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Understorey herbs form a diverse and understudied plant assemblage in tropical forests. Although several studies and research teams have long been dedicated to the study of this conspicuous vegetation component in Amazonia, no effort to unify the data has been undertaken to date. In contrast to trees and other life forms for which major data compilations already exist, a unified database dedicated to herbs is still lacking. Part of the problem is in defining what is a herb and how to effectively sample herb assemblages. In this article, we describe the database HERBase, an exhaustive compilation of published and unpublished data on herb inventories in Amazonia. We also describe the structure, functioning, and guidelines for data curation and integration in HERBase. We were able to compile information from 1381 plots from all six Amazonian geographic regions. Based on this dataset, we describe and discuss sampling and knowledge gaps, priority areas for new collections, and recommend sampling protocols to facilitate data integration in the future. This novel database provides a unique biodiversity data repository on understorey herbs that will enable new studies on community ecology and biogeography.

SummaryWe describe six new species in the neotropical fern genusDanaeaSm. (Marattiaceae). These areD. alansmithiiTuomisto & Keskiniva in subgenusArthrodanaeaandD. gracilisTuomisto & Keskiniva,D. lanceolataTuomisto & Keskiniva,D. strictaTuomisto & Keskiniva,D. tenuicaulisTuomisto & Keskiniva, andD. vanderwerffiiTuomisto & Keskiniva in subgenusHolodanaea. Recent synonymisations in the genus are discussed, a former synonymisation ofD. lucensA.Rojas is reverted andD. quebradensisChristenh. is synonymised underD. lucens.

The current animal-based food systems are being challenged by environmental, social and economic sustainability issues. A systemic transformation from conventional agriculture to a more sustainable cellular agriculture utilising cell-cultivation technologies to produce animal products has been proposed. The aim of this study is to explore the political and policy stakeholders’ perceptions about cellular agriculture; how they perceive of the current food system and its potential transformation into a cellular agricultural system. The qualitative data comprises 13 interviews conducted with representatives of German stakeholders. The data were analysed with the Transformative Innovation Policy approach. Perceptions of the transformative potential of cultured products were classified into drivers and bottlenecks that either advance or hinder the progress of cellular agriculture in Germany. The results show that the political and policy stakeholders are aware of the changes that are needed, but anticipate that large-scale transformation to the cellular agriculture system may not be a plausible solution in the near future.

SummaryProtecting tropical forests from deforestation is important for mitigating both biodiversity loss and anthropogenic climate change. In Amazonia, a common approach to protected area (PA) impact studies has been to investigate differences among broad PA categories, such as strictly protected, sustainable use and indigenous areas, yet these may be insufficient for the management of PAs at local scales. We used a matching method to compare impacts and carbon emissions avoided during 2011–2016 of individual PAs in the state of Acre (Brazil). Although most PAs had a positive impact and effectively prevented forest loss, we observed substantial variation among them in terms of impacts, pressures and emissions during our study period. The impacts varied from 3.6% avoided to 15.6% induced forest loss compared to expected levels of deforestation estimated for each PA using the matching method. All but a few PAs helped avoid substantial amounts of emissions. Our results emphasize the need for more PA impact studies that compare multiple PAs at the individual level in Amazonia and beyond.

The habitats present in research sites across Amazonia have usually been characterized only very broadly, for example, as inundated or non-inundated (terra firma) forests. However, within each of these categories there is considerable variation in soil properties and floristic composition. This variation is likely to determine habitat quality for animal populations through its effects on primary productivity and food availability, thereby affecting carrying capacity. Therefore, comparison of edaphic properties across sites could provide new insight into which factors affect animal foraging patterns, population densities, and home-range sizes. We provide an example from Estacion Biologica Quebrada Blanco in Peruvian Amazonia, where behavioral studies on primates (especially tamarins) have been conducted for more than three decades but little is known about the edaphic or floristic characteristics of the forest they occupy. We used indicator plant species to estimate and map soil base cation concentration and its variability at Estacion Biologica Quebrada Blanco. We found that soils in the study area are relatively cation-poor in a western Amazonian context, which probably translates into low primary productivity. Some differences in soils among the home-ranges of three tamarin groups were also observed, illustrating the usefulness of the method for detailed habitat mapping. in Spanish is available with online material.

