The effect of reef morphology on coral recruitment at multiple spatial scales

Coral reefs are in decline worldwide, making it increasingly important to promote coral recruitment in new or degraded habitat. Coral reef morphology-the structural form of reef substrate-affects many aspects of reef function, yet the effect of reef morphology on coral recruitment is not well understood. We used structure-from-motion photogrammetry and airborne remote sensing to measure reef morphology (rugosity, curvature, slope, and fractal dimension) across a broad continuum of spatial scales and evaluated the effect of morphology on coral recruitment in three broadcast-spawning genera. We also measured the effect of other environmental and biotic factors such as fish density, adult coral cover, hydrodynamic larval import, and depth on coral recruitment. All variables combined explained 72% of coral recruitment in the study region. Coarse reef rugosity and curvature mapped at ≥2 m spatial resolution-such as large colonies, knolls, and boulders-were positively correlated with coral recruitment, explaining 22% of variation in recruitment. Morphology mapped at finer scales (≤32 cm resolution) was not significant. Hydrodynamic larval import was also positively related to coral recruitment in Porites and Montipora spp., and grazer fish density was linked to significantly lower recruitment in all genera. In addition, grazer density, reef morphology, and hydrodynamic import had differential effects on coral genera, reflecting genus-specific life history traits, and model performance was lower in gonochoric species. Overall, coral reef morphology is a key indicator of recruitment potential that can be detected by remote sensing, allowing potential larval sinks to be identified and factored into restoration actions.

Coral reefs benefit from reduced land-sea impacts under ocean warming

Coral reef ecosystems are being fundamentally restructured by local human impacts and climate-driven marine heatwaves that trigger mass coral bleaching and mortality1. Reducing local impacts can increase reef resistance to and recovery from bleaching2. However, resource managers lack clear advice on targeted actions that best support coral reefs under climate change3 and sector-based governance means most land- and sea-based management efforts remain siloed4. Here we combine surveys of reef change with a unique 20-year time series of land-sea human impacts that encompassed an unprecedented marine heatwave in Hawai'i. Reefs with increased herbivorous fish populations and reduced land-based impacts, such as wastewater pollution and urban runoff, had positive coral cover trajectories predisturbance. These reefs also experienced a modest reduction in coral mortality following severe heat stress compared to reefs with reduced fish populations and enhanced land-based impacts. Scenario modelling indicated that simultaneously reducing land-sea human impacts results in a three- to sixfold greater probability of a reef having high reef-builder cover four years postdisturbance than if either occurred in isolation. International efforts to protect 30% of Earth's land and ocean ecosystems by 2030 are underway5. Our results reveal that integrated land-sea management could help achieve coastal ocean conservation goals and provide coral reefs with the best opportunity to persist in our changing climate.

Fish assemblage structure, diversity and controls on reefs of South Kona, Hawai'i Island

The structure of coral-reef fish assemblages is affected by natural and anthropogenic factors such as the architectural complexity, benthic composition and physical characteristics of the habitat, fishing pressure and land-based input. The coral-reef ecosystem of South Kona, Hawai'i hosts diverse reef habitats with a relatively high live coral cover, but a limited number of studies have focused on the ecosystem or the fish assemblages. Here, we surveyed fish assemblages at 119 sites in South Kona in 2020 and 2021 and investigated the associations between the fish assemblages and environmental variables obtained from published Geographic Information System (GIS) layers, including depth, latitude, reef rugosity, housing density and benthic cover. The fish assemblages in South Kona were dominated by a relatively small number of widely occurring species. Multivariate analyses indicated that fish assemblage structure strongly correlated with depth, reefscape-level rugosity and sand cover individually, while the final parsimonious model included latitude, depth, housing density within 3-km of shore, chlorophyll-a concentration and sand cover. Univariate analysis revealed negative associations between housing density and fish species richness and abundance. Effects of environmental factors specific to fish trophic groups were also found. Reefscape-level rugosity had strong positive influences on the distributions of all herbivores (browsers, grazers and scrapers), while housing density had strong negative influences only on the abundance of browsers. Positive associations were also found between live coral cover and the presence of scrapers, as well as the abundance of corallivorous fish. This study intensively surveyed shallow coral reefs along the coastline of South Kona and was the most complete spatial survey on the reef fish assemblages to date. As it utilized GIS layers to assess large-scale patterns in the fish assemblages, future studies including in-situ environmental data may further reveal local-scale patterns and insights into factors affecting the structure of fish assemblages in Hawai'i.

Leaf spectroscopy of resistance to Ceratocystis wilt of 'Ōhi'a

Plant pathogens are increasingly compromising forest health, with impacts to the ecological, economic, and cultural goods and services these global forests provide. One response to these threats is the identification of disease resistance in host trees, which with conventional methods can take years or even decades to achieve. Remote sensing methods have accelerated host resistance identification in agricultural crops and for a select few forest tree species, but applications are rare. Ceratocystis wilt of 'ōhi'a, caused by the fungal pathogen Ceratocystis lukuohia has been killing large numbers of the native Hawaiian tree, Metrosideros polymorpha or 'Ōhi'a, Hawaii's most common native tree and a biocultural keystone species. Here, we assessed whether resistance to C. lukuohia is detectable in leaf-level reflectance spectra (400-2500 nm) and used chemometric conversion equations to understand changes in leaf chemical traits of the plants as indicators of wilt symptom progression. We collected leaf reflectance data prior to artificially inoculating 2-3-year-old M. polymorpha clones with C. lukuohia. Plants were rated 3x a week for foliar wilt symptom development and leaf spectra data collected at 2 to 4-day intervals for 120 days following inoculation. We applied principal component analysis (PCA) to the pre-inoculation spectra, with plants grouped according to site of origin and subtaxon, and two-way analysis of variance to assess whether each principal component separated individuals based on their disease severity ratings. We identified seven leaf traits that changed in susceptible plants following inoculation (tannins, chlorophyll a+b, NSC, total C, leaf water, phenols, and cellulose) and leaf chemistries that differed between resistant and early-stage susceptible plants, most notably chlorophyll a+b and cellulose. Further, disease resistance was found to be detectable in the reflectance data, indicating that remote sensing work could expedite Ceratocystis wilt of 'ōhi'a resistance screenings.

