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The relative influence of Neogene geomorphological events and Quaternary climatic changes as causal mechanisms on Neotropical diversification remains largely speculative, as most divergence timing inferences are based on a single locus and have limited taxonomic or geographic sampling. To investigate these influences, we use a multilocus (two mitochondrial and 11 nuclear genes) range-wide sampling of Phyllopezus pollicaris, a gecko complex widely distributed across the poorly studied South American 'dry diagonal' biomes. Our approach couples traditional and model-based phylogeography with geospatial methods, and demonstrates Miocene diversification and limited influence of Pleistocene climatic fluctuations on P. pollicaris. Phylogeographic structure and distribution models highlight that persistence across multiple isolated regions shaped the diversification of this species complex. Approximate Bayesian computation supports hypotheses of allopatric and ecological/sympatric speciation between lineages that largely coincide with genetic clusters associated with Chaco, Cerrado, and Caatinga, standing for complex diversification between the 'dry diagonal' biomes. We recover extremely high genetic diversity and suggest that eight well-supported clades may be valid species, with direct implications for taxonomy and conservation assessments. These patterns exemplify how low-vagility species complexes, characterized by strong genetic structure and pre-Pleistocene divergence histories, represent ideal radiations to investigate broad biogeographic histories of associated biomes.

Sympatric species of vesper mice (Calomys expulsus and C. tener) from the Cerrado biome are often distinguished by their respective sizes. Using geometric morphometrics, we tested if interspecific differences were mainly due to isometric or allometric size variations or allometry-free shape differences. To delimit species groups, we used and compared linear discriminant analysis, calculated on subsets of individuals of known identity, and pattern recognition techniques, needing no prior information on specimens. They both yielded similar results, indicating that patterns of interspecific morphological differences are mainly due to size-free shape differences located at landmarks defined at the suture between the frontals and the parietals and between the latter and the interparietal. Correct specimen identification was obtained with pattern recognition techniques using Gaussian mixture models. Morphological differences also were found between the 2 species analyzed and the newly described C. tocantinsi represented here by its paratypes. The combination of geometric morphometrics and pattern recognition techniques seems suitable for systematic analyses aimed at elucidating interspecific patterns of morphological variation in closely related species in field studies and museum specimens.

Natural and human-ignited fires affect all major biomes, altering ecosystem structure, biogeochemical cycles and atmospheric composition. Satellite observations provide global data on spatiotemporal patterns of biomass burning and evidence for the rapid changes in global fire activity in response to land management and climate. Satellite imagery also provides detailed information on the daily or sub-daily position of fires that can be used to understand the dynamics of individual fires. The Global Fire Atlas is a new global dataset that tracks the dynamics of individual fires to determine the timing and location of ignitions, fire size and duration, and daily expansion, fire line length, speed, and direction of spread. Here, we present the underlying methodology and Global Fire Atlas results for 2003–2016 derived from daily moderate-resolution (500 m) Collection 6 MCD64A1 burned-area data. The algorithm identified 13.3 million individual fires over the study period, and estimated fire perimeters were in good agreement with independent data for the continental United States. A small number of large fires dominated sparsely populated arid and boreal ecosystems, while burned area in agricultural and other human-dominated landscapes was driven by high ignition densities that resulted in numerous smaller fires. Long-duration fires in boreal regions and natural landscapes in the humid tropics suggest that fire season length exerts a strong control on fire size and total burned area in these areas. In arid ecosystems with low fuel densities, high fire spread rates resulted in large, short-duration fires that quickly consumed available fuels. Importantly, multiday fires contributed the majority of burned area in all biomass burning regions. A first analysis of the largest, longest and fastest fires that occurred around the world revealed coherent regional patterns of extreme fires driven by large-scale climate forcing. Global Fire Atlas data are publicly available through http://www.globalfiredata.org (last access: 9 August 2018) and https://doi.org/10.3334/ORNLDAAC/1642, and individual fire information and summary data products provide new information for benchmarking fire models within ecosystem and Earth system models, understanding vegetation–fire feedbacks, improving global emissions estimates, and characterizing the changing role of fire in the Earth system.