Forest edges are an increasingly common feature of Amazonian landscapes due to human-induced forest fragmentation. Substantial evidence shows that edge effects cause profound changes in forest biodiversity and productivity. However, the broader impacts of edge effects on ecosystem functioning remain unclear. Assessing the three-dimensional arrangement of forest elements has the potential to unveil structural traits that are scalable and closely linked to important functional characteristics of the forest. Using over 600 high-resolution terrestrial laser scanning measurements, we present a detailed assessment of forest structural metrics linked to ecosystem processes such as energy harvesting and light use efficiency. Our results show a persistent change in forest structural characteristics along the edges of forest fragments, which resulted in a significantly lower structural diversity, in comparison with the interior of the forest fragments. These structural changes could be observed up to 35 m from the forest edges and are likely to reflect even deeper impacts on other ecosystem variables such as microclimate and biodiversity. Traits related to vertical plant material allocation were more affected than traits related to canopy height. We demonstrate a divergent response from the forest understory (higher vegetation density close to the edge) and the upper canopy (lower vegetation density close to the edge), indicating that assessing forest disturbances using vertically integrated metrics, such as total plant area index, can lead to an erroneous interpretation of no change. Our results demonstrate the strong potential of terrestrial laser scanning for benchmarking broader-scale (e.g. airborne and space-borne) remote sensing assessments of forest disturbances, as well as to provide a more robust interpretation of biophysical changes detected at coarser resolutions. publishedVersion

Several factors have been proposed as drivers of species diversification in the Neotropics, including environmental heterogeneity, the development of drainage systems and historical changes in forest distribution due to climatic oscillations. Here, we investigate which drivers contributed to the evolutionary history and current patterns of diversity of a polymorphic songbird (Arremon taciturnus) that is widely distributed in Amazonian and Atlantic forests as well as in Cerrado gallery and seasonally-dry forests. We use genomic, phenotypic and habitat heterogeneity data coupled with climatic niche modelling. Results suggest the evolutionary history of the species is mainly related to paleoclimatic changes, although changes in the strength of the Amazon river as a barrier to dispersal, current habitat heterogeneity and geographic distance were also relevant. We propose an ancestral distribution in the Guyana Shield, and recent colonization of areas south of the Amazon river at ~380 to 166 kya, and expansion of the distribution to southern Amazonia, Cerrado and the Atlantic Forest. Since then, populations south of the Amazon River have been subjected to cycles of isolation and possibly secondary contact due to climatic changes that affected habitat heterogeneity and population connectivity. Most Amazonian rivers are not associated with long lasting isolation of populations, but some might act as secondary barriers, susceptible to crossing under specific climatic conditions. Morphological variation, while stable in some parts of the distribution, is not a reliable indicator of genetic structure or phylogenetic relationships.

A novel food such as plant cell culture (PCC) is an important complementary asset for traditional agriculture to tackle global food insecurity. To evaluate environmental impacts of PCC, a life cycle assessment was applied to tobacco bright yellow-2 and cloudberry PCCs. Global warming potential (GWP), freshwater eutrophication potential (FEUP), marine eutrophication potential, terrestrial acidification potential (TAP), stratospheric ozone depletion, water consumption and land use were assessed. The results showed particularly high contributions (82–93%) of electricity consumption to GWP, FEUP and TAP. Sensitivity analysis indicated that using wind energy instead of the average Finnish electricity mix reduced the environmental impacts by 34–81%. Enhancement in the energy efficiency of bioreactor mixing processes and reduction in cultivation time also effectively improved the environmental performance (4–47% reduction of impacts). In comparison with other novel foods, the environmental impacts of the PCC products studied were mostly comparable to those of microalgae products but higher than those of microbial protein products produced by autotrophic hydrogen-oxidizing bacteria. Assayed fresh PCC products were similar or close to GWP of conventionally grown food products and, with technological advancements, can be highly competitive.