Foliar functional and genetic variation in a keystone Hawaiian tree species estimated through spectroscopy

Imaging spectroscopy has the potential to map closely related plant taxa at landscape scales. Although spectral investigations at the leaf and canopy levels have revealed relationships between phylogeny and reflectance, understanding how spectra differ across, and are inherited from, genotypes of a single species has received less attention. We used a common-garden population of four varieties of the keystone canopy tree, Metrosideros polymorpha, from Hawaii Island and four F1-hybrid genotypes derived from controlled crosses to determine if reflectance spectra discriminate sympatric, conspecific varieties of this species and their hybrids. With a single exception, pairwise comparisons of leaf reflectance patterns successfully distinguished varieties of M. polymorpha on Hawaii Island as well as populations of the same variety from different islands. Further, spectral variability within a single variety from Hawaii Island and the older island of Oahu was greater than that observed among the four varieties on Hawaii Island. F1 hybrids most frequently displayed leaf spectral patterns intermediate to those of their parent taxa. Spectral reflectance patterns distinguished each of two of the hybrid genotypes from one of their parent varieties, indicating that classifying hybrids may be possible, particularly if sample sizes are increased. This work quantifies a baseline in spectral variability for an endemic Hawaiian tree species and advances the use of imaging spectroscopy in biodiversity studies at the genetic level.

The Spectral Species Concept in Living Color

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

Coral taxonomy and local stressors drive bleaching prevalence across the Hawaiian Archipelago in 2019

The Hawaiian Archipelago experienced a moderate bleaching event in 2019—the third major bleaching event over a 6-year period to impact the islands. In response, the Hawai‘i Coral Bleaching Collaborative (HCBC) conducted 2,177 coral bleaching surveys across the Hawaiian Archipelago. The HCBC was established to coordinate bleaching monitoring efforts across the state between academic institutions, non-governmental organizations, and governmental agencies to facilitate data sharing and provide management recommendations. In 2019, the goals of this unique partnership were to: 1) assess the spatial and temporal patterns of thermal stress; 2) examine taxa-level patterns in bleaching susceptibility; 3) quantify spatial variation in bleaching extent; 4) compare 2019 patterns to those of prior bleaching events; 5) identify predictors of bleaching in 2019; and 6) explore site-specific management strategies to mitigate future bleaching events. Both acute thermal stress and bleaching in 2019 were less severe overall compared to the last major marine heatwave events in 2014 and 2015. Bleaching observed was highly site- and taxon-specific, driven by the susceptibility of remaining coral assemblages whose structure was likely shaped by previous bleaching and subsequent mortality. A suite of environmental and anthropogenic predictors was significantly correlated with observed bleaching in 2019. Acute environmental stressors, such as temperature and surface light, were equally important as previous conditions (e.g. historical thermal stress and historical bleaching) in accounting for variation in bleaching during the 2019 event. We found little evidence for acclimation by reefs to thermal stress in the main Hawaiian Islands. Moreover, our findings illustrate how detrimental effects of local anthropogenic stressors, such as tourism and urban run-off, may be exacerbated under high thermal stress. In light of the forecasted increase in severity and frequency of bleaching events, future mitigation of both local and global stressors is a high priority for the future of corals in Hawai‘i.

Species-level tree crown maps improve predictions of tree recruit abundance in a tropical landscape

Predicting forest recovery at landscape scales will aid forest restoration efforts. The first step in successful forest recovery is tree recruitment. Forecasts of tree recruit abundance, derived from the landscape-scale distribution of seed sources (i.e., adult trees), could assist efforts to identify sites with high potential for natural regeneration. However, previous work revealed wide variation in the effect of seed sources on seedling abundance, from positive to no effect. We quantified the relationship between adult tree seed sources and tree recruits and predicted where natural recruitment would occur in a fragmented, tropical, agricultural landscape. We integrated species-specific tree crown maps generated from hyperspectral imagery and property ownership data with field data on the spatial distribution of tree recruits from five species. We then developed hierarchical Bayesian models to predict landscape-scale recruit abundance. Our models revealed that species-specific maps of tree crowns improved recruit abundance predictions. Conspecific crown area had a much stronger impact on recruitment abundance (8.00% increase in recruit abundance when conspecific tree density increases from zero to one tree; 95% credible interval (CI): 0.80% to 11.57%) than heterospecific crown area (0.03% increase with the addition of a single heterospecific tree, 95% CI: -0.60% to 0.68%). Individual property ownership was also an important predictor of recruit abundance: The best performing model had varying effects of conspecific and heterospecific crown area on recruit abundance, depending on individual property ownership. We demonstrate how novel remote sensing approaches and cadastral data can be used to generate high-resolution and landscape-level maps of tree recruit abundance. Spatial models parameterized with field, cadastral, and remote sensing data are poised to assist decision support for forest landscape restoration.

Early detection of a tree pathogen using airborne remote sensing

Native forests of Hawai'i Island are experiencing an ecological crisis in the form of Rapid 'Ōhi'a Death (ROD), a recently characterized disease caused by two fungal pathogens in the genus Ceratocystis. Since approximately 2010, this disease has caused extensive mortality of Hawai'i's keystone endemic tree, known as 'ōhi'a (Metrosideros polymorpha). Visible symptoms of ROD include rapid browning of canopy leaves, followed by death of the tree within weeks. This quick progression leading to tree mortality makes early detection critical to understanding where the disease will move at a timescale feasible for controlling the disease. We used repeat laser-guided imaging spectroscopy (LGIS) of forests on Hawai'i Island collected by the Global Airborne Observatory (GAO) in 2018 and 2019 to derive maps of foliar trait indices previously found to be important in distinguishing between ROD-infected and healthy 'ōhi'a canopies. Data from these maps were used to develop a prognostic indicator of tree stress prior to the visible onset of browning. We identified canopies that were green in 2018, but became brown in 2019 (defined as "to become brown"; TBB), and a corresponding set of canopies that remained green. The data mapped in 2018 showed separability of foliar trait indices between TBB and green 'ōhi'a, indicating early detection of canopy stress prior to the onset of ROD. Overall, a combination of linear and non-linear analyses revealed canopy water content (CWC), foliar tannins, leaf mass per area (LMA), phenols, cellulose, and non-structural carbohydrates (NSC) are primary drivers of the prognostic spectral capability which collectively result in strong consistent changes in leaf spectral reflectance in the near-infrared (700-1300 nm) and shortwave-infrared regions (1300-2500 nm). Results provide insight into the underlying foliar traits that are indicative of physiological responses of M. polymorpha trees infected with Ceratocycstis and suggest that imaging spectroscopy is an effective tool for identifying trees likely to succumb to ROD prior to the onset of visible symptoms.

Empirically validated drought vulnerability mapping in the mixed conifer forests of the Sierra Nevada

Severe droughts are predicted to become more frequent in the future, and the consequences of such droughts on forests can be dramatic, resulting in massive tree mortality, rapid change in forest structure and composition, and substantially increased risk of catastrophic fire. Forest managers have tools at their disposal to try to mitigate these effects but are often faced with limited resources, forcing them to make choices about which parts of the landscape to target for treatment. Such planning can greatly benefit from landscape vulnerability assessments, but many existing vulnerability analyses are unvalidated and not grounded in robust empirical datasets. We combined robust sets of ground-based plot and remote sensing data, collected during the 2012-2016 California drought, to develop rigorously validated tools for assessing forest vulnerability to drought-related canopy tree mortality for the mixed conifer forests of the Sequoia and Kings Canyon national parks and potentially for mixed conifer forests in the Sierra Nevada as a whole. Validation was carried out using a large external dataset. The best models included normalized difference vegetation index (NDVI), elevation, and species identity. Models indicated that tree survival probability decreased with greenness (as measured by NDVI) and elevation, particularly if trees were growing slowly. Overall, models showed good calibration and validation, especially for Abies concolor, which comprise a large majority of the trees in many mixed conifer forests in the Sierra Nevada. Our models tended to overestimate mortality risk for Calocedrus decurrens and underestimate risk for pine species, in the latter case probably due to pine bark beetle outbreak dynamics. Validation results indicated dangers of overfitting, as well as showing that the inclusion of trees already under attack by bark beetles at the time of sampling can give false confidence in model strength, while also biasing predictions. These vulnerability tools should be useful to forest managers trying to assess which parts of their landscape were vulnerable during the 2012-2016 drought, and, with additional validation, may prove useful for ongoing assessments and predictions of future forest vulnerability.

Optimizing invasive species management using mathematical programming to support stewardship of water and carbon-based ecosystem services

Invasive species alter hydrologic processes at watershed scales, with impacts to biodiversity and the supporting ecosystem services. This effect is aggravated by climate change. Here, we integrated modelled hydrologic data, remote sensing products, climate data, and linear mixed integer optimization (MIP) to identify stewardship actions across space and time that can reduce the impact of invasive species. The study area is the windward coast of Hawai'i Island (USA) across which non-native strawberry guava occurrence varies from extremely dense stands in lower watershed reaches, to low densities in upper watershed forests. We focused on the removal of strawberry guava, an invader that exerts significant impacts on watershed condition. MIP analyses spatially optimized the assignment of effective management actions to increase water yield, generate revenue from enhanced freshwater services, and income from removed biomass. The hydrological benefit of removing guava, often marginal when considered in isolation, was financially quantified, and single- and multiobjective MIP formulations were then developed over a 10-year planning horizon. Optimization resulted in $2.27 million USD benefit over the planning horizon using a payment-for-ecosystem-services scheme. That value jumped to $4.67 million when allowing work schedules with overnight camping to reduce costs. Pareto frontiers of weighted pairs of management goals showed the benefit of clustering treatments over space and time to improve financial efficiency. Values of improved land-water natural capital using payment-for-ecosystem-services schemes are provided for several combinations of spatial, temporal, economical, and ecosystem services flows.

Mapping the vulnerability of giant sequoias after extreme drought in California using remote sensing

Between 2012 and 2016, California suffered one of the most severe droughts on record. During this period Sequoiadendron giganteum (giant sequoias) in the Sequoia and Kings Canyon National Parks (SEKI), California, USA experienced canopy water content (CWC) loss, unprecedented foliage senescence, and, in a few cases, death. We present an assessment of the vulnerability of giant sequoia populations to droughts that is currently lacking and needed for management. We used a temporal trend of remotely sensed CWC obtained between 2015 and 2017, and recently georeferenced giant sequoia crowns to quantify the vulnerability of 7,408 individuals in 10 groves in the northern portion of SEKI. CWC is sensitive to changes in liquid water in tree canopies; therefore, it is a useful metric for quantifying the response of sequoia trees to drought. Temporal trends indicated that 9% of giant sequoias had a significant decline or consistently low CWC, suggesting these trees were likely operating at low photosynthetic capacity and potentially at high risk to drought stress. We also found that 20% of the giant sequoias had an increase or consistently high level of CWC, indicating these trees were at low risk to drought stress. These vulnerability categories were used in a random forest model with a combination of topographic, fire-related, and climate variables to generate high-resolution vulnerability risk maps. These maps show that higher risk is associated with lower elevation and higher climate water deficit. We also found that sequoias at higher elevations but located near meadows had higher vulnerability risk. These results and the vulnerability maps can identify vulnerable sequoias that may be difficult to save or locations of refugia to be protected, and thus may aid forest managers in preparation for future droughts.

Amazon tree dominance across forest strata

The forests of Amazonia are among the most biodiverse plant communities on Earth. Given the immediate threats posed by climate and land-use change, an improved understanding of how this extraordinary biodiversity is spatially organized is urgently required to develop effective conservation strategies. Most Amazonian tree species are extremely rare but a few are common across the region. Indeed, just 227 'hyperdominant' species account for >50% of all individuals >10 cm diameter at 1.3 m in height. Yet, the degree to which the phenomenon of hyperdominance is sensitive to tree size, the extent to which the composition of dominant species changes with size class and how evolutionary history constrains tree hyperdominance, all remain unknown. Here, we use a large floristic dataset to show that, while hyperdominance is a universal phenomenon across forest strata, different species dominate the forest understory, midstory and canopy. We further find that, although species belonging to a range of phylogenetically dispersed lineages have become hyperdominant in small size classes, hyperdominants in large size classes are restricted to a few lineages. Our results demonstrate that it is essential to consider all forest strata to understand regional patterns of dominance and composition in Amazonia. More generally, through the lens of 654 hyperdominant species, we outline a tractable pathway for understanding the functioning of half of Amazonian forests across vertical strata and geographical locations.

Recovery of logged forest fragments in a human-modified tropical landscape during the 2015-16 El Niño

The past 40 years in Southeast Asia have seen about 50% of lowland rainforests converted to oil palm and other plantations, and much of the remaining forest heavily logged. Little is known about how fragmentation influences recovery and whether climate change will hamper restoration. Here, we use repeat airborne LiDAR surveys spanning the hot and dry 2015-16 El Niño Southern Oscillation event to measure canopy height growth across 3,300 ha of regenerating tropical forests spanning a logging intensity gradient in Malaysian Borneo. We show that the drought led to increased leaf shedding and branch fall. Short forest, regenerating after heavy logging, continued to grow despite higher evaporative demand, except when it was located close to oil palm plantations. Edge effects from the plantations extended over 300 metres into the forests. Forest growth on hilltops and slopes was particularly impacted by the combination of fragmentation and drought, but even riparian forests located within 40 m of oil palm plantations lost canopy height during the drought. Our results suggest that small patches of logged forest within plantation landscapes will be slow to recover, particularly as ENSO events are becoming more frequent.

It is unclear whether tropical forest fragments within plantation landscapes are resilient to drought. Here the authors analyse LiDAR and ground-based data from the 2015-16 El Niño event across a logging intensity gradient in Borneo. Although regenerating forests continued to grow, canopy height near oil palm plantations decreased, and a strong edge effect extended up to at least 300 m away.

Spatial heterogeneity facilitates carnivore coexistence

Competitively dominant carnivore species can limit the population sizes and alter the behavior of inferior competitors. Established mechanisms that enable carnivore coexistence include spatial and temporal avoidance of dominant predator species by subordinates, and dietary niche separation. However, spatial heterogeneity across landscapes could provide inferior competitors with refuges in the form of areas with lower competitor density and/or locations that provide concealment from competitors. Here, we combine temporally overlapping telemetry data from dominant lions (Panthera leo) and subordinate African wild dogs (Lycaon pictus) with high-resolution remote sensing in an integrated step selection analysis to investigate how fine-scaled landscape heterogeneity might facilitate carnivore coexistence in South Africa's Hluhluwe-iMfolozi Park, where both predators occur at exceptionally high densities. We ask whether the primary lion-avoidance strategy of wild dogs is spatial avoidance of lions or areas frequented by lions, or if wild dogs selectively use landscape features to avoid detection by lions. Within this framework, we also test whether wild dogs rely on proactive or reactive responses to lion risk. In contrast to previous studies finding strong spatial avoidance of lions by wild dogs, we found that the primary wild dog lion-avoidance strategy was to select landscape features that aid in avoidance of lion detection. This habitat selection was routinely used by wild dogs, and especially when in areas and during times of high lion-encounter risk, suggesting a proactive response to lion risk. Our findings suggest that spatial landscape heterogeneity could represent an alternative mechanism for carnivore coexistence, especially as ever-shrinking carnivore ranges force inferior competitors into increased contact with dominant species.

Advancing Landscape and Seascape Ecology from a 2D to a 3D Science

Landscape ecology has fundamentally changed the way ecologists view the world through a greater understanding of the links between spatial patterns and ecological processes. Until recently, landscape ecology has been largely a two-dimensional (2D) science focused on the spatial patterning of 2D planar surfaces rather than three-dimensional (3D) structures. Advances in high-resolution remote sensing technologies, such as laser altimetry, acoustic sensors, and photogrammetry now provide the capability to map complex ecosystem structure in three dimensions, creating more structurally realistic models of the environment. In the present article, we focus on high-resolution 3D structure, using terrestrial and marine examples to illustrate how state-of-the-art advances in landscape ecology achieved through novel data fusion, spatial analysis, and geovisualization of environmental data can provide new ecological insights. These examples provide a look to the future in landscape and seascape ecology, where continued progress toward a multidimensional science will fundamentally shift the way we view, explore, and conceptualize the world.

Surface slicks are pelagic nurseries for diverse ocean fauna

Most marine animals have a pelagic larval phase that develops in the coastal or open ocean. The fate of larvae has profound effects on replenishment of marine populations that are critical for human and ecosystem health. Larval ecology is expected to be tightly coupled to oceanic features, but for most taxa we know little about the interactions between larvae and the pelagic environment. Here, we provide evidence that surface slicks, a common coastal convergence feature, provide nursery habitat for diverse marine larvae, including > 100 species of commercially and ecologically important fishes. The vast majority of invertebrate and larval fish taxa sampled had mean densities 2-110 times higher in slicks than in ambient water. Combining in-situ surveys with remote sensing, we estimate that slicks contain 39% of neustonic larval fishes, 26% of surface-dwelling zooplankton (prey), and 75% of floating organic debris (shelter) in our 1000 km² study area in Hawai'i. Results indicate late-larval fishes actively select slick habitats to capitalize on concentrations of diverse prey and shelter. By providing these survival advantages, surface slicks enhance larval supply and replenishment of adult populations from coral reef, epipelagic, and deep-water ecosystems. Our findings suggest that slicks play a critically important role in enhancing productivity in tropical marine ecosystems.

Impacts of pollution, fishing pressure, and reef rugosity on resource fish biomass in West Hawaii

Human activities and land-use drivers combine in complex ways to affect coral reef health and, in turn, the diversity and abundance of reef fauna. Here we examine the impacts of different marine protected area (MPA) types, and various human and habitat drivers, on resource fish functional groups (i.e., total fish, herbivore, grazer, scraper, and browser biomass) along the 180 km west coast of Hawaii Island. Across survey years from 2008 to 2018, we observed an overall decrease in total fish biomass of 45%, with similar decreases in biomass seen across most fish functional groups. MPAs that prohibited a combination of lay nets, aquarium collection, and spear fishing were most effective in maintaining and/or increasing fish biomass across all functional groups. We also found that pollution, fishing, and habitat drivers all contributed to changes in total fish biomass, where the most negative impact was nitrogen input from land-based sewage disposal. Fish biomass relationships with our study drivers depended on fish functional grouping. For surgeonfish (grazers), changes in biomass linked most strongly to changes in reef rugosity. For parrotfish (scrapers), biomass was better explained by changes in commercial catch where current commercial fishing levels are negatively affecting scraper populations. Our observations suggest that regional management of multiple factors, including habitat, pollution, and fisheries, will benefit resource fish biomass off Hawaii Island.

Monitoring tropical forest succession at landscape scales despite uncertainty in Landsat time series

Forecasting rates of forest succession at landscape scales will aid global efforts to restore tree cover to millions of hectares of degraded land. While optical satellite remote sensing can detect regional land cover change, quantifying forest structural change is challenging. We developed a state-space modeling framework that applies Landsat satellite data to estimate variability in rates of natural regeneration between sites in a tropical landscape. Our models work by disentangling measurement error in Landsat-derived spectral reflectance from process error related to successional variability. We applied our modeling framework to rank rates of forest succession between 10 naturally regenerating sites in Southwestern Panama from about 2001 to 2015 and tested how different models for measurement error impacted forecast accuracy, ecological inference, and rankings of successional rates between sites. We achieved the greatest increase in forecasting accuracy by adding intra-annual phenological variation to a model based on Landsat-derived normalized difference vegetation index (NDVI). The best-performing model accounted for inter- and intra-annual noise in spectral reflectance and translated NDVI to canopy height via Landsat-lidar fusion. Modeling forest succession as a function of canopy height rather than NDVI also resulted in more realistic estimates of forest state during early succession, including greater confidence in rank order of successional rates between sites. These results establish the viability of state-space models to quantify ecological dynamics from time series of space-borne imagery. State-space models also provide a statistical approach well-suited to fusing high-resolution data, such as airborne lidar, with lower-resolution data that provides better temporal and spatial coverage, such as the Landsat satellite record. Monitoring forest succession using satellite imagery could play a key role in achieving global restoration targets, including identifying sites that will regain tree cover with minimal intervention.

Near-real time aboveground carbon emissions in Peru

Monitoring aboveground carbon stocks and fluxes from tropical deforestation and forest degradation is important for mitigating climate change and improving forest management. However, high temporal and spatial resolution analyses are rare. This study presents the most detailed tracking of aboveground carbon over time, with yearly, quarterly and monthly estimations of emissions using the stock-difference approach and masked by the forest loss layer of Global Forest Watch. We generated high spatial resolution (1-ha) monitoring of aboveground carbon density (ACD) and emissions (ACE) in Peru by incorporating hundreds of thousands of Planet Dove satellite images, Sentinel-1 radar, topography and airborne LiDAR, embedded into a deep learning regression workflow using high-performance computing. Consistent ACD results were obtained for all quarters and months analyzed, with R² values of 0.75–0.78, and root mean square errors (RMSE) between 20.6 and 22.0 Mg C ha^-1. A total of 7.138 Pg C was estimated for Peru with annual ACE of 20.08 Tg C between the third quarters of 2017 and 2018, respectively, or 23.4% higher than estimates from the FAO Global Forest Resources Assessment. Analyzed quarterly, the spatial evolution of ACE revealed 11.5 Tg C, 6.6 Tg C, 8.6 Tg C, and 10.1 Tg C lost between the third quarters of 2017 and 2018. Moreover, our monthly analysis for the dry season reveals the evolution of ACE at unprecedented temporal detail. We discuss environmental controls over ACE and provide a spatially explicit tool for enhanced forest carbon management at scale.

Leaf reflectance spectra capture the evolutionary history of seed plants

Leaf reflectance spectra have been increasingly used to assess plant diversity. However, we do not yet understand how spectra vary across the tree of life or how the evolution of leaf traits affects the differentiation of spectra among species and lineages. Here we describe a framework that integrates spectra with phylogenies and apply it to a global dataset of over 16 000 leaf-level spectra (400-2400 nm) for 544 seed plant species. We test for phylogenetic signal in spectra, evaluate their ability to classify lineages, and characterize their evolutionary dynamics. We show that phylogenetic signal is present in leaf spectra but that the spectral regions most strongly associated with the phylogeny vary among lineages. Despite among-lineage heterogeneity, broad plant groups, orders, and families can be identified from reflectance spectra. Evolutionary models also reveal that different spectral regions evolve at different rates and under different constraint levels, mirroring the evolution of their underlying traits. Leaf spectra capture the phylogenetic history of seed plants and the evolutionary dynamics of leaf chemistry and structure. Consequently, spectra have the potential to provide breakthrough assessments of leaf evolution and plant phylogenetic diversity at global scales.

Quantifying Tropical Plant Diversity Requires an Integrated Technological Approach

Tropical biomes are the most diverse plant communities on Earth, and quantifying this diversity at large spatial scales is vital for many purposes. As macroecological approaches proliferate, the taxonomic uncertainties in species occurrence data are easily neglected and can lead to spurious findings in downstream analyses. Here, we argue that technological approaches offer potential solutions, but there is no single silver bullet to resolve uncertainty in plant biodiversity quantification. Instead, we propose the use of artificial intelligence (AI) approaches to build a data-driven framework that integrates several data sources - including spectroscopy, DNA sequences, image recognition, and morphological data. Such a framework would provide a foundation for improving species identification in macroecological analyses while simultaneously improving the taxonomic process of species delimitation.

