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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-zeroFeb 2024View details →

Speciation across biomes: rapid diversification with reproductive isolation in the Australian delicate mice

Phylogeographic studies of continental clades, especially when combined with palaeoclimate modelling, provide powerful insight into how environment drives speciation across climatic contexts. Australia, a continent characterized by disparate modern biomes and dynamic climate change, is a model system for reconstructing the impact of past and present environments on diversification. Here we use genomic-scale data (1310 exons and whole mitogenomes from n = 111 samples) to investigate Pleistocene diversification, cryptic diversity, and secondary contact in the Australian delicate mice (Hydromyini: Pseudomys), a recent radiation spanning almost all Australian environments. Across northern Australia, we find no evidence for introgression between cryptic lineages within Pseudomys delicatulus sensu lato, with palaeoclimate models supporting contraction and expansion of suitable habitat since the last glacial maximum. Despite multiple contact zones, we also find little evidence of introgression at a continental scale, with the exception of a potential hybrid zone in the mesic biome. In the arid zone, combined insights from genetic data and palaeomodels support a recent expansion in the arid specialist P. hermannsburgensis, and contraction in the semi-arid P. bolami. In the face of repeated secondary contact, differences in sperm morphology and chromosomal rearrangements are potential mechanisms that maintain species boundaries in these recently diverged species. Additionally, we describe the western delicate mouse as a new species and recommend taxonomic reinstatement of the eastern delicate mouse. Overall, we show that speciation in an evolutionarily young and widespread clade has been driven by environmental change, and potentially maintained by divergence in reproductive morphology and chromosome rearrangements.

opencc-zeroMar 2024View details →

Systematic review of field research reveals critical shortfalls for restoration of tropical grassy biomes

Scientists and policymakers are becoming aware of the pressing need to restore tropical grassy biomes (TGB), which are home to unique biodiversity and provide essential ecosystem services to hundreds of millions of people. However, TGB face increasing threats, including the forest- and tree-centric approaches that promote their degradation though we still lack a systematic assessment of where and how TGB restoration research has been done to guide policy and practice. We synthesised knowledge on field restoration experiments by conducting a systematic literature review to map TGB restoration field studies, examine the association of restoration techniques and degradations sources, and investigate the diversity of indicators used to monitor restoration outcomes. TGB restoration was concentrated in Brazilian and Australian savannas, with large blindspots in Asia, Africa, and northern and western South America. Studies were largely context-dependent, with an inconsistent usage of restoration techniques to different sources of degradation. Less than half of the indicators evaluated were monitored consistently through time, often using a low-dimensional approach related to ecosystem functioning. Few studies manipulated fire, herbivores, and soils, the key drivers for the re-establishment of TGB dynamics. Unfortunately, many studies lacked negative (degraded ecosystems), positive (reference ecosystems) controls, or both, impairing attempts to robustly determine restoration outcomes. Our overview of field research on TGB restoration highlights that research needs improvement to refine our ability to assess, plan, implement, and monitor restoration. Severe issues with experimental designs and data reporting are identified as barriers to finding generality and upscale TGB restoration to meet the goals of the UN Decade on Ecosystem Restoration. Synthesis and implications: Our synthesis calls for enhanced field experiments, transparent data reporting, and quantitative syntheses to guide large-scale TGB restoration. The overall lack of knowledge on improving resilience and measuring outcomes hampers meaningful comparisons between studies and hinders synthetic views essential for determining appropriate restoration techniques for different degradation sources and suitable monitoring indicators. To overcome the scarcity of reliable and transparent data supporting TGB restoration, we propose a simple checklist for minimum research reporting information and a more complete multilingual standardized guideline.

opencc-zeroMar 2024View details →

Data from: Seed dispersal mode and habitat connectivity underpin variation in carbon stocking between Brazilian biomes