Novel food production technologies are being developed to address the challenges of securing sustainable and healthy nutrition for the growing global population. This study assessed the environmental impacts of microbial protein (MP) produced by autotrophic hydrogen-oxidizing bacteria (HOB). Data was collected from a company currently producing MP using HOB (hereafter simply referred to as MP) on a small-scale. Earlier studies have performed an environmental assessment of MP on a theoretical basis but no study yet has used empirical data. An attributional life cycle assessment (LCA) with a cradle-to-gate approach was used to quantify global warming potential (GWP), land use, freshwater and marine eutrophication potential, water scarcity, human (non-)carcinogenic toxicity, and the cumulative energy demand (CED) of MP production in Finland. A Monte Carlo analysis was performed to assess uncertainties. The impacts of alternative production options and locations were explored. The impacts were compared with animal- and plant-based protein sources for human consumption as well as protein sources for feed. The results showed that electricity consumption had the highest contribution to environmental impacts. Therefore, the source of energy had a substantial impact on the results. MP production using hydropower as an energy source yielded 87.5% lower GWP compared to using the average Finnish electricity mix. In comparison with animal-based protein sources for food production, MP had 53-100% lower environmental impacts depending on the reference product and the source of energy assumed for MP production. When compared with plant-based protein sources for food production, MP had lower land and water use requirements, and eutrophication potential but GWP was reduced only if low-emission energy sources were used. Compared to protein sources for feed production, MP production often resulted in lower environmental impact for GWP (FHE), land use, and eutrophication and acidification potential, but generally caused high water scarcity and required more energy.

Species distribution models are required for the research and management of biodiversity in the hyperdiverse tropical forests, but reliable and ecologically relevant digital environmental data layers are not always available. We here assess the usefulness of multispectral canopy reflectance (Landsat) relative to climate data in modelling understory plant species distributions in tropical rainforests. We used a large dataset of quantitative fern and lycophyte species inventories across lowland Amazonia as the basis for species distribution modelling (SDM). As predictors, we used CHELSA climatic variables and canopy reflectance values from a recent basin-wide composite of Landsat TM/ETM+ images both separately and in combination. We also investigated how species accumulate over sites when environmental distances were expressed in terms of climatic or surface reflectance variables. When species accumulation curves were constructed such that differences in Landsat reflectance among the selected plots were maximised, species accumulated faster than when climatic differences were maximised or plots were selected in a random order. Sixty-nine species were sufficiently frequent for species distribution modelling. For most of them, adequate SDMs were obtained whether the models were based on CHELSA data only, Landsat data only or both combined. Model performance was not influenced by species’ prevalence or abundance. Adding Landsat-based environmental data layers overall improved the discriminatory capacity of SDMs compared to climate-only models, especially for soil specialist species. Our results show that canopy surface reflectance obtained by multispectral sensors can provide studies of tropical ecology, as exemplified by SDMs, much higher thematic (taxonomic) detail than is generally assumed. Furthermore, multispectral datasets complement the traditionally used climatic layers in analyses requiring information on environmental site conditions. We demonstrate the utility of freely available, global remote sensing data for biogeographical studies that can aid conservation planning and biodiversity management.

