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opencc-by-4.0Dec 2016View details →

Figures 4-7 in A new species of Falsocis (Coleoptera: Ciidae) from the Atlantic Forest biome with new geographic records and an updated identification key for the species of the genus

Figures 4-7. Dissected male terminalia of a paratype of Falsocis sooretama sp. nov.: (4) sternite VIII; (5) basal piece; (6) tegmen; (7) penis. Scale bar: 0.1 mm.

opencc-by-4.0Dec 2016View details →

Figures 1-3 in A new species of Falsocis (Coleoptera: Ciidae) from the Atlantic Forest biome with new geographic records and an updated identification key for the species of the genus

Figures 1-3. Adult male holotype of Falsocis sooretama sp. nov.: (1) dorsal view; (2) lateral view; (3) ventral view. Scale bar: 0.5 mm.

opencc-by-4.0Jul 2023View details →

Figure 1 in New records of two-winged flies (Diptera: Brachycera) in social wasp colonies (Hymenoptera: Vespidae) from the Atlantic Forest biome in the state of Minas Gerais, Brazil

Figure 1. Specimens of two-winged flies (Brachycera) recorded in social wasp colonies (Polistinae). A-B. Megaselia scalaris. C-D. Sargus fasciatus. E-F. Acrosticta apicalis. G-H. Pseudogaurax aff. longilineatus. / Ejemplares de moscas de dos alas (Brachycera) registrados en colonias de avispas sociales (Polistinae). A-B. Megaselia scalaris. C-D. Sargus fasciatus. E-F. Acrosticta apicalis. G-H. Pseudogaurax aff. longilineatus.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Exploring Sisyrinchium (Iridaceae) diversity in the Atlantic Forest Biome: three new species in S. sect. Viperella.

Natural history specimen data linked to collectors and determiners held within, "Exploring Sisyrinchium (Iridaceae) diversity in the Atlantic Forest Biome: three new species in S. sect. Viperella". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/26bd4c36-21ee-4133-bbfb-201708d4c13d">https://bionomia.net/dataset/26bd4c36-21ee-4133-bbfb-201708d4c13d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/26bd4c36-21ee-4133-bbfb-201708d4c13d">https://gbif.org/dataset/26bd4c36-21ee-4133-bbfb-201708d4c13d</a>. Formatted as a Frictionless Data package.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: The spider genus Patrera Simon (Araneae: Dionycha, Anyphaeninae) in the Atlantic Forest biome, with the description of one new species from Brazil.

Natural history specimen data linked to collectors and determiners held within, "The spider genus Patrera Simon (Araneae: Dionycha, Anyphaeninae) in the Atlantic Forest biome, with the description of one new species from Brazil". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/f9dde51c-a2b9-4f3e-abe5-a7a3d7b0002d">https://bionomia.net/dataset/f9dde51c-a2b9-4f3e-abe5-a7a3d7b0002d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/f9dde51c-a2b9-4f3e-abe5-a7a3d7b0002d">https://gbif.org/dataset/f9dde51c-a2b9-4f3e-abe5-a7a3d7b0002d</a>. Formatted as a Frictionless Data package.

opencc-zeroFeb 2023View details →

Meta-analysis shows forest soil CO2 effluxes are dependent on the disturbance regime and biome type

Forest soil CO2 efflux (FCO2) is a crucial process in global carbon cycling; however, how FCO2 responds to disturbance regimes in different forest biomes is poorly understood. We quantified the effects of disturbance regimes on FCO2 across boreal, temperate, tropical, and Mediterranean forests based on 1240 observations from 380 studies. Globally, climatic perturbations such as elevated CO2 concentration, warming, and increased precipitation increase FCO2 by 13 to 25%. FCO2 is increased by forest conversion to grassland and elevated carbon input by forest management practices but reduced by decreased carbon input, fire, and acid rain. Disturbance also changes soil temperature and water content, which in turn affect the direction and magnitude of disturbance influences on FCO2. FCO2 is disturbance- and biome-type dependent, and such effects should be incorporated into earth system models to improve the projection of the feedback between the terrestrial C cycle and climate change.

opencc-by-4.0Mar 2023View details →

Supplementary Data for the publication "High economic costs of reduced carbon sinks and declining biome stability in Central American forests"

Supplementary data from the DGVM simulations underlying the main figures presented in the publication. Naming convention: {variable}_{aggregation period}-{comparison period [only relevant for bsprob]}_{climate model}-{climate scenario}.tif Variables are: <ul> <li>bsprob-Snell2013ed = biome shift probability (biomization adjusted from Snell et al. 2013) [%]</li> <li>nee = net ecosystem exchange [kgC/m2/year]</li> </ul> Global climate models include GFDL = GFDL-ESM4 and IPSL= IPSL-CM6A-LR. Climate scenarios refer to the shared socioeconomic pathways (SSP) SSP126= SSP1-2.6 and SSP370= SSP3-7.0.

