianc

A comparative analysis reveals weak relationships between ecological factors and beta diversity of stream insect metacommunities at two spatial levels.

The hypotheses that beta diversity should increase with decreasing latitude and increase with spatial extent of a region have rarely been tested based on a comparative analysis of multiple datasets, and no such study has focused on stream insects. We first assessed how well variability in beta diversity of stream insect metacommunities is predicted by insect group, latitude, spatial extent, altitudinal range, and dataset properties across multiple drainage basins throughout the world. Second, we assessed the relative roles of environmental and spatial factors in driving variation in assemblage composition within each drainage basin. Our analyses were based on a dataset of 95 stream insect metacommunities from 31 drainage basins distributed around the world. We used dissimilarity-based indices to quantify beta diversity for each metacommunity and, subsequently, regressed beta diversity on insect group, latitude, spatial extent, altitudinal range, and dataset properties (e.g., number of sites and percentage of presences). Within each metacommunity, we used a combination of spatial eigenfunction analyses and partial redundancy analysis to partition variation in assemblage structure into environmental, shared, spatial, and unexplained fractions. We found that dataset properties were more important predictors of beta diversity than ecological and geographical factors across multiple drainage basins. In the within-basin analyses, environmental and spatial variables were generally poor predictors of variation in assemblage composition. Our results revealed deviation from general biodiversity patterns because beta diversity did not show the expected decreasing trend with latitude. Our results also call for reconsideration of just how predictable stream assemblages are along ecological gradients, with implications for environmental assessment and conservation decisions. Our findings may also be applicable to other dynamic systems where predictability is low

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost