Tropical forests harbor high biodiversity but are suffering from ongoing human-induced habitat disruption, including the expansion of transportation infrastructure. Existing research lacks analysis of the impacts of planned railways on landscape connectivity from the perspective of landscape composition and spatial distribution, resulting in insufficient conservation of patches with significant connectivity functions during route design. This study employed morphological spatial pattern analysis and minimum cumulative resistance model to explore the impact of Ethiopia's planned railways on landscape connectivity at both patch and regional scales within tropical montane forests, identifying alternative routes that could mitigate connectivity impacts. Results showed that proposed railways would significantly disrupt landscape connectivity, destroying 2210 km2 of core forests. Interconnected forest patches would decrease from 51.9% to 41.4%, with a total of 854 km of railways having a strong negative impact on landscape connectivity. There were 29 least-cost routes with a total length of 5895 km, which would help to optimize the location of railways by reducing their environmental costs. The results contribute to identify forest patches that are critical for maintaining connectivity and reducing the negative impacts of railways development on landscape connectivity in Ethiopia and beyond.

Climatic and anthropogenic disturbances have led to intense small-scale tree cover loss in global forests. However, it remains unclear when forest attributes at a large scale (e.g., 0.05° resolution) will decline in response to such sub-grid (e.g., 30-m) tree cover losses within forest ecosystems. Utilizing global maps of forest attribute proxies, we discover that vegetation greenness, canopy structure, composition, and photosynthesis function can all increase under limited tree cover loss, indicating a widely existing safety margin in global forests that is primarily buffered by a positive edge effect of landscape fragmentation within forest ecosystems. The safety margin varies across biomes (tropical: 7.7%; temperate: 3.7%; boreal: 1.0%) and is often positively correlated with ecosystem resistance. In addition, about 35.7% of the remaining global forests have exceeded the safety margin. Our finding contrasts with the conventional perception that sub-grid tree cover losses are inevitably associated with declines in forest attributes and functions. It provides quantitative information for mitigating forest degradation and has strong implications for sustainable forest management practices.

Old-growth tropical forests store vast amounts of carbon in their aboveground biomass (AGB), yet the relative roles of abiotic factors such as climate, soil, and topography in governing its spatial distribution remain poorly understood. In particular, the degree to which climate acts on AGB through forest structure is still poorly quantified at the pantropical scale. Using a pantropical dataset of more than 2,000 old-growth forest plots and a structure-explicit framework, we assess how climate influences AGB through its effects on four structural attributes: basal area, mean diameter, stem density, and basal area-weighted wood density. We find that climate shapes AGB primarily through its effects on forest structure. However, structural attributes respond to climate in opposite directions, so climate’s net effect on AGB largely cancels out, and no clear climate-AGB relationship emerges across tropical regions. Moreover, only wood density responds consistently, decreasing with annual precipitation and increasing with precipitation seasonality, whereas all other attributes respond to climate differently from one region to another. This geographical variation further obscures any global climatic signal on AGB and points to the role of biogeographic history in shaping forest structure. Our findings highlight the central role of the climate-structure nexus in explaining AGB variation, and call for structure-explicit models to improve carbon stock predictions and inform climate adaptation strategies.

Amazon rainforests face intensifying water stress due to increases in vapour pressure deficit and changing hydrological regimes. Embolism resistance (Ψ50) is a critical metric of tree survival under drought conditions, it is defined as a plant's capacity to resist disruption of xylem water flow due to air bubble formation from water stress. However, measurements of Ψ50 are only available for a limited number of Amazon locations and species. Conversely, data on forest taxonomic composition are abundant across Amazonia, and if Ψ50 is conserved phylogenetically, these data could provide a way to scale-up drought resistance patterns. Here we evaluate Ψ50 measurements across non-flooded Amazonian tree taxa and reveal a moderate phylogenetic signal, with phylogenetic conservatism evident at the family-level. Notably, Fabaceae is amongst the most embolism-resistant tree families in Amazonia. Leveraging the phylogenetic signal we use species composition and tree size data from 448 forest plots across Amazonia to produce a macroecological assessment of Amazonian vulnerability to embolism. The resulting estimate spatial pattern reveals that forests in the Brazilian and Guiana Shield regions, where Fabaceae abundance is high, show strong resistance to embolism. In contrast, tree communities in Western Amazonia appear more vulnerable to embolism, suggesting a reduced capacity to withstand future drought conditions.

Wood density is a critical control on tree biomass, so poor understanding of its spatial variation can lead to large and systematic errors in forest biomass estimates and carbon maps. The need to understand how and why wood density varies is especially critical in tropical America where forests have exceptional species diversity and spatial turnover in composition. As tree identity and forest composition are challenging to estimate remotely, ground surveys are essential to know the wood density of trees, whether measured directly or inferred from their identity. Here, we assemble an extensive dataset of variation in wood density across the most forested and tree-diverse continent, examine how it relates to spatial and environmental variables, and use these relationships to predict spatial variation in wood density over tropical and sub-tropical South America. Our analysis refines previously identified east-west Amazon gradients in wood density, improves them by revealing fine-scale variation, and extends predictions into Andean, dry, and Atlantic forests. The results halve biomass prediction errors compared to a naïve scenario with no knowledge of spatial variation in wood density. Our findings will help improve remote sensing-based estimates of aboveground biomass carbon stocks across tropical South America.

Camera traps are widely used in wildlife research and monitoring, so it is imperative to understand their strengths, limitations, and potential for increasing impact. We investigated a decade of use of wildlife cameras (2012-2022) with a case study on Australian terrestrial vertebrates using a multifaceted approach. We (i) synthesised information from a literature review; (ii) conducted an online questionnaire of 132 professionals; (iii) hosted an in-person workshop of 28 leading experts representing academia, non-governmental organisations (NGOs), and government; and (iv) mapped camera trap usage based on all sources. We predicted that the last decade would have shown: (i) exponentially increasing sampling effort, a continuation of camera usage trends up to 2012; (ii) analytics to have shifted from naive presence/absence and capture rates towards hierarchical modelling that accounts for imperfect detection, thereby improving the quality of outputs and inferences on occupancy, abundance, and density; and (iii) broader research scales in terms of multi-species, multi-site and multi-year studies. However, the results showed that the sampling effort has reached a plateau, with publication rates increasing only modestly. Users reported reaching a saturation point in terms of images that could be processed by humans and time for complex analyses and academic writing. There were strong taxonomic and geographic biases towards medium-large mammals (>500g) in forests along Australia's southeastern coastlines, reflecting proximity to major cities. Regarding analytical choices, bias-prone indices still accounted for similar to 50% of outputs and this was consistent across user groups. Multi-species, multi-site and multiple-year studies were rare, largely driven by hesitancy around collaboration and data sharing. There is no widely used repository for wildlife camera images and the Atlas of Living Australia (ALA) is the dominant repository for sharing tabular occurrence records. However, the ALA is presence-only and thus is unsuitable for creating detection histories with absences, inhibiting hierarchical modelling. Workshop discussions identified a pressing need for collaboration to enhance the efficiency, quality and scale of research and management outcomes, leading to the proposal of a Wildlife Observatory of Australia (WildObs). To encourage data standards and sharing, WildObs should (i) promote a metadata collection app; (ii) create a tagged image repository to facilitate artificial intelligence/machine learning (AI/ML) computer vision research in this space; (iii) address the image identification bottleneck via the use of AI/ML-powered image-processing platforms; (iv) create data commons for detection histories that are suitable for hierarchical modelling; and (v) provide capacity building and tools for hierarchical modelling. Our review highlights that while Australia's investments in monitoring biodiversity with cameras position it to be a global leader in this context, realising that potential requires a paradigm shift towards best practices for collecting, curating, sharing and analysing 'Big Data'. Our findings and framework have broad applicability outside Australia to enhance camera usage to meet conservation and management objectives ranging from local to global scales. This review articulates a country/continental observatory approach that is also suitable for international collaborative wildlife research networks.