opencc-zeroApr 2023View details →

Herbaceous vegetation responses to experimental fire in savannas and forests depend on biome and climate

Fire-vegetation feedbacks potentially maintain global savanna and forest distributions. Accordingly, vegetation in savanna and forest ecosystems should have differential responses to fire, but fire response data for herbaceous vegetation has yet to be synthesized across biomes. Here, we examined herbaceous vegetation responses to experimental fire at 30 sites spanning four continents. Across a variety of metrics, herbaceous vegetation increased in abundance where fire was applied, with larger responses to fire in wetter and in cooler and/or less seasonal systems. Compared to forests, savannas were associated with a 4.8 (±0.4) times larger difference in burned versus unburned herbaceous vegetation abundance. In particular, grass cover decreased with fire exclusion in savannas, largely via decreases in C4 grass cover, whereas changes in fire frequency had a relatively weak effect on grass cover in forests. These differential responses underscore the importance of fire for maintaining the vegetation structure of savannas and forests.

publicFeb 2023View details →

Meta-analysis shows forest soil CO2 effluxes are dependent on the disturbance regime and biome type

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publicSep 2019View details →

A new approach to map landscape variation in forest restoration success in tropical and temperate forest biomes

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publicApr 2023View details →

Herbaceous vegetation responses to experimental fire in savannas and forests depend on biome and climate

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opencc-zeroSep 2015View details →

Supporting data: The biogeographic origin of a radiation of trees in Madagascar: Implications for the assembly of a tropical forest biome

This directory contains xml files (which in turn contain concatenated alignments), newick, and phylip formatted tree files. Code and scripts used for generating figures, phylogenies and biogeographic models is available on request. ~/trees/ contains tree files and maximum clade credibility trees from the three phylogenetic inferences: 1) Canarieae with a fossil tip; 2) Canarieae with fossil nodes; and 3) Canarieae with fossil nodes, but without the Canarieae fossil node calibration. See the paper and supplemental text for more information. ~/trees/RAxML/ contains the RAXML starter trees used   ~/xml_files/ contains the xml files used for Bayesian phylogenetic inference in BEAST for all three phylogenetic inferences (see above). Concatenated alignments of molecular data can be found within the xml files.

opencc-by-4.0Jun 2024View details →

Data & codes for "Changes in abundance and distribution of European forest bird populations depend on biome, ecological specialisation and traits"