Maps of environmental characteristics are needed to improve our understanding of species distributions and ecosystem dynamics. Despite the growing demand for digital environmental maps, scarcity of environmental field samples to be used as input data often constrains the accuracy of such maps, especially for soils. We developed and tested a method that combines information on species–environment associations and the spatial distribution of indicator species (as retrieved from repositories such as GBIF) to improve mapping accuracy of environmental variables. Our approach includes: (a) Compile field data on the environmental variable of interest (direct environmental data) and documented occurrences of the species to be used as indicators; (b) define species optima for the environmental variable; (c) use georeferenced records of the indicator species to calculate species‐based environmental values (indirect environmental data); (d) generate maps using direct and indirect environmental data as input data for interpolation; (e) validate the maps. We applied the method to map the concentration of exchangeable base cations in Amazonian soils using fern and lycophyte species as indicators. Including soil values that had been indirectly estimated using indicator species represented a 12‐fold increase in the number of input data points used for mapping. At the same time, map accuracy improved considerably: the correlation between mapped soil cation concentration estimates and field‐measured values from an independent validation dataset increased from r = 0.48 to r = 0.71. Knowledge on species–environment relationships can be useful for modelling ecologically relevant environmental variables in areas where species occurrence data are more readily available than direct environmental measurements. The method works even with haphazard species occurrence points obtained from public repositories such as GBIF and can be applied to other environmental variables and other indicator groups, provided that the environmental variable of interest is relevant as a determinant of species occurrences in the indicator group. The Amazonian soil cation concentration maps produced (available at https://doi.pangaea.de/10.1594/PANGAEA.879542) can be used as digital layers in species distribution and habitat modelling, and to guide conservation actions in Amazonia.

In the version of this Article originally published, in Supplementary Table 7, the energy and land-use values for mealworms in Thevenot et al. were mistakenly swapped. The correct values are 65.39 MJ for energy use and 4.31 m 2 for land use. In the same table, the energy and land use values for black soldier fly in Salome et al. were incorrectly given as 0.14 MJ and 41.67 m 2 ; they should have read 7.248 MJ and 0.024 m 2 (respectively). Correcting these values has led to corresponding changes in Fig. 3, Supplementary Figs 2 and 4 and Supplementary Table 8. Additionally, in the panel of Fig. 3 that contains information about vitamin A, the land-use values of 593 for pork and 1914 for beef were incorrect, and should have been 666 and 3238, respectively.

Aims Assessment of the future of biodiversity under climate change has been based on climate-only models. We investigated the effects of including soil information when predicting future suitable areas for selected plant species in Amazonia. Methods We modelled current and future suitable habitats for 35 plant species and compared results of climate-only models with those obtained when climatic and edaphic variables were included. We considered six climatic scenarios for 2050 using different algorithms and projections of atmospheric CO2 concentration. Results Twenty-five species distribution models had an AUC > 0.69. Out of those, edaphic variables had the greatest contribution in 11 species models, while climatic variables were more important for 14 species. The inclusion of soil variables affected the size and shape of predicted suitable areas, especially in future models. For nearly half of the species, the size of future suitable areas were smaller in climate+soil models than predicted by climate-only models. Area reduction was more extreme in future scenarios with the higher level of CO2 concentration. Conclusions Our results highlight the importance of moving beyond climatic scenarios when modelling biodiversity responses to climate change. Failure to include soils in the models can overestimate future habitat suitability for many plant species. Kluwer Academic Publishers

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In the version of this Comment initially published, there was an error in the spelling of an author’s name. The name of Jessica Fanzo, initially appearing as “Frazo,” has been corrected in the online version of the article.

Questions: Understanding spatial variation in floristic composition is crucial to quantify the extent, patchiness and connectivity of distinct habitats and their spatial relationships. Broad-scale variation in floristic composition and the degree of uniqueness of different regions remains poorly mapped and understood in several areas across the globe. We here aim to map vegetation heterogeneity in Amazonia. Location: Middle Juruá river region, Amazonas State, Brazil. Methods: We mapped four plant groups by applying machine learning to scale up locally observed community composition and using environmental and remotely sensed variables as predictors, which were obtained as GIS layers. To quantify how reliable our predictions were, we made an assessment of model transferability and spatial applicability. We also compared our floristic composition map to the official Brazilian national-level vegetation classification. Results: The overall performance of our floristic models was high for all four plant groups, especially ferns, and the predictions were found to be spatially congruent and highly transferable in space. For some areas, the models were assessed not to be applicable, as the field sampling did not cover the spectral or environmental characteristics of those regions. Our maps show extensive habitat heterogeneity across the region. When compared to the Brazilian vegetation classification, floristic composition was relatively homogeneous within dense forests, while floristic heterogeneity in rainforests classified as open was high. Conclusion: Our maps provide geoecological characterization of the regions and can be used to test biogeographical hypotheses, develop species distribution models and, ultimately, aid science-based conservation and land-use planning.