In tropical forests, about 60 to 80% of woody plant species depend on animal-plant interactions for dispersal. The dependence on animal species for dispersal makes this interaction very fragile in the face of anthropogenic changes in land use. Disrupting seed dispersal processes, principally zoochoric dispersal, could significantly alter the long-term carbon storage potential of tropical forests. An important question is how landscape structure changes tree carbon stocks in different types of tropical vegetation and how variation is mediated by the dispersal mode of animal (zoochoric) or abiotic (non-zoochoric) seeds. We focused on tree plots at 126 sites in Brazil spanning four types of forest and savanna vegetation, and calculated carbon stored in zoochoric, non-zoochoric, and large frugivore-dispersed species. Our results showed that carbon stocks in zoochoric species and non-zoochoric species differ significantly among vegetation types, with rainforests having higher stocks in zoochoric species and semideciduous seasonally dry tropical forests having higher values in non-zoochoric species. A greater area of native vegetation promotes higher proportions of carbon stocks dispersed by large frugivore species, whereas a higher mean shape index reduces this proportion. Synthesis: This study highlights that seed-dispersal type underpins the variation in carbon stocks between vegetation types and that the maintenance of habitat of large dispersers and connectivity are key for retaining carbon stocks in zoochoric species, particularly in rainforest and cerrado sensu stricto.

opencc-zeroMar 2024View details →

Data from: Advancing transdisciplinary research on grassy biomes to support resilience in tropical ecosystems and livelihoods

Madagascar-wide metadata relating to Malagasy Grassy Biomes. The understanding of vegetation dynamics in tropical grassy biomes is severely limited across spatio-temporal scales, limiting effective management and support for livelihoods and biodiversity. Despite their extent, utility, and central importance to people and ecosystem function, grassy biomes are often uncritically regarded as degraded, valueless landscapes that result primarily from destructive anthropogenic forces. Moreover, this characterization is often presented without investigation of their history, biodiversity, or ecological complexity. Iconically, Madagascar's grassy biomes cover approximately 80% of the island's land surface today and exemplify core challenges to understanding tropical grassy ecosystems and their interactions with anthropogenic activities across spatio-temporal scales. Intersections between human history and environmental change have sparked debates about the role of land use in shaping grassy biomes (e.g., pastoralism, cultivation, fire use), echoing land use debates globally, and highlighting obstacles to ecosystem and livelihood resilience. Like many tropical biodiversity hotspots, Madagascar faces converging challenges that can be aided by an improved understanding of grassy ecosystems and the livelihoods they support, including food and health insecurity, economic inequities, biodiversity loss, climate change, land conversion, and limited resource access. Centered on improved understanding and management of grassy biomes, we present a framework to guide transdisciplinary research across the tropics by: (1) establishing a common terminology; (2) summarising data contributions and knowledge gaps that reflect those in other tropical regions; (3) identifying priority research questions; and (4) highlighting transdisciplinary and inclusive approaches to resolve knowledge gaps and co-benefit ecosystems and livelihoods.

opencc-by-nc-4.0Jan 2023View details →

Figure 4 in Breeding biology of the Maguari Stork Ciconia maguari (Aves, Ciconiidae) in the Pampa, and an outline in other Brazilian biomes

Figure 4. Seasonal occurrence of records of the Maguari Stork (Ciconia maguari) involving juveniles in the Pampa biome, in Rio Grande do Sul state, southern Brazil. Records were obtained by citizens and gathered in the WikiAves database in June 2020. Arabic numerals after each month represent 10-days long periods – (I):– days 1-10, (II): days 11-20, (III): days 21-31 of each month.

opencc-zeroApr 2024View details →

Acclimation of thermal tolerance in juvenile plants from three biomes is supressed when extremes co-occur

Given the rising frequency of thermal extremes (heatwaves and cold snaps) due to climate change, comprehending how a plant's origin affects its thermal tolerance breadth becomes vital. We studied juvenile plants from three biomes: temperate coastal rainforest, desert, and alpine. In controlled settings, plants underwent hot days and cold nights in a factorial design to examine thermal tolerance acclimation. We assessed thermal thresholds (Tcrit-hot and Tcrit-cold) and thermal tolerance breadth (TTB). We hypothesised that: 1) desert species would show the highest heat tolerance, alpine the greatest cold tolerance, with temperate species intermediate; 2) all species would increase heat tolerance post hot days and cold tolerance after cold nights; 3) combined exposure would broaden TTB more than individual conditions, especially in desert and alpine species. We found that biome responses were minor compared to the responses to the extreme temperature treatments. All plants increased thermal tolerance in response to hot 40°C days (Tcrit-hot increased by ~3.5°C) but there was minimal change in Tcrit-cold in response to the cold -2°C nights. In contrast, when exposed to both hot days and cold nights, on average plants exhibited an antagonistic response in TTB, where cold tolerance decreased and heat tolerance was reduced, and so we did not see the bi-directional expansion we hypothesised. There was, however, considerable variation among species in these responses. As climate change intensifies, plant communities, especially in transitional seasons, will regularly face such temperature swings. Our results shed light on potential plant responses under these extremes, emphasizing the need for deeper species-specific thermal acclimation insights, ultimately guiding conservation efforts.