<h1>1.    Selection of European forest bird species and classification of their biome preferences</h1> We selected all species that are related to forest and woodland based on two data sources: Storchová & Hořák (2018) and Tobias et al. (2022), resulting in 107 bird species studied (Data S1). We defined forest bird species as those using environments ranging from closed-canopy forests to more open-canopy woodlands (A. Lehikoinen & Virkkala, 2018; Storchová & Hořák, 2018; Tobias et al., 2022). We determined their biome specialisation using breeding distribution centroids and the overall breeding distribution of each of the species, using the global map of terrestrial ecoregions from Olson et al. (2001) and range data from European Breeding Bird Atlas 1 and 2 (Hagemeijer & Blair, 1997; Keller et al., 2020). We categorised species as Mediterranean, temperate, or boreal based on their predominant biogeographic region. We considered species commonly occurring over several biomes as “generalists”. For instance, we reclassified the two typically boreal species Glaucidium passerinum Linnaeus and Strix uralensis Pallas as “generalists” due to significant range expansions into central and southern Europe in recent decades, therefore no longer restricted to the boreal region. For the complete list of species, biome specialisation, traits, and specialisation indices, refer to Data S1. <h1>2.    Changes in abundance and distribution of European forest bird species</h1> We assessed long-term changes in European forest bird populations through two approaches: (i) changes in estimated total European-level species abundance over a 40-year timeframe; and (ii) changes in species spatial distribution over a 30-year timeframe (Fig. 1). We utilized the estimated trends in European-level population size (i.e., the total number of individuals) for each common native European bird species from 1980 to 2017, as reported by Burns et al. (2021). Three species out of the 107 studied forest species were missing in the original manuscript and we used data generated with the same method from 1980 to 2018 from the European assessment, Article 12 (https://nature-art12.eionet.europa.eu/article12/). These abundance trends were calculated by Burns et al. (2021) using multi-sourced annual times series. For each species, they gathered population estimates and trends from each European country as well as European Union (EU)-level population trends. They analysed these data with a Bayesian hierarchical model to reconstruct EU-level smoothed species population time series. The model outputs include an average annual rate of abundance change and an associated 95% credible interval (Burns et al., 2021). Therefore, we did not directly use the average annual rate of abundance change, as this would have led us to consider species with low uncertainty as similar to those with high uncertainty. To account for the uncertainty, we categorised species as (i) declining, i.e., annual rates below one, (ii) increasing, i.e., annual rates above one and (iii) stable, i.e., annual rate whose 95% CI overlap one, i.e., no significant change. To better acknowledge the magnitude of the abundance change, significant changes with rates below 0.98 were labelled as “strongly declining” (i.e., 6.5% of the 107 species), while those above 1.02 were labelled as “strongly increasing” (i.e., 11% of the 107 species). To evaluate the sensitivity of the decision to categorised abundance change data, we also analysed abundance trend as continuous variable (see Supporting Information Fig. S8). To determine changes in species distributions, we used a comparison of species distributions between two periods (i.e., 1985-1988 and 2013-2017) using the European Breeding Bird Atlas 1 and 2 (EBBA 1 & 2; Hagemeijer & Blair, 1997; Howard et al., 2023; Keller et al., 2020). Howard et al. (2023) provided calculations of observed colonisation and extinction areas at a 50 x 50 km resolution across Europe. We measured changes in range as the difference between colonisations and extinctions of each species, with negative values indicating contracting ranges and positive values indicating expanding ranges. Additionally, we calculated the shift in the centre of gravity of the distribution range between the two periods, as a distance (km) along the south-north gradient for each species (Howard et al., 2023). <h1>3.    Trait and specialisation data for European forest bird species</h1> We extracted data for six functional traits from several sources (Table 1). (i) The species temperature index (STI)represents the long-term average temperature within the species’ breeding range (A. Lehikoinen et al., 2021). (ii) Diet data during the breeding season were obtained from Storchová & Hořák (2018), classifying species into binary variables as vertebrate carnivorous, invertebrate carnivorous, and herbivores (combining the leaf and seed eaters). Storchová & Hořák (2018) classified species into a diet category when the corresponding food resource represented at least 10% of the species diet throughout the breeding season. Therefore, one species can be in several categories (i.e., omnivores). (iii) We obtained nesting site data from Pearman et al. (2014), classifying species into binary variables as ground nesters, tree hole nesters, or elevated nesters (> 1 m in a tree or shrub). We also included data on (iv) species dependence on old-growth forests (Data S1; mostly from Fraixedas et al. (2015) and Mönkkönen et al. (2014), if present on both references, we classified them as “1” and if only in one reference as “0.5”), (v) migration distance (Howard et al., 2023), and (vi) body mass (Tobias et al., 2022). Finally, we extracted and developed seven species specialisation indices. (i) We used an overall specialisation index based on multiple traits (i.e., temperature, diet, foraging behaviour and substrate, habitat, and nesting site), and (ii) a nesting specialisation index, both obtained from Morelli et al. (2019). Both indices represent species specialization based on the dispersion of trait preferences for each species: e.g., nesting specialism equal 0 for species that nest in all habitat type and equal 1 for species that nest in only one habitat type). They are both calculated using the Gini index of inequality, which measures overall dispersion across, e.g., all traits for the overall specialization, based on data from Pearman et al. (2014) and Storchová & Hořák (2018). For additional information, see Morelli et al. (2019). We also used (iii) the diet specialisation index, (iv) the species distribution range during the breeding season (hereafter “breeding range area”) and (v) the climatic niche breadth from Reif et al. (2016). The diet specialisation index was calculated as the coefficient of variation for diet preferences for each species, where high values denotes specialized species (Reif et al., 2016). The breeding range area was evaluated as the number of 50-km squares in the distribution maps in Europe occupied by each species during the reproduction period, and is based on EBBA 1 (Hagemeijer & Blair, 1997). The climatic niche breadth was calculated as the difference between the 5% hottest and the 5% coldest mean temperature between April and June in which each species occurs, using EBBA 1 (Hagemeijer & Blair, 1997; Reif et al., 2016). Additionally, (vi) we calculated a broadleaf forest specialisation index based on binary forest habitat preferences (Storchová & Hořák, 2018), assigning values of one for species found only in broadleaf forests; zero for those in coniferous forests, and 0.5 for those found in both. Lastly, (vii) we created a forest specialisation index based on the species habitat preferences (Storchová & Hořák, 2018). The forest specialisation index was calculated as the mean of species affinity across habitats. We used increasing habitat weights along a gradient of tree dominance: open habitats as 1, shrubland as 1.5, woodland as 2 (i.e., species associated with habitats structured by trees in lower density than in forest), forest generalist (found in both coniferous and broadleaf dense forests) as 3, and forest specialist (found only either in coniferous or broadleaf dense forests) as 4. For instance, the index value for species occurring either in shrubland, woodland or both broadleaf and coniferous forests is 2.167. <h1>4.    Data analysis</h1> Data analyses were conducted with R software version 4.4.1. (R Core Team, 2024). Given the non-independence of species due to their genetic relatedness, we accounted for interspecific phylogenetic distance in all models. We constructed the phylogenetic tree for the 107 European forest bird species using ‘rotl’ and ‘ape’ R-packages (Michonneau et al., 2022; Paradis et al., 2023). We used rotl as an interface with the "Open Tree of Life", employing tol_induced_subtree R-function to generate the phylogenetic tree and compute.brlen R-function to set branch lengths using Grafen’s computation. We generated separate phylogenetic trees for boreal (17), temperate (15), Mediterranean (16) and “generalist” (59) species to perform biome-specific analysis (see Supplementary Information, Figs. S1 & S2). To investigate the effects of functional traits and specialisation indices on abundance, range changes, and distribution shift, we used two regression methods. All methods were based on the relationships between a measure of change and a functional trait or specialisation index. Our sample unit is an individual forest bird species (i.e., one value for each species, either abundance or range change, or distribution shift). Abundance change was a categorical variable (i.e., strong decline – decline – stable – increase – strong increase), while range change (i.e., difference between colonisation and extinction) and distribution shift (i.e., south-north shift) were continuous variables. Therefore, to study abundance changes, we used proportional-odds linear mixed effects model using (Phylo)clmm R-function from the ‘ordinal’ R-package (Christensen, 2022). Interspecific phylogenetic relatedness was included as a random effect, reflecting the correlation between species based on phylogenetic distances (see also Hagge et al. (2021) and Seibold et al. (2015)). For distribution changes, we employed phylogenetic generalised least squares regression (PGLS) using the gls R-function from the ‘nlme’ R-package (Pinheiro et al., 2023). The phylogenetic correlation structure was integrated into PGLS using Pagel’s lambda parameter (λ; Pagel (1999)) a widely used measured of phylogenetic signal strength (see, e.g., Hagge et al., 2021; Triviño et al., 2013). Furthermore, we included latitude, a key driver of bird communities at broad scales (Luoto et al., 2007), as a fixed covariable (centroid latitude of the species’ breeding distribution) in all global models (i.e., species from all biomes together), except for the STI model due to strong correlation. For biome-specific analysis, we included latitude only in boreal species models for range change and distribution shift, as it significantly improved model fit (ΔAIC < -2). We did not add latitude for models specific to temperate, Mediterranean, and generalist species since it did not improve model fits (ΔAIC > -2). Additionally, we included breeding range area in range change and distribution shift models, assuming that species with larger ranges would exhibit larger shifts. We scaled predictors to a mean of 0 and standard deviation of 1 to facilitate effect size comparisons. We adjusted p-values using the Holm method (for n=3) to account for multiple testing of traits and specialisation indices on three response variables.

opencc-by-nc-4.0Aug 2022View details →

Figure S4 in Small mammals and microhabitat selection in forest fragments in the transition zone between Atlantic Forest and Pampa biome

Figure S4. Rarefaction curve for both studied fragments in the Atlantic Forest biome, Brazil. Sample coverage is the proportion of the total number of individuals that belong to the species detected in the sample. F1 = Fragment 1 (28°08′38″S, 54°45′36″W); F2 = Fragment 2 (28°07′33″S, 54°44′57″W).

opencc-by-nc-4.0Aug 2022View details →

Figure 3 in Small mammals and microhabitat selection in forest fragments in the transition zone between Atlantic Forest and Pampa biome

Figure 3. Variables coefficients and their confidence intervals in the models selected (with ΔAIC ≤ 2) for each small mammal species. (A) Akodon montensis; (B) Oligoryzomys nigripes; (C) Sooretamys angouya; (D) Didelphis albiventris. PC1GC = first axis of the PCA for soil variables; PC2GC = second axis of the PCA for soil variables; PC1VS = first axis of the PCA for vegetation structure; PC2VS = second axis of the PCA for vegetation structure.

Fig. 2 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil

Fig. 2. Spatial distribution of the species found in the Cruzeiro do Sul rural district.

Fig. 1 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil

Fig. 1. Localization of the study areas in the Atlantic Forest biome, São Paulo state – Brazil.

Fig. 6 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil

Fig. 6. Adjusted Semi-variograms of the Simpson diversity index.

Fig. 3 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil

Fig. 3. Spatial distribution of the species found in the Vale das Cigarras rural districts.