A "Global Safety Net" to reverse biodiversity loss and stabilize Earth's climate

Global strategies to halt the dual crises of biodiversity loss and climate change are often formulated separately, even though they are interdependent and risk failure if pursued in isolation. The Global Safety Net maps how expanded nature conservation addresses both overarching threats. We identify 50% of the terrestrial realm that, if conserved, would reverse further biodiversity loss, prevent CO₂ emissions from land conversion, and enhance natural carbon removal. This framework shows that, beyond the 15.1% land area currently protected, 35.3% of land area is needed to conserve additional sites of particular importance for biodiversity and stabilize the climate. Fifty ecoregions and 20 countries contribute disproportionately to proposed targets. Indigenous lands overlap extensively with the Global Safety Net. Conserving the Global Safety Net could support public health by reducing the potential for zoonotic diseases like COVID-19 from emerging in the future.

Active restoration accelerates the carbon recovery of human-modified tropical forests

More than half of all tropical forests are degraded by human impacts, leaving them threatened with conversion to agricultural plantations and risking substantial biodiversity and carbon losses. Restoration could accelerate recovery of aboveground carbon density (ACD), but adoption of restoration is constrained by cost and uncertainties over effectiveness. We report a long-term comparison of ACD recovery rates between naturally regenerating and actively restored logged tropical forests. Restoration enhanced decadal ACD recovery by more than 50%, from 2.9 to 4.4 megagrams per hectare per year. This magnitude of response, coupled with modal values of restoration costs globally, would require higher carbon prices to justify investment in restoration. However, carbon prices required to fulfill the 2016 Paris climate agreement [$40 to $80 (USD) per tonne carbon dioxide equivalent] would provide an economic justification for tropical forest restoration.

Incorporating connectivity into conservation planning for the optimal representation of multiple species and ecosystem services

Conservation planning tends to focus on protecting species' ranges or landscape connectivity but seldom both-particularly in the case of diverse taxonomic assemblages and multiple planning goals. Therefore, information on potential trade-offs between maintaining landscape connectivity and achieving other conservation objectives is lacking. We developed an optimization approach to prioritize the maximal protection of species' ranges, ecosystem types, and forest carbon stocks, while also including habitat connectivity for range-shifting species and dispersal corridors to link protected area. We applied our approach to Sabah, Malaysia, where the state government mandated an increase in protected-area coverage of approximately 305,000 ha but did not specify where new protected areas should be. Compared with a conservation planning approach that did not incorporate the 2 connectivity features, our approach increased the protection of dispersal corridors and elevational connectivity by 13% and 21%, respectively. Coverage of vertebrate and plant species' ranges and forest types were the same whether connectivity was included or excluded. Our approach protected 2% less forest carbon and 3% less butterfly range than when connectivity features were not included. Hence, the inclusion of connectivity into conservation planning can generate large increases in the protection of landscape connectivity with minimal loss of representation of other conservation targets.

Geomorphic transience moderates topographic controls on tropical canopy foliar traits

Tropical ecosystems that exist on mountainous terrain harbour enormous species and functional diversity. In addition, the morphology of these complex landscapes is dynamic. Stream channels respond to mountain uplift by eroding into rising rock bodies. Many local factors determine whether channels are actively downcutting, in relative steady-state, or aggrading. It is possible to assess the trajectory of catchment-level landscape evolution utilising lidar-based models, but the effect of these trajectories on biogeochemical gradients and organisation of canopy traits across climatic and geochemical conditions remain uncertain. We use canopy trait maps to assess how variable erosion rate within catchments influence hillslope controls on canopy traits across Mt. Kinabalu, Borneo. While foliar nutrient content generally increased along hillslopes, these relationships were moderated by catchment responses to changing erosion pressure, with active downcutting associated with greater turnover in canopy traits along hillslopes. These results provide an understanding of geomorphic process controls on forest functional diversity.

Covariance of Sun and Shade Leaf Traits Along a Tropical Forest Elevation Gradient

Foliar trait adaptation to sun and shade has been extensively studied in the context of photosynthetic performance of plants, focusing on nitrogen allocation, light capture and use via chlorophyll pigments and leaf morphology; however, less is known about the potential sun-shade dichotomy of other functionally important foliar traits. In this study, we measured 19 traits in paired sun and shade leaves along a 3,500-m elevation gradient in southern Peru to test whether the traits differ with canopy position, and to assess if relative differences vary with species composition and/or environmental filters. We found significant sun-shade differences in leaf mass per area (LMA), photosynthetic pigments (Chl ab and Car), and δ¹³C. Sun-shade offsets among these traits remained constant with elevation, soil substrates, and species compositional changes. However, other foliar traits related to structure and chemical defense, and those defining general metabolic processes, did not differ with canopy position. Our results suggest that whole-canopy function is captured in many traits of sun leaves; however, photosynthesis-related traits must be scaled based on canopy light extinction. These findings show that top-of-canopy measurements of foliar chemistry from spectral remote sensing approaches map directly to whole-canopy foliar traits including shaded leaves that cannot be directly observed from above.

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Informing trait-based ecology by assessing remotely sensed functional diversity across a broad tropical temperature gradient

Spatially continuous data on functional diversity will improve our ability to predict global change impacts on ecosystem properties. We applied methods that combine imaging spectroscopy and foliar traits to estimate remotely sensed functional diversity in tropical forests across an Amazon-to-Andes elevation gradient (215 to 3537 m). We evaluated the scale dependency of community assembly processes and examined whether tropical forest productivity could be predicted by remotely sensed functional diversity. Functional richness of the community decreased with increasing elevation. Scale-dependent signals of trait convergence, consistent with environmental filtering, play an important role in explaining the range of trait variation within each site and along elevation. Single- and multitrait remotely sensed measures of functional diversity were important predictors of variation in rates of net and gross primary productivity. Our findings highlight the potential of remotely sensed functional diversity to inform trait-based ecology and trait diversity-ecosystem function linkages in hyperdiverse tropical forests.