Figure 6 in Description of a new endemic genus of the Namib Desert and adjacent biomes in Namibia (Tineoidea: Tineidae: Hapsiferinae)

Figure 6 – Rooiklipia michaelmeyi spec. nov., male genitalia, A: lateral, B: ventral, C: dorsal.

Figure 2 in Description of a new endemic genus of the Namib Desert and adjacent biomes in Namibia (Tineoidea: Tineidae: Hapsiferinae)

Figure 2 – Rooiklipia michaelmeyi spec. nov., male holotype, Holstein guest farm.

Figure 5 in Description of a new endemic genus of the Namib Desert and adjacent biomes in Namibia (Tineoidea: Tineidae: Hapsiferinae)

Figure 5 – Rooiklipia mirabib (Mey, 2011), male genitalia, A: lateral, B: ventral, C: dorsal.

Figure 7 in Description of a new endemic genus of the Namib Desert and adjacent biomes in Namibia (Tineoidea: Tineidae: Hapsiferinae)

Figure 7 – Rooiklipia vanbiljoni spec. nov., male genitalia, A: lateral, B: ventral, C: dorsal.

Figure 1 in Description of a new endemic genus of the Namib Desert and adjacent biomes in Namibia (Tineoidea: Tineidae: Hapsiferinae)

Figure 1 – Rooiklipia mirabib (Mey, 2011), male, Rooiklip guest farm.

Figure 3 in Description of a new endemic genus of the Namib Desert and adjacent biomes in Namibia (Tineoidea: Tineidae: Hapsiferinae)

Figure 3 – Rooiklipia vanbiljoni spec. nov., male paratype, Koiimasis guest farm.

Figure 8 in Description of a new endemic genus of the Namib Desert and adjacent biomes in Namibia (Tineoidea: Tineidae: Hapsiferinae)

Figure 8 – Rooiklipia neufferae spec. nov., male genitalia, A: lateral, B: ventral, C: dorsal.

Figure 10 in Description of a new endemic genus of the Namib Desert and adjacent biomes in Namibia (Tineoidea: Tineidae: Hapsiferinae)

Figure 10 – The inselberg Mirabib in the central Namib seen from the north.

Figure 4 in Description of a new endemic genus of the Namib Desert and adjacent biomes in Namibia (Tineoidea: Tineidae: Hapsiferinae)

Figure 4 – Rooiklipia michaelmeyi spec. nov., wing venation.

Figure 12 in Description of a new endemic genus of the Namib Desert and adjacent biomes in Namibia (Tineoidea: Tineidae: Hapsiferinae)

Figure 12 – Currently known distribution of Rooiklipia gen. nov. in Namibia.

opencc-by-4.0Nov 2024View details →

Dataset on Spatial Analysis and Clustering of Deforestation in the Amazon Biome: Spatio-Temporal Patterns and Priority Areas

The dataset was developed with the aim of facilitating the development of a methodology to identify and evaluate deforestation patterns and trends in the Amazon. This innovative method combines deforestation alerts from the Real-Time Deforestation Detection System (DETER) with detailed information on various land categories, including environmental protection areas, settlements, rural properties, undesignated public forests, indigenous lands, and conservation units. The integration of this robust data allowed for the precise identification of areas at risk of deforestation, significantly strengthening monitoring and control activities aimed at combating deforestation in the Amazon region.   Spatial resolution The data are available with a spatial resolution of 25 x 25 km (625 km²) and cover the Amazon biome.   Temporal resolution  Period of observed data: 2017 and 2021   Coordinate reference system  Geographic Coordinate System with Datum SIRGAS 2000 (EPSG:5880)   Data format Data is provided as Shapefile.   Dataset usage  It is free to use, but please make sure to cite the repository and our paper properly if you use this dataset.   Publication & further information For additional scenario information, please contact Francisco Gilney Silva Bezerra (franciscogilney@gmail.com).

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.