The anticipated failure of many countries to achieve the Sustainable Development Goals by 2030 necessitates the assessment of science–policy engagement mechanisms for food systems transformation. We explore options for enhancing existing partnerships, mandates and resources — or reimagining a new mission — for science–policy interfaces.

To determine the effect of rivers, environmental conditions, and isolation by distance on the distribution of species in Amazonia. Location: Brazilian Amazonia. Time period: Current. Major taxa studied: Birds, fishes, bats, ants, termites, butterflies, ferns + lycophytes, gingers and palms. We compiled a unique dataset of biotic and abiotic information from 822 plots spread over the Brazilian Amazon. We evaluated the effects of environment, geographic distance and dispersal barriers (rivers) on assemblage composition of animal and plant taxa using multivariate techniques and distance- and raw-data-based regression approaches. Environmental variables (soil/water), geographic distance, and rivers were associated with the distribution of most taxa. The wide and relatively old Amazon River tended to determine differences in community composition for most biological groups. Despite this association, environment and geographic distance were generally more important than rivers in explaining the changes in species composition. The results from multi-taxa comparisons suggest that variation in community composition in Amazonia reflects both dispersal limitation (isolation by distance or by large rivers) and the adaptation of species to local environmental conditions. Larger and older river barriers influenced the distribution of species. However, in general this effect is weaker than the effects of environmental gradients or geographical distance at broad scales in Amazonia, but the relative importance of each of these processes varies among biological groups.

Recognition of the spatial variation in tree species composition is a necessary precondition for wise management and conservation of forests. In the Peruvian Amazonia, this goal is not yet achieved mostly because adequate species inventory data has been lacking. The recently started Peruvian national forest inventory (INFFS) is expected to change the situation. Here, we analyzed genus-level variation, summarized through non-metric multidimensional scaling (NMDS), in a set of 157 INFFS inventory plots in lowland to low mountain rain forests (&lt, 2000 m above sea level) using Landsat satellite imagery and climatic, edaphic, and elevation data as predictor variables. Genus-level floristic patterns have earlier been found to be indicative of species-level patterns. In correlation tests, the floristic variation of tree genera was most strongly related to Landsat variables and secondly to climatic variables. We used random forest regression, under varying criteria of feature selection and cross-validation, to predict the floristic composition on the basis of Landsat and environmental data. The best model explained &gt, 60% of the variation along NMDS axes 1 and 2 and 40% of the variation along NMDS axis 3. We used this model to predict the three NMDS dimensions at a 450-m resolution over all of the Peruvian Amazonia and classified the pixels into 10 floristic classes using k-means classification. An indicator analysis identified statistically significant indicator genera for 8 out of the 10 classes. The results are congruent with earlier studies, suggesting that the approach is robust and can be applied to other tropical regions, which is useful for reducing research gaps and for identifying suitable areas for conservation.