Understanding future changes in the terrestrial carbon cycle is important for reliable projections of climate change and impacts on ecosystems. It is well known that nitrogen (N) could limit plants' response to increased atmospheric carbon dioxide and it is therefore important to include a representation of the N cycle in Earth system models. Here we present the implementation of the terrestrial nitrogen cycle in the Joint UK Land Environment Simulator (JULES) – the land surface scheme of the UK Earth System Model (UKESM). Two configurations are discussed – the first one (JULES-CN) has a bulk soil biogeochemical model and the second one is a development configuration that resolves the soil biogeochemistry with depth (JULES-CNlayer). In JULES the nitrogen (N) cycle is based on the existing carbon (C) cycle and represents all the key terrestrial N processes in a parsimonious way. Biological N fixation is dependent on net primary productivity, and N deposition is specified as an external input. Nitrogen leaves the vegetation and soil system via leaching and a bulk gas loss term. Nutrient limitation reduces carbon-use efficiency (CUE – ratio of net to gross primary productivity) and can slow soil decomposition. We show that ecosystem level N limitation of net primary productivity (quantified in the model by the ratio of the potential amount of C that can be allocated to growth and spreading of the vegetation compared with the actual amount achieved in its natural state) falls at the lower end of the observational estimates in forests (approximately 1.0 in the model compared with 1.01 to 1.38 in the observations). The model shows more N limitation in the tropical savanna and tundra biomes, consistent with the available observations. Simulated C and N pools and fluxes are comparable to the limited available observations and model-derived estimates. The introduction of an N cycle improves the representation of interannual variability of global net ecosystem exchange, which was more pronounced in the C-cycle-only versions of JULES (JULES-C) than shown in estimates from the Global Carbon Project. It also reduces the present-day CUE from a global mean value of 0.45 for JULES-C to 0.41 for JULES-CN and 0.40 for JULES-CNlayer, all of which fall within the observational range. The N cycle also alters the response of the C fluxes over the 20th century and limits the CO2 fertilisation effect, such that the simulated current-day land C sink is reduced by about 0.5 Pg C yr-1 compared to the version with no N limitation. JULES-CNlayer additionally improves the representation of soil biogeochemistry, including turnover times in the northern high latitudes. The inclusion of a prognostic land N scheme marks a step forward in functionality and realism for the JULES and UKESM models.

Shorelines at the interface of marine, estuarine and terrestrial biomes are among the most degraded and threatened habitats in the coastal zone because of their sensitivity to sea level rise, storms and increased human utilization. Previous efforts to protect shorelines have largely involved constructing bulkheads and seawalls which can detrimentally affect nearshore habitats. Recently, efforts have shifted towards \"living shoreline\" approaches that include biogenic breakwater reefs. Our study experimentally tested the efficacy of breakwater reefs constructed of oyster shell for protecting eroding coastal shorelines and their effect on nearshore fish and shellfish communities. Along two different stretches of eroding shoreline, we created replicated pairs of subtidal breakwater reefs and established unaltered reference areas as controls. At both sites we measured shoreline and bathymetric change and quantified oyster recruitment, fish and mobile macro-invertebrate abundances. Breakwater reef treatments mitigated shoreline retreat by more than 40% at one site, but overall vegetation retreat and erosion rates were high across all treatments and at both sites. Oyster settlement and subsequent survival were observed at both sites, with mean adult densities reaching more than eighty oysters m(-2) at one site. We found the corridor between intertidal marsh and oyster reef breakwaters supported higher abundances and different communities of fishes than control plots without oyster reef habitat. Among the fishes and mobile invertebrates that appeared to be strongly enhanced were several economically-important species. Blue crabs (Callinectes sapidus) were the most clearly enhanced (+297%) by the presence of breakwater reefs, while red drum (Sciaenops ocellatus) (+108%), spotted seatrout (Cynoscion nebulosus) (+88%) and flounder (Paralichthys sp.) (+79%) also benefited. Although the vertical relief of the breakwater reefs was reduced over the course of our study and this compromised the shoreline protection capacity, the observed habitat value demonstrates ecological justification for future, more robust shoreline protection projects.

Large-scale studies of ocean biogeochemistry and carbon cycling have often partitioned the ocean into regions along lines of latitude and longitude despite the fact that spatially more complex boundaries would be closer to the true biogeography of the ocean. Herein, we define 17 open-ocean biomes classified from four observational data sets: sea surface temperature (SST), spring/summer chlorophyll a concentrations (Chl a), ice fraction, and maximum mixed layer depth (maxMLD) on a 1° × 1° grid (available at doi:10.1594/PANGAEA.828650). By considering interannual variability for each input, we create dynamic ocean biome boundaries that shift annually between 1998 and 2010. Additionally we create a core biome map, which includes only the grid cells that do not change biome assignment across the 13 years of the time-varying biomes. These biomes can be used in future studies to distinguish large-scale ocean regions based on biogeochemical function.