Monitoring tropical forest carbon stocks and emissions using Planet satellite data

Tropical forests are crucial for mitigating climate change, but many forests continue to be driven from carbon sinks to sources through human activities. To support more sustainable forest uses, we need to measure and monitor carbon stocks and emissions at high spatial and temporal resolution. We developed the first large-scale very high-resolution map of aboveground carbon stocks and emissions for the country of Peru by combining 6.7 million hectares of airborne LiDAR measurements of top-of-canopy height with thousands of Planet Dove satellite images into a random forest machine learning regression workflow, obtaining an R² of 0.70 and RMSE of 25.38 Mg C ha⁻¹ for the nationwide estimation of aboveground carbon density (ACD). The diverse ecosystems of Peru harbor 6.928 Pg C, of which only 2.9 Pg C are found in protected areas or their buffers. We found significant carbon emissions between 2012 and 2017 in areas aggressively affected by oil palm and cacao plantations, agricultural and urban expansions or illegal gold mining. Creating such a cost-effective and spatially explicit indicators of aboveground carbon stocks and emissions for tropical countries will serve as a transformative tool to quantify the climate change mitigation services that forests provide.

Uncovering Ecological Patterns with Convolutional Neural Networks

Using remotely sensed imagery to identify biophysical components across landscapes is an important avenue of investigation for ecologists studying ecosystem dynamics. With high-resolution remotely sensed imagery, algorithmic utilization of image context is crucial for accurate identification of biophysical components at large scales. In recent years, convolutional neural networks (CNNs) have become ubiquitous in image processing, and are rapidly becoming more common in ecology. Because the quantity of high-resolution remotely sensed imagery continues to rise, CNNs are increasingly essential tools for large-scale ecosystem analysis. We discuss here the conceptual advantages of CNNs, demonstrate how they can be used by ecologists through distinct examples of their application, and provide a walkthrough of how to use them for ecological applications.

Elephants limit aboveground carbon gains in African savannas

Understanding the drivers of vegetation carbon dynamics is essential for climate change mitigation and effective policy formulation. However, most efforts focus on abiotic drivers of plant biomass change, with little consideration for functional roles performed by animals, particularly at landscape scales. We combined repeat airborne Light Detection and Ranging with measurements of elephant densities, abiotic factors, and exclusion experiments to determine the relative importance of drivers of change in aboveground woody vegetation carbon stocks in Kruger National Park, South Africa. Despite a growing elephant population, aboveground carbon density (ACD) increased across most of the landscape over the 6-year study period, but at fine scales, bull elephant density was the most important factor determining carbon stock change, with ACD losses recorded only where bull densities exceeded 0.5 bulls/km² . Effects of bull elephants were, however, spatially restricted and landscape dependent, being especially pronounced along rivers, at mid-elevations, and on steeper slopes. In contrast, elephant herds and abiotic drivers had a comparatively small influence on the direction or magnitude of carbon stock change. Our findings demonstrate that animals can have a substantive influence on regional-scale carbon dynamics and warrant consideration in carbon cycling models and policy formulation aimed at carbon management and climate change mitigation.

Dominant tree species drive beta diversity patterns in western Amazonia

The forests of western Amazonia are among the most diverse tree communities on Earth, yet this exceptional diversity is distributed highly unevenly within and among communities. In particular, a small number of dominant species account for the majority of individuals, whereas the large majority of species are locally and regionally extremely scarce. By definition, dominant species contribute little to local species richness (alpha diversity), yet the importance of dominant species in structuring patterns of spatial floristic turnover (beta diversity) has not been investigated. Here, using a network of 207 forest inventory plots, we explore the role of dominant species in determining regional patterns of beta diversity (community-level floristic turnover and distance-decay relationships) across a range of habitat types in northern lowland Peru. Of the 2,031 recorded species in our data set, only 99 of them accounted for 50% of individuals. Using these 99 species, it was possible to reconstruct the overall features of regional beta diversity patterns, including the location and dispersion of habitat types in multivariate space, and distance-decay relationships. In fact, our analysis demonstrated that regional patterns of beta diversity were better maintained by the 99 dominant species than by the 1,932 others, whether quantified using species-abundance data or species presence-absence data. Our results reveal that dominant species are normally common only in a single forest type. Therefore, dominant species play a key role in structuring western Amazonian tree communities, which in turn has important implications, both practically for designing effective protected areas, and more generally for understanding the determinants of beta diversity patterns.

Tropical forest leaves may darken in response to climate change

Tropical forest leaf albedo (reflectance) greatly impacts how much energy the planet absorbs; however; little is known about how it might be impacted by climate change. Here, we measure leaf traits and leaf albedo at ten 1-ha plots along a 3,200-m elevation gradient in Peru. Leaf mass per area (LMA) decreased with warmer temperatures along the elevation gradient; the distribution of LMA was positively skewed at all sites indicating a shift in LMA towards a warmer climate and future reduced tropical LMA. Reduced LMA was significantly (P < 0.0001) correlated with reduced leaf near-infrared (NIR) albedo; community-weighted mean NIR albedo significantly (P < 0.01) decreased as temperature increased. A potential future 2 °C increase in tropical temperatures could reduce lowland tropical leaf LMA by 6-7 g m^-2 (5-6%) and reduce leaf NIR albedo by 0.0015-0.002 units. Reduced NIR albedo means that leaves are darker and absorb more of the Sun's energy. Climate simulations indicate this increased absorbed energy will warm tropical forests more at high CO₂ conditions with proportionately more energy going towards heating and less towards evapotranspiration and cloud formation.

Landscape evolution and nutrient rejuvenation reflected in Amazon forest canopy chemistry

Terra firme forests make up more than three quarters of the western Amazon basin and are often considered functionally homogeneous in regional scale mapping and modelling efforts. However, the landforms underlying these systems are subject to dynamic processes of landscape evolution occurring within an otherwise geomorphically stable terrace formation. These processes may introduce systematic variability in local nutrient status of terra firme ecosystems. We utilised high-resolution airborne topographic and imaging spectroscopy data, with directed field soil surveys, to reveal that active stream incision and patterns of soil rock derived nutrient availability drive foliar canopy chemistry distributions across seven catchments within a single terrace formation. These results strongly suggest that fine-scale geomorphic processes directly affect biogeochemical cycles throughout the lowland western Amazon. Furthermore, links between landscape evolution and foliar chemical distributions indicate that geomorphic processes drive the fine-scale spatial organisation of this tropical ecosystem, with implications for the functional assembly and biogeography of Amazonian forests.