Aim: To disentangle the influence of environmental factors at different spatial grains (regional and local) on fern and lycophyte species richness and ask how regional and plot-level richness are related to each other. Location: Global. Time period: Present. Major Taxa studied: Ferns and lycophytes. Methods: We explored fern and lycophyte species richness at two spatial grains, regional (hexagonal grid cells of 7,666 km2) and plot level (300–500 m2), in relation to environmental data at regional and local grains (the 7,666 km2 hexagonal grid cells and 4 km2 square grid cells, respectively). For the regional grain, we obtained species richness data for 1,243 spatial units and used them together with climatic and topographical predictors to model global fern richness. For the plot-level grain, we collated a global dataset of nearly 83,000 vegetation plots with a surface area in the range 300–500 m2 in which all fern and lycophyte species had been counted. We used structural equation modelling to identify which regional and local factors have the biggest effect on plot-level fern and lycophyte species richness worldwide. We investigate how plot-level richness is related to modelled regional richness at the plot's location. Results: Plot-level fern and lycophyte species richness were best explained by models allowing a link between regional environment and plot-level richness. A link between regional richness and plot-level richness was essential, as models without it were rejected, while models without the regional environment-plot-level richness link were still valid but had a worse goodness-of-fit value. Plot-level richness showed a hump-shaped relationship with regional richness. Main conclusions: Regional environment and regional fern and lycophyte species richness each are important determinants of plot-level richness, and the inclusion of one does not substitute the inclusion of the other. Plot-level richness increases with regional richness until a saturation point is reached, after which plot-level richness decreases despite increasing regional richness, possibly reflecting species interactions.

Remote sensing can provide useful explanatory variables for tree species distribution modeling, but only a few studies have explored this potential in Amazonia at local scales. Particularly for tropical forest management it would be useful to be able to predict the potential distribution of important tree taxa in areas where field data is as yet missing. Forest concessions produce valuable census data that cover large areas with high sampling effort and can be used as occurrence data in species distribution models (SDM). Nevertheless, these tree records are often spatially clumped and possibly only provide accurate predictions over areas close to where the training occurrence records are located. Here, we aim at investigating to what degree SDM performance and spatial predictions differ between models that have different spatial configurations of the occurrence data. For this, we divided the available occurrence data from a forest concession census in Peruvian Amazonia into different spatial configurations (narrow, elongated and compact), each of which contained approximately 20% of the full dataset. We then modelled the distributions of five tree taxa using Landsat data and elevation. More elongated configurations of the training data were more representative of the available environmental space, and also produced more robust SDMs. Average model performance (expressed as AUC) was 5% higher and variation in model performance 50% lower when elongated rather than compact configurations of training area were used. This confirms that covering only a small fraction of the environmental variability in the area of interest may lead to misleading SDM predictions, which needs to be taken into account when forest management decisions are based on SDMs.

Altering diets is increasingly acknowledged as an important solution to feed the world’s growing population within the planetary boundaries. In our search for a planet-friendly diet, the main focus has been on eating more plant-source foods, and eating no or less animal-source foods, while the potential of future foods, such as insects, seaweed or cultured meat has been underexplored. Here we show that compared to current animal-source foods, future foods have major environmental benefits while safeguarding the intake of essential micronutrients. The complete array of essential nutrients in the mixture of future foods makes them good-quality alternatives for current animal-source foods compared to plant-source foods. Moreover, future foods are land-efficient alternatives for animal-source foods, and if produced with renewable energy, they also offer greenhouse gas benefits. Further research on nutrient bioavailability and digestibility, food safety, production costs and consumer acceptance will determine their role as main food sources in future diets.

Vertical farming and cultured-meat production have the potential to contribute to the reduction of negative environmental impacts of agriculture and improve global food security in the future. However, the technologies are still at early stages of development and thus cannot help with the urgent problems we face.

Combining palaeontological and neontological data offers a unique opportunity to investigate the relative roles of biotic and abiotic controls of species diversification, and the importance of origination versus extinction in driving evolutionary dynamics. Ferns comprise a major terrestrial plant radiation with an extensive evolutionary history providing a wealth of modern and fossil data for modelling environmental drivers of diversification. Here we develop a novel Bayesian model to simultaneously estimate correlations between diversification dynamics and multiple environmental trajectories. We estimate the impact of different factors on fern diversification over the past 400 million years by analysing a comprehensive dataset of fossil occurrences and complement these findings by analysing a large molecular phylogeny. We show that origination and extinction rates are governed by fundamentally different processes: originations depend on within-group diversity but are largely unaffected by environmental changes, whereas extinctions are strongly affected by external factors such as climate and geology. Our results indicate that the prime driver of fern diversity dynamics is environmentally driven extinction, with origination being an opportunistic response to diminishing ecospace occupancy.