The forest, savanna, and grassland biomes, and the transitions between them, are expected to undergo major changes in the future due to global climate change. Dynamic global vegetation models (DGVMs) are very useful for understanding vegetation dynamics under the present climate, and for predicting its changes under future conditions. However, several DGVMs display high uncertainty in predicting vegetation in tropical areas. Here we perform a comparative analysis of three different DGVMs (JSBACH, LPJ-GUESS-SPITFIRE and aDGVM) with regard to their representation of the ecological mechanisms and feedbacks that determine the forest, savanna, and grassland biomes, in an attempt to bridge the knowledge gap between ecology and global modeling. The outcomes of the models, which include different mechanisms, are compared to observed tree cover along a mean annual precipitation gradient in Africa. By drawing on the large number of recent studies that have delivered new insights into the ecology of tropical ecosystems in general, and of savannas in particular, we identify two main mechanisms that need improved representation in the examined DGVMs. The first mechanism includes water limitation to tree growth, and tree–grass competition for water, which are key factors in determining savanna presence in arid and semi-arid areas. The second is a grass–fire feedback, which maintains both forest and savanna presence in mesic areas. Grasses constitute the majority of the fuel load, and at the same time benefit from the openness of the landscape after fires, since they recover faster than trees. Additionally, these two mechanisms are better represented when the models also include tree life stages (adults and seedlings), and distinguish between fire-prone and shade-tolerant forest trees, and fire-resistant and shade-intolerant savanna trees. Including these basic elements could improve the predictive ability of the DGVMs, not only under current climate conditions but also and especially under future scenarios.

Surface soils interact strongly with both climate and biota and provide fundamental ecosystem services that maintain food, climate and human security. However, the quantitative linkages between soil properties, climate and biota remain unclear at the global scale. By compiling a comprehensive global soil database, we mapped eight major soil properties (bulk density; clay, silt, and sand fractions; soil pH; soil organic carbon, SOC, density; soil total nitrogen, STN, density; and soil C:N mass ratios) in the surface soil layer (0–30 cm), based on machine learning algorithms, and demonstrated the quantitative linkages between surface soil properties, climate and biota at the global scale, which we call the global soil–climate–biome diagram. In the diagram, bulk density increased significantly with higher mean annual temperature (MAT) and lower mean annual precipitation (MAP); soil clay fraction increased significantly with higher MAT and MAP; soil pH decreased with higher MAP and lower MAT and the “critical MAP”, which means the corresponding MAP at a soil pH of =7.0 (a shift from alkaline to acidic soil), decreased with lower MAT. SOC density and STN density were both jointly affected by MAT and MAP, showing an increase at lower MAT and a saturation towards higher MAP. Surface soil physical and chemical properties also showed remarkable variation across biomes. The soil–climate–biome diagram suggests shifts in soil properties under global climate and land cover change.

The mid-Piacenzian is known as a period of relative warmth when compared to the present day. A comprehensive understanding of conditions during the Piacenzian serves as both a conceptual model and a source for boundary conditions as well as means of verification of global climate model experiments. In this paper we present the PRISM4 reconstruction, a paleoenvironmental reconstruction of the mid-Piacenzian ( ∼  3 Ma) containing data for paleogeography, land and sea ice, sea-surface temperature, vegetation, soils, and lakes. Our retrodicted paleogeography takes into account glacial isostatic adjustments and changes in dynamic topography. Soils and lakes, both significant as land surface features, are introduced to the PRISM reconstruction for the first time. Sea-surface temperature and vegetation reconstructions are unchanged but now have confidence assessments. The PRISM4 reconstruction is being used as boundary condition data for the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2) experiments.

Climate change leads to species' range shifts, which may end up reducing the effectiveness of protected areas. These deleterious changes in biodiversity may become amplified if they include functionally important species, such as herbivores or pollinators. We evaluated how effective protected areas in the Brazilian Atlantic Forest are in maintaining the diversity of tiger moths (Arctiinae) under climate change. Specifically, we assessed whether protected areas will gain or lose species under climate change and mapped their locations in the Atlantic Forest, in order to assess potential spatial patterns of protected areas that will gain or lose species richness. Comparisons were completed using modeled species occurrence data based on the current and projected climate in 2080. We also built a null model for random allocation of protected areas to identify where reductions in species richness will be more severe than expected. We employed several modern techniques for modeling species' distributions and summarized results using ensembles of models. Our models indicate areas of high species richness in the central and southern regions of the Atlantic Forest both for now and the future. However, we estimate that in 2080 these regions should become climatically unsuitable, decreasing the species' distribution area. Around 4% of species were predicted to become extinct, some of them being endemic to the biome. Estimates of species turnover from current to future climate tended to be high, but these findings are dependent on modeling methods. Our most important results show that only a few protected areas in the southern region of the biome would gain species. Protected areas in semideciduous forests in the western region of the biome would lose more species than expected by the null model employed. Hence, current protected areas are worse off, than just randomly selected areas, at protecting species in the future.