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FIGURE 4 in The influence of landscape at multiple spatial scales of the river basins at the Eastern Amazon fish assemblage

FIGURE 4 | Venn's diagram showing the exclusive and shared explanation of environmental variables (local, land use, and macroscale) and space in structuring fish assemblages in four catchments of the Eastern Amazon. Local = variation explained by the local matrix (variables of instream habitat); land use = variation explained by the land-use matrix (agriculture, pasture, and urbanization of drainagenetwork buffers and catchment); space = variation explained by the space matrix (environmental filters); macroscale = variation explained by the matrix of macroscale variables (soil characteristics and geographic factor); and residue = variation not explained. * p <0.05.

FIGURE 3 in The influence of landscape at multiple spatial scales of the river basins at the Eastern Amazon fish assemblage

FIGURE 3 | Graphic model showing the environmental and spatial variables that composed the four matrices used in the RDAp.

FIGURE 5 in The influence of landscape at multiple spatial scales of the river basins at the Eastern Amazon fish assemblage

FIGURE 5 | The dbRDA graph displays the distribution of fish data according to the most explanatory (local, macroscale, and land use) variables selected in the distLM model. Environmental variables were chosen by Forward and best model by AIC. Species code: Brachyhypopomus brevirostris (Pbre), Pyrrhulina capim (Pcap), Nannostomus trifasciatus (Ntri), Gymnorhamphichthys rondoni (Gron), Iguanodectes rachovii (Irac), Denticetopsis epa (Dent). Abbreviations: Local variables: EM: Average distance from excavated margins, MA: Average margin angle, SIN: Stretch sinuosity, SF: Smooth flow, LB: Substrate leaf bank, OM: Substrate organic matter, pH, RF: Rapids flow; TD: Average of thalweg depth; SSA: Stream sand. Macroscale variables: CLY: Catchment clay average, CSL: Catchment slope average, SDY: Catchment soil density, CSA: Catchment substrate sand; Land use: UR_S: Urbanization in 30m buffer, PAS_L: Pasture in 60m buffer, AGR_T: agriculture on catchment, PAS_S: Pasture in 30m buffer.

FIGURE 2 in The influence of landscape at multiple spatial scales of the river basins at the Eastern Amazon fish assemblage

FIGURE 2 | Scheme showing different spatial scales from which land use types were quantified in four catchments of the Eastern Amazon.

FIGURE 1 in The influence of landscape at multiple spatial scales of the river basins at the Eastern Amazon fish assemblage

FIGURE 1 | Study area containing 76 streams distributed in four Eastern Amazon basins and land use distribution. Land use: represents all uses (e.g., pasture, agriculture, and urbanization).

Fig. 4 in Life-history of the South American darter, Characidium pterostictum (Crenuchidae): evidence for small scale spatial variation in a piedmont stream

Fig. 4. Seasonal variation in proportion (%) of maturity stages for Characidium pterostictum at Lajeado river (southern Brazil). Values for PA and PB were pooled.

Fig. 3 in Life-history of the South American darter, Characidium pterostictum (Crenuchidae): evidence for small scale spatial variation in a piedmont stream

Fig. 3. Seasonal variation of the gonadosomatic index (GSI) of Characidium pterostictum in Lajeado river, southern Brazil.

Fig. 5 in Life-history of the South American darter, Characidium pterostictum (Crenuchidae): evidence for small scale spatial variation in a piedmont stream

Fig. 5. Boxplots comparing the total length of mature Characidium pterostictum at two sampling sites at Lajeado river (southern Brazil). PA, upstream site; PB, downstream site. Circles are outliers.

Fig. 2 in Life-history of the South American darter, Characidium pterostictum (Crenuchidae): evidence for small scale spatial variation in a piedmont stream

Fig. 2. Boxplots comparing the total length (Lt) of Characidium pterostictum in Lajeado river (southern Brazil). PA, upstream site; PB downstream site. Numbers in parenthesis are sample size; filled circles are outliers, asterisks are extreme values, dotted line is the mean Lt.

Fig. 1 in Life-history of the South American darter, Characidium pterostictum (Crenuchidae): evidence for small scale spatial variation in a piedmont stream

Fig. 1. Size frequency distribution (total length, Lt) for Characidium pterostictum in two sampling sites at Lajeado river, southern Brazil (PA, n = 62; PB, n = 188).

opencc-zeroSep 2021View details →

Data from: Fractal triads efficiently sample ecological diversity and processes across spatial scales

The relative influence of ecological assembly processes, such as environmental filtering, competition, and dispersal, vary across spatial scales. Changes in phylogenetic and taxonomic diversity across environments provide insight into these processes, however, it is challenging to assess the effect of spatial scale on these metrics. Here, we outline a nested sampling design that fractally spaces sampling locations to concentrate statistical power across spatial scales in a study area. We test this design in northeast Utah, at a study site with distinct vegetation types (including sagebrush steppe and mixed conifer forest), that vary across environmental gradients. We demonstrate the power of this design to detect changes in community phylogenetic diversity across environmental gradients and assess the spatial scale at which the sampling design captures the most variation in empirical data. We find clear evidence of broad-scale changes in multiple features of phylogenetic and taxonomic diversity across aspect. At finer scales, we find additional variation in phylodiversity, highlighting the power of our fractal sampling design to efficiently detect patterns across multiple spatial scales. Thus, our fractal sampling design and analysis effectively identify important environmental gradients and spatial scales that drive community phylogenetic structure. We discuss the insights this gives us into the ecological assembly processes that differentiate plant communities found in northeast Utah.