Roads act as conduits for human incursions and hence underlie many of humanity's impacts on nature, including deforestation, wildfires, and natural-resource overexploitation. Unfortunately, existing roadmaps often drastically underestimate the true extent of road networks and future predictions of road-related impacts rely on incomplete and outdated data, undermining development planning and conservation decision-making. Here, we develop a multivariate "road expansion risk" index to identify areas prone to road building and therefore vulnerable to road-related environmental impacts. Using a massive road dataset-137 million 1-ha raster cells drawn from three different sources arrayed across the Amazon and Congo basins and insular Asia-Pacific region-we predict road-prone locations via a statistical model that integrates a range of biophysical, socioeconomic, and administrative data. This highly integrative, large-scale approach allowed us to identify areas likely to experience future road building and regions that may contain unmapped roads. Importantly, our road expansion risk index is a strong predictor of forest loss and degradation and can hence identify future road building and deforestation hotspots, even for the many tropical forest locales with grossly deficient road data.

Tropical forest canopies are the biosphere's most concentrated atmospheric interface for carbon, water and energy1,2. However, in most Earth System Models, the diverse and heterogeneous tropical forest biome is represented as a largely uniform ecosystem with either a singular or a small number of fixed canopy ecophysiological properties3. This situation arises, in part, from a lack of understanding about how and why the functional properties of tropical forest canopies vary geographically4. Here, by combining field-collected data from more than 1,800 vegetation plots and tree traits with satellite remote-sensing, terrain, climate and soil data, we predict variation across 13 morphological, structural and chemical functional traits of trees, and use this to compute and map the functional diversity of tropical forests. Our findings reveal that the tropical Americas, Africa and Asia tend to occupy different portions of the total functional trait space available across tropical forests. Tropical American forests are predicted to have 40% greater functional richness than tropical African and Asian forests. Meanwhile, African forests have the highest functional divergence-32% and 7% higher than that of tropical American and Asian forests, respectively. An uncertainty analysis highlights priority regions for further data collection, which would refine and improve these maps. Our predictions represent a ground-based and remotely enabled global analysis of how and why the functional traits of tropical forest canopies vary across space.

On 29 January 2024, a truck accident spilled concentrated sulfonic acid into a river in Brazil's State of Santa Catarina. This disaster, which occurred in a protected area, killed various types of fish (e.g., crenuchids, heptapterids, and loricariids) and probably many other less observable animals, such as small invertebrates. The accident, similar to others around the world, gained national and international media attention. Because truck accidents causing aquatic pollution through chemical spills are occurring not just in Brazil but also globally, the recent event led us to consider similar cases and to recommend potential measures to help mitigate biodiversity loss in waterbodies near roads. Three factors need to be considered: (i) human (drivers); (ii) truck (mechanical condition); and (iii) road (e.g., infrastructure). In addition, improved expert assessments of the negative impact of these accidents on biodiversity are needed. A requirement to aid recovery of polluted ecosystems should be incumbent upon the companies involved in truck accidents.

Understanding how the traits of lineages are related to diversification is key for elucidating the origin of variation in species richness. Here, we test whether traits are related to species richness among lineages of trees from all major biogeographical settings of the lowland wet tropics. We explore whether variation in mortality rate, breeding system and maximum diameter are related to species richness, either directly or via associations with range size, among 463 genera that contain wet tropical forest trees. For Amazonian genera, we also explore whether traits are related to species richness via variation among genera in mean species-level range size. Lineages with higher mortality rates—faster life-history strategies—have larger ranges in all biogeographic settings and have higher mean species-level range sizes in Amazonia. These lineages also have smaller maximum diameters and, in the Americas, contain dioecious species. In turn, lineages with greater overall range size have higher species richness. Our results show that fast life-history strategies influence species richness in all biogeographic settings because lineages with these ecological strategies have greater range sizes. These links suggest that dispersal has been a key process in the evolution of the tropical forest flora.

Plants cope with the environment by displaying large phenotypic variation. Two spectra of global plant form and function have been identified: a size spectrum from small to tall species with increasing stem tissue density, leaf size, and seed mass; a leaf economics spectrum reflecting slow to fast returns on investments in leaf nutrients and carbon. When species assemble to communities it is assumed that these spectra are filtered by the environment to produce community level functional composition. It is unknown what are the main drivers for community functional composition in a large area such as Amazonia. We use 13 functional traits, including wood density, seed mass, leaf characteristics, breeding system, nectar production, fruit type, and root characteristics of 812 tree genera (5211 species), and find that they describe two main axes found at the global scale. At community level, the first axis captures not only the 'fast-slow spectrum', but also most size-related traits. Climate and disturbance explain a minor part of this variance compared to soil fertility. Forests on poor soils differ largely in terms of trait values from those on rich soils. Trait composition and soil fertility exert a strong influence on forest functioning: biomass and relative biomass production.

The island of New Guinea harbors some of the world's most biologically diverse and highly endemic tropical ecosystems. Nevertheless, progressing land-use change in the region threatens their integrity, which will adversely affect their biodiversity as well as carbon stocks and fluxes. Our objectives were to (1) compare deforestation drivers between Indonesian New Guinea and Papua New Guinea, (2) identify areas with a high risk of future deforestation under different development scenarios, and (3) evaluate the effects of potential deforestation scenarios on carbon pools. We integrated machine learning and cellular automata to model and forecast deforestation across New Guinea. We assessed the potential loss of irrecoverable carbon stocks for four deforestation scenarios ranging from 4.8 % (business-as-usual) to 28 % (high development scenario) forest loss between 2020 and 2040. Areas of high deforestation risk were consistently forecasted in lowland regions across the four deforestation scenarios. In Indonesian New Guinea, 75 % of deforestation was forecasted below ~380 m a.s.l., but ranged higher in Papua New Guinea (<750 m a.s.l.). Land change-induced carbon loss varied largely across the four scenarios and ranged between 156 and 918 Mt in Indonesian New Guinea and between 223 and 1082 Mt in Papua New Guinea, respectively. Our analysis reveals promising potential for integrating random forests and cellular automata models to forecast high-resolution deforestation over large spatial extents. Our models reveal the vulnerability of New Guinea's lowlands to future deforestation, emphasizing the need to protect key areas where deforestation conflicts with the conservation of carbon stocks, ecosystem functions, and biodiversity. ABSTRACT IN BAHASA INDONESIA: Pulau New Guinea merupakan rumah bagi beberapa ekosistem tropis yang paling beragam secara biologis dan sangat endemik di dunia. Namun demikian, perubahan penggunaan lahan yang terus terjadi di kawasan ini mengancam integritas kawasan tersebut, yang akan berdampak buruk terhadap keanekaragaman hayati serta persdiaan dan fluks karbon. Tujuan penelitian ini adalah (1) membandingkan penyebab deforestasi antara New Guinea dan Papua Nugini, (2) mengidentifikasi kawasan dengan risiko tinggi deforestasi di masa depan berdasarkan skenario pembangunan yang berbeda, dan (3) mengevaluasi dampak skenario deforestasi potensial terhadap sumber karbon. . Kami mengintegrasikan pembelajaran mesin dan automata seluler untuk memodelkan dan memperkirakan deforestasi di seluruh New Guinea. Kami menilai potensi hilangnya cadangan karbon yang tidak dapat dipulihkan untuk empat skenario deforestasi yang berkisar antara 4,8 % (skenario pembangunan biasa) hingga 28 % (skenario pembangunan tinggi) antara tahun 2020 dan 2040. Wilayah dengan risiko deforestasi tinggi secara konsisten diperkirakan berada di wilayah dataran rendah dalam empat skenario deforestasi. Di Papua Nugini, 75 % deforestasi diperkirakan berada di bawah ~380 m dpl, namun berkisar lebih tinggi di Papua Nugini (<750 m dpl). Hilangnya karbon yang disebabkan oleh perubahan lahan sangat bervariasi di keempat skenario dan berkisar antara 156 dan 918 Mt di Nugini, dan masing-masing antara 223 dan 1.082 Mt di Papua Nugini. Analisis kami mengungkapkan potensi yang menjanjikan untuk mengintegrasikan hutan acak dan model automata seluler untuk memperkirakan deforestasi resolusi tinggi pada wilayah spasial yang luas. Model kami mengungkap bahwa kerentanan dataran rendah New Guinea terhadap deforestasi di masa depan, menekankan perlunya melindungi wilayah-wilayah utama di mana deforestasi bertentangan dengan konservasi persediaan karbon, fungsi ekosistem, dan keanekaragaman hayati.