Persistent cloud cover is an important obstacle to studying the ground surface of tropical rain forest areas using high-resolution optical data, such as those obtained with Landsat satellites. The identification and masking of the cloud-affected parts of such images is a necessary preprocessing step, but it easily leads to impractical fragmentation of the informative image area. Pixel-based multitemporal image compositing solves the fragmentation problem, but depends on a predefined compositing period length and compositing criterion. Here, we evaluate the radiometric consistency of Landsat TM/ETM+ composite images over undisturbed Amazonian forests and test to what degree it varies with the number of available multitemporal observations per pixel and the compositing criterion. Five compositing criteria were tested: maximum NDVI, median red, median near-infrared, multidimensional medoid, and minimum aerosol optical thickness. Each was applied to datasets consisting of 3–30 observations per pixel. Compositing quality was assessed both visually and with quantitative measures using the overlap area of neighboring WRS-2 scenes. We found that the medoid approach generated the most radiometrically consistent composite images. Composite image quality increased monotonically with the number of observations, but with diminishing returns. Satisfactory results were generally obtained with 10–15 observations per pixel.

Summary Understanding the responses of individual plant species along different edaphic gradients is a key question in ecology, with implications to community assembly, functioning of forest ecosystems, niche theory, and conservation planning. In tropical rain forests, responses to soil nutrients have been described only for a handful of species. Even abundant and conspicuous components of the forest, such as the palms (Arecaceae), are largely unknown in this respect. We inventoried all palm species and analysed soil chemistry in 96 sites across western Amazonia to (i) assess the relative importance of some micronutrients vs. macronutrients as drivers of patterns in palm species composition, (ii) model the response shapes of 61 palm taxa and of canopy vs. understory palms along gradients of selected soil nutrients, (iii) determine if the response shapes of conspecific palm varieties differ in relation to the most important observed soil nutrient, and (iv) assess if the rank-order of the most abundant canopy species changes along edaphic gradients. Patterns in palm species composition were best explained by Mehlich-III extractable exchangeable bases (Ca, K, Mg) and phosphorus (P), with the different palm species clearly separating along the soil cation concentration gradient. All 61 palm taxa exhibited statistical responses along soil nutrient gradients. Response shapes in relation to exchangeable bases varied among bimodal skewed (51% of species), unimodal skewed (47%), and monotonic (2%). There were no significant differences between canopy vs. understory species in their mean response shapes to the exchangeable bases, but canopy species had significantly higher mean optimum value for soil P and greater mean niche width along the Ca, K, Mg, P, and boron (B) gradients. Varieties of the same species of Bactris, Desmoncus and Geonoma generally had different response shapes and different optima for the exchangeable bases. Synthesis: Among the soil variables we studied, macronutrients (especially exchangeable bases and P) emerged as more important than micronutrients in predicting species abundances in palm communities of western Amazonian non-inundated forests. Non-gaussian responses were predominant, conspecific palm varieties exhibited different response types, and the rank-order of dominance of canopy palms varied along the gradients of exchangeable bases and P. Together, these findings advance niche theory about palms and may be used for generating better predictive models of palm species distributions and for experimental studies that search for the physiological mechanisms underlying inter- and infra-specific trade-offs along edaphic gradients. This article is protected by copyright. All rights reserved.

Biodiversity mapping in extensive tropical forest areas poses a major challenge for the interpretation of Landsat images, because floristically clearly distinct forest types may show little difference in reflectance. In such cases, the effects of the bidirectional reflection distribution function (BRDF) can be sufficiently strong to cause erroneous image interpretation and classification. Since the opening of the Landsat archive in 2008, several BRDF normalization methods for Landsat have been developed. The simplest of these consist of an empirical view angle normalization, whereas more complex approaches apply the semi-empirical Ross–Li BRDF model and the MODIS MCD43-series of products to normalize directional Landsat reflectance to standard view and solar angles. Here we quantify the effect of surface anisotropy on Landsat TM/ETM+ images over old-growth Amazonian forests, and evaluate five angular normalization approaches. Even for the narrow swath of the Landsat sensors, we observed directional effects in all spectral bands. Those normalization methods that are based on removing the surface reflectance gradient as observed in each image were adequate to normalize TM/ETM+ imagery to nadir viewing, but were less suitable for multitemporal analysis when the solar vector varied strongly among images. Approaches based on the MODIS BRDF model parameters successfully reduced directional effects in the visible bands, but removed only half of the systematic errors in the infrared bands. The best results were obtained when the semi-empirical BRDF model was calibrated using pairs of Landsat observation. This method produces a single set of BRDF parameters, which can then be used to operationally normalize Landsat TM/ETM+ imagery over Amazonian forests to nadir viewing and a standard solar configuration.

The changes in the publishing landscape are inevitable and the transition is bound to create challenges and occasional dysfunctions. Ecography is a journal of the Nordic Society Oikos and, as such, is committed to explore solutions that will enable adherence to open science policies while minimising the adverse effects of inequality that can emerge from them.

The riverine barrier hypothesis is a central concept in Amazonian biogeography. It states that large rivers limit species distributions and trigger vicariant speciation. Although the hypothesis has explanatory power, many recent biogeographical observations addressing it have produced conflicting results. We propose that the controversies arise because tributary arrangements in the Amazon river system have changed in geologically recent times, such that large tracts of forest that were on the same side of a river at one time got separated to different sides at another. Based on topographical data and sediment dating, we map about 20 major avulsion and river capture events that have rearranged the river network in central Amazonia during the late Pleistocene and Holocene. We identify areas where past riverine barrier effects might still linger in the absence of a major river, as well as areas where such effects may not yet have accumulated across an existing river. These results call for a reinterpretation of previous biogeographical studies and a reorientation of future works to take into account the idiosyncratic histories of individual rivers.

AIM: To map and interpret floristic and geoecological patterns across the Amazon basin by combining extensive field data with basin‐wide Landsat imagery and climatic data. LOCATION: Amazonia. TAXON: Ground truth data on ferns and lycophytes; remote sensing results reflect forest canopy properties. METHODS: We used field plot data to assess main ecological gradients across Amazonia and to relate floristic ordination axes to soil base cation concentration, Climatologies at High Resolution for the Earth's Land Surface Areas (CHELSA) climatic variables and reflectance values from a basin‐wide Landsat image composite with generalized linear models. Ordination axes were then predicted across all Amazonia using Landsat and CHELSA, and a regional subdivision was obtained using k‐medoid classification. RESULTS: The primary floristic gradient was strongly related to base cation concentration in the soil, and the secondary gradient to climatic variables. The Landsat image composite revealed a tapestry of broad‐scale variation in canopy reflectance characteristics across Amazonia. Ordination axis scores predicted using Landsat and CHELSA variables produced spatial patterns consistent with existing knowledge on soils, geology and vegetation, but also suggested new floristic patterns. The clearest dichotomy was between central Amazonia and the peripheral areas, and the available data supported a classification into at least eight subregions. MAIN CONCLUSIONS: Landsat data are capable of predicting soil‐related species compositional patterns of understorey ferns and lycophytes across the Amazon basin with surprisingly high accuracy. Although the exact floristic relationships may differ among plant groups, the observed ecological gradients must be relevant for other plants as well, since surface reflectance recorded by satellites is mostly influenced by the tree canopy. This opens exciting prospects for species distribution modelling, conservation planning, and biogeographical and ecological studies on Amazonian biota. Our maps provide a preliminary geoecological subdivision of Amazonia that can now be tested and refined using field data of other plant groups and from hitherto unsampled areas.