opencc-zeroMar 2023View details →

Data from: The spatial patterns of community composition, their environmental drivers and their spatial scale dependence vary markedly between fungal ecological guilds

Aim How community composition varies in space and what governs the variation has been extensively investigated in macroorganisms. However, we have only limited knowledge for microorganisms, especially fungi, despite their ecological and economic significance. Based on previous research, we define and test a series of hypotheses regarding the composition of fungal communities, its most influential drivers and their spatial scale dependence. Location Czech Republic. Time period Present. Taxa studied Fungi. Methods We analyzed the distance decay relationships, community composition and its drivers (physical distance, litter and soil chemistry, tree composition, climate) in fungi, using multivariate analyses. We compared the results across three fungal ecological guilds (ectomycorrhizal fungi, saprotrophs and yeasts), two forest microhabitats (litter and bulk soil) and six spatial scales (from 5 m to 80 km) that comprehensively cover the Czech Republic. Results We found that, similar to macroorganisms, the ectomycorrhizal fungi and saprotrophs showed marked distance-decay relationships, and their community composition was driven mainly by vegetation and dispersal at local scales, but at regional scales, by environmental effects. In contrast, the third fungal guild, the unicellular yeasts, showed little distance decay, suggesting extraordinary spatial homogeneity, as often seen in microorganisms, such as bacteria. Main conclusions Our results underscore the remarkable variation in the community ecology of fungi, which seems to range well-known patterns both from the macro- and the microworld. Knowledge of these patterns advances our understanding of the ecology of fungi, rather understudied organisms of significant ecological and economic importance, which our findings identify as a potentially suitable model for bridging the gaps between the biogeography of micro- and macroorganisms.

opencc-zeroApr 2023View details →

Data from: Using environmental DNA metabarcoding to monitor fish communities in small rivers and large brooks: Insights on the spatial scale of information

Monitoring fish communities is central to the evaluation of ecological health of rivers. Not only presence/absence of species is important to assess, but also the species composition of local fish assemblages is a crucial parameter. Lotic fish communities are traditionally monitored via electrofishing, characterized by a known limited efficiency and high survey costs. The use of environmental DNA-based analyses could serve as a non-destructive alternative, but this approach requires further insights in practical sampling schemes incorporating transport and dilution of the eDNA fragments; as well as optimization of molecular detection in terms of predictive power and quality assurance. By introducing fifteen species known to occur in Belgian waters via a controlled cage experiment, we aim to extend the knowledge on streamreach of eDNA in small rivers and large brooks, as laid out in the European Water Framework Directive's water typology. Introducing fish communities in two transects of a species poor river characterized by contrasting river discharge rates, we found strong and significant correlations between the eDNA relative abundances and the relative biomass per species in the cage community. Despite a decreasing correlation over distance, the underlying community composition remained stable over a distance of 300 m up to 1 km downstream of the cages, depending on the river discharge rate. Such decrease in similarity between relative source biomass and the corresponding eDNA-based community profile with increasing distance downstream from the source, can partly be attributed to variation in species-specific eDNA persistence. Our findings offer novel insights on eDNA behaviour and characterization of riverine fish communities. We conclude that water sampled from a relatively small river offers an adequate snapshot of the total fish community composition occurring within an upstream perimeter ranging between 300 and 1000 meters. The potential application for other river systems is discussed in this study.

opencc-zeroMay 2023View details →

Resource quantity and quality differentially control stream invertebrate biodiversity across spatial scales

Resource quantity controls biodiversity across spatial scales, however the importance of resource quality to cross-scale patterns in species richness has seldom been explored. We evaluated the relationship between stream basal resource quantity (periphyton chlorophyll-a) and invertebrate richness and compared this to the relationship of resource quality (periphyton stoichiometry) and richness at local and regional scales across 27 North American streams. At the local scale, invertebrate richness peaked at intermediate levels of chlorophyll-a, but had a shallow negative relationship with periphyton C:P and N:P. However, at the regional scale richness had a strong negative relationship with both chlorophyll-a and periphyton C:P and N:P. The divergent effects of periphyton chl-a and stoichiometry on invertebrate richness suggest that basal resource quantity limits diversity more than resource quality, consistent with patterns of eutrophication. Collectively, we demonstrate that resource quantity and quality play important, yet differing roles in shaping freshwater biodiversity across spatial scale.

opencc-by-4.0Sep 2022View details →

Input data, species level results and code accompanying paper: Drivers of the changing abundance of European birds at two spatial scales

This repository contains the input data, species level results and code associated with the paper: Drivers of the changing abundance of European birds at two spatial scales.

