The height of woody plants is a defining characteristic of forest and shrubland ecosystems because height responds to climate, soil and disturbance history. Orbiting LiDAR instruments, Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation LiDAR (GEDI), can provide near-global datasets of plant height at plot-level resolution. We evaluate canopy height measurements from ICESat-2 and GEDI with high resolution airborne LiDAR in six study sites in different biomes from dryland shrub to tall forests, with mean canopy height across sites of 0.5–40 m. ICESat-2 and GEDI provide reliable estimates for the relative height with RMSE and mean absolute error (MAE) of 7.49 and 4.64 m (all measurements ICESat-2) and 6.52 and 4.08 m (all measurements GEDI) for 98th percentile relative heights. Both datasets slightly overestimate the height of short shrubs (1–2 m at 5 m reference height), underestimate that of tall trees (by 6–7 m at 40 m reference height) and are highly biased (>3 m) for reference height <5 m, perhaps because of the difficulty of distinguishing canopy from ground signals. Both ICESat-2 and GEDI height estimates were only weakly sensitive to canopy cover and terrain slope (R² < 0.06) and had lower error for night compared to day samples (ICESat-2 RMSE night: 5.57 m, day: 6.82 m; GEDI RMSE night: 5.94 m, day: 7.03 m). For GEDI, the day versus night differences varied with differences in mean sample heights for the day and night samples and had little effect on bias. Accuracy of ICESat-2 and GEDI canopy heights varies among biomes