Climate change and increasing availability of resources such as carbon dioxide are modifying forest functioning worldwide, but the effects of these changes on forest structure are unclear. As additional resources become available, for example, through CO2 fertilization or nitrogen deposition, large trees, with greater access to light, may be expected to gain further advantages. Conversely, smaller light-suppressed trees might benefit more if their light compensation point changes, while bigger trees may be the most negatively impacted by increasing heat and drought. We assessed recent changes in the structure of Earth's largest tropical forest by analysing 30 years of Amazonian tree records across 188 mature forest plots. We find that, at a stand level, trees have become larger over time, with mean tree basal area increasing by 3.3% per decade (95% CI 2.4; 4.1). Larger trees have increased in both number and size, yet we observed similar rates of relative size gain in large and small trees. This evidence is consistent with a resource-driven boost for larger trees but also a reduction in suppression among smaller trees. These results, especially the persistence and consistency of tree size increases across Amazonian forest plots, communities and regions, indicate that any negative impacts of climate change on forests and large trees here have so far been mitigated by the positive effects of increased resources.

Recent evidence suggests that the ecological footprints of pre-Columbian Indigenous peoples in Amazonia persist in modern forests. Ecological impacts resulting from European colonization c. 1550 CE and the Amazonian Rubber Boom c. 1850 to 1920 CE are largely unexplored but could be important additive influences on forest structure and tree species composition. Using environmental niche models, we show the highest probabilities of pre-Columbian and colonial occupation sites, and hence human-induced ecological influences, occurred in forests along rivers. In many areas, the predicted pre-Columbian and colonial distributions overlap spatially with the potential for superimposed ecological influences. Environmental gradients are known to structure Amazonian vegetation composition, but they are also strong predictors of past human influence, both spatially and temporally. Our comparisons of model outputs with relative abundances of Amazonian tree species suggest that pre-Columbian and colonial-period ecological legacies are associated with modern forest composition.

Understanding the capacity of forests to adapt to climate change is of pivotal importance for conservation science, yet this is still widely unknown. This knowledge gap is particularly acute in high-biodiversity tropical forests. Here, we examined how tropical forests of the Americas have shifted community trait composition in recent decades as a response to changes in climate. Based on historical trait-climate relationships, we found that, overall, the studied functional traits show shifts of less than 8% of what would be expected given the observed changes in climate. However, the recruit assemblage shows shifts of 21% relative to climate change expectation. The most diverse forests on Earth are changing in functional trait composition but at a rate that is fundamentally insufficient to track climate change.

In the tropics and beyond, roads are key proximate drivers of environmental impacts, including forest fragmentation,1,2 fires,3 mining,4,5 and land clearing.6,7,8 Such impacts may be amplified for the initial roads constructed in intact forests-which we term "first-cut roads"-which often promote a rash of associated secondary roads branching off the new infrastructure.9,10,11,12,13 These secondary roads in turn can dramatically elevate forest and biodiversity losses.10,14,15 Although widely seen as a conservation concern,12,15,16,17 the magnitude and effects of secondary road development have not been previously quantified. Without such information, impact assessment procedures for road projects risk misjudging the level of expected forest loss, hampering decision-making.16,18,19,20 Here, we quantify the environmental impacts of both first-cut and secondary roads in three of the world's major tropical regions where high-quality road maps have recently become available: the Brazilian Amazon, the Congo Basin, and New Guinea. We identified 92 first-cut roads across our study region for which we quantified the length of adjoining secondary roads and the area of related forest loss and degradation. On average, we found 4.8, 9.8, and 49.1 km of secondary road for every kilometer of first-cut road in the Congo Basin, New Guinea, and Brazilian Amazon regions, respectively. Forest loss and degradation associated with these secondary roads was remarkably heavy, being 31.5, 22.2, and 305.2 times greater, respectively, than that directly linked with first-cut roads. Our findings provide key insights into the potential scale and extent of forest loss and degradation that will emerge with proposed roads and development corridors in tropical forests.