On the relationship between fire regime and vegetation structure in the tropics

We assessed data from 11 experiments examining the effects of the timing and/or frequency of fire on tropical forest and/or savanna vegetation structure over one decade or more. The initial 'control treatment' in many such cases consisted of previously cleared land. This is as opposed to natural vegetation subject to some sort of endogenous fire regime before the imposition of fire treatments. Effects of fire on fractional foliar cover are up to 10-fold greater when clearing pre-treatments are imposed. Moreover, because many of the 'classic' fire trials were initialised with applied management questions in mind, most have also used burning regimes much more frequent and/or severe than those occurring in the absence of human activity. Once these factors are taken into account, our modelling analysis shows that nonanthropogenic fire regimes serve to reduce canopy vegetative cover to a much lower extent than has previously been argued to be the case. These results call into question the notion that fire effects on tropical vegetation can be of a sufficient magnitude to maintain open-type savanna ecosystems under climatic/soil regimes otherwise sufficient to give rise to a more luxurious forest-type vegetation cover.

Megafaunal effects on vegetation structure throughout a densely wooded African landscape

Megafauna strongly affect vegetation structure and composition, often leading to management concern. However, the extent of their influence across large scales and varying ecosystems remains largely unknown. Using high resolution airborne Light Detection and Ranging (LiDAR), we investigated landscape-scale changes in vegetation height and three-dimensional (3D) structure across landscapes of varying elephant densities and presence over time, and in response to surface water distribution and terrain variability in the heavily managed thicket biome of the Addo Elephant National Park, South Africa. Elephants caused up to a fourfold reduction in vegetation height and altered the vertical profile, but increased vegetation height variability. Vegetation height also increased with elevation and distance from water, particularly in areas that elephants had long occupied at high densities. Slope had opposing effects on vegetation height, with height increasing with slope in areas long exposed to elephants, but decreasing where elephants had only recently been granted access. Our results suggest that elephants are the primary agents of vegetation change in this ecosystem, but that the strength of their effects varies across the landscape, enabling management to use water and terrain as mitigation tools. We further highlight the necessity of landscape-level experimental studies on megafaunal effects to untangle mechanisms and establish causality.

What mediates tree mortality during drought in the southern Sierra Nevada?

Severe drought has the potential to cause selective mortality within a forest, thereby inducing shifts in forest species composition. The southern Sierra Nevada foothills and mountains of California have experienced extensive forest dieback due to drought stress and insect outbreak. We used high-fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) from the Carnegie Airborne Observatory (CAO) to estimate the effect of forest dieback on species composition in response to drought stress in Sequoia National Park. Our aims were (1) to quantify site-specific conditions that mediate tree mortality along an elevation gradient in the southern Sierra Nevada Mountains, (2) to assess where mortality events have a greater probability of occurring, and (3) to estimate which tree species have a greater likelihood of mortality along the elevation gradient. A series of statistical models were generated to classify species composition and identify tree mortality, and the influences of different environmental factors were spatially quantified and analyzed to assess where mortality events have a greater likelihood of occurring. A higher probability of mortality was observed in the lower portion of the elevation gradient, on southwest- and west-facing slopes, in areas with shallow soils, on shallower slopes, and at greater distances from water. All of these factors are related to site water balance throughout the landscape. Our results also suggest that mortality is species-specific along the elevation gradient, mainly affecting Pinus ponderosa and Pinus lambertiana at lower elevations. Selective mortality within the forest may drive long-term shifts in community composition along the elevation gradient.

Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation

Functional biogeography may bridge a gap between field-based biodiversity information and satellite-based Earth system studies, thereby supporting conservation plans to protect more species and their contributions to ecosystem functioning. We used airborne laser-guided imaging spectroscopy with environmental modeling to derive large-scale, multivariate forest canopy functional trait maps of the Peruvian Andes-to-Amazon biodiversity hotspot. Seven mapped canopy traits revealed functional variation in a geospatial pattern explained by geology, topography, hydrology, and climate. Clustering of canopy traits yielded a map of forest beta functional diversity for land-use analysis. Up to 53% of each mapped, functionally distinct forest presents an opportunity for new conservation action. Mapping functional diversity advances our understanding of the biosphere to conserve more biodiversity in the face of land use and climate change.

Riparian vegetation structure and the hunting behavior of adult estuarine crocodiles

Riparian ecosystems are amongst the most biodiverse tropical habitats. They are important, and essential, ecological corridors, linking remnant forest fragments. In this study, we hypothesised that crocodile's actively select nocturnal resting locations based on increased macaque predation potential. We examined the importance of riparian vegetation structure in the maintenance of crocodile hunting behaviours. Using airborne Light Detection and Ranging (LiDAR) and GPS telemetry on animal movement, we identified the repeated use of nocturnal resting sites by adult estuarine crocodiles (Crocodylus porosus) throughout the fragmented Lower Kinabatangan Wildlife Sanctuary in Sabah, Malaysia. Crocodile resting locations were found to resemble, in terms of habitat characteristics, the sleeping sites of long-tailed macaque; positioned in an attempt to avoid predation by terrestrial predators. We found individual crocodiles were actively selecting overhanging vegetation and that the protrusion of trees from the tree line was key to site selection by crocodiles, as well as influencing both the presence and group size of sleeping macaques. Although these findings are correlational, they have broad management implications, with the suggestion that riparian corridor maintenance and quality can have implications beyond that of terrestrial fauna. We further place our findings in the context of the wider ecosystem and the maintenance of trophic interactions, and discuss how future habitat management has the potential to mitigate human-wildlife conflict.