opencc-by-4.0May 2023View details →

Long-term spatial memory, across large spatial scales, in Heliconius butterflies

Data accompanying "Long-term spatial memory, across large spatial scales, in Heliconius butterflies", Current Biology 2023:   exp1.csv. Behavioural data from experiment 1. exp2.csv. Behavioural data from experiment 2. exp3.csv. Behavioural data from experiment 3. Exp1&2.csv. Behavioural data comparing experiment 1 and 2. Exp1byDay.csv. Behavioural data for experiment 1 split by day. Exp2byDay.csv. Behavioural data for experiment 2 split by day. Exp3byDay.csv. Behavioural data for experiment 3 split by day. exp1.R. R code for experiment 1 analysis. exp2.R. R code for experiment 2 analysis. exp3.R. R code for experiment 3 analysis. exp1vsExp2.R. R code for comparing experiment 1 and 2.

opencc-by-4.0Jan 2023View details →

D. viride occupancy in Compiegne forest at two spatial scales with temporal survey at fine scale, used in Percel et al. study

This repository contains two datasets in text format, to be imported in codes related to Percel et al. study of D. viride colonization process within Compiegne forest. The dataset "Data_Dviride.2017_AVCHESS_d14.txt" contains the data regarding the occupancy turnover between two surveys in three stands of the forest. The dataset "Data_Dviride.2018_foret.txt" contains the data regarding the coarse grained spatial distribution of D. viride at the forest scale. This repository also contains a basic script used to compute statistics reported in Table 1 of Percel et al. article.

opencc-zeroJul 2023View details →

Recurring bleaching events disrupt the spatial properties of coral reef benthic communities across scales

Marine heatwaves are causing recurring coral bleaching events on tropical reefs that are driving ecosystem change. Yet little is known about how bleaching and subsequent coral mortality impacts the spatial properties of tropical seascapes, such as patterns of organism spatial clustering and heterogeneity across scales. Changes in these spatial properties can offer insight into ecosystem recovery potential following disturbance. Here we repeatedly quantified coral reef benthic spatial properties around the circumference of an uninhabited tropical island in the central Pacific over a 9-year period that included a minor and severe marine heatwave. Benthic communities showed increased biotic homogenisation following both minor and mass bleaching, becoming more taxonomically similar with less diverse intra-island community composition. Hard coral cover, which was highly spatially clustered around the island prior to bleaching, became less spatially clustered following minor bleaching and was indiscernible from a random distribution across all scales (100–2000 m) following mass bleaching. Interestingly, the reduced degree of hard coral cover spatial clustering was already evident by the onset of mass bleaching and before any dramatic wholesale loss in island-mean coral cover occurred. Reductions in hard coral spatial clustering may therefore offer an early indication of the ecosystem becoming degraded prior to mass coral mortality. In contrast, the spatial clustering of competitive fleshy macroalgae remained unchanged through both bleaching events, while crustose coralline algae and fleshy turf algae became more spatially clustered at larger scales (200–700 m) following mass bleaching. Overall, benthic community spatial patterning became less predictable following bleaching and was no longer reflective of gradients in long-term environmental drivers that typically structure these remote reefs. Our findings provide novel insights into how climate-driven marine heatwaves can impact the spatial properties of coral reef communities over multiple scales.

opencc-by-4.0Sep 2023View details →

Code and data: Understanding temporal variability across trophic levels and spatial scales in freshwater ecosystems

Code and data to reproduce the results in Siqueira et al. (submitted) published as a Preprint (https://doi.org/10.32942/osf.io/mpf5x) The full set of results, including those made available as supplementary material, can be reproduced by running five scripts in the R_codes folder following this sequence: <ul> <li>01_Dataprep_stability_metrics.R</li> <li>02_SEM_analyses.R</li> <li>03_Stab_figs.R</li> <li>04_Stab_supp_m.R</li> <li>05_Sensit_analysis.R</li> </ul> and using the data available in the Input_data folder. The original raw data made available include the abundance (individual counts, biomass, coverage area) of a given taxon, at a given site, in a given year. See details here https://doi.org/10.32942/osf.io/mpf5x However, this is a collaborative effort and not all authors are allowed to share their raw data. One data set (LEPAS), out of 30, was not made available due to data sharing policies of The Ohio Division of Wildlife (ODOW). So, in code "01_Dataprep_stability_metrics.R" all data made available are imported, except the LEPAS data set. For this specific data set, code "01_Dataprep_stability_metrics.R" imports variability and synchrony components estimated using the methods described in Wang et al. (2019 Ecography; doi/10.1111/ecog.04290), diversity metrics (alpha and gamma diversity), and some variables describing the data set. A protocol for requesting access to the LEPAS data sets can be found here: https://ael.osu.edu/researchprojects/lake-erie-plankton-abundance-study-lepas Dataset owner: Ohio Department of Natural Resources – Division of Wildlife, managed by Jim Hood, Dept. of Evolution, Ecology, and Organismal Biology, The Ohio State University. Email: hood.211@osu.edu Anyone who wants to reproduce the results described in the preprint can just download the whole R project (that includes code and data) and run codes from 01 to 05. I am making the whole R project folder (with everything needed to reproduce the results) available as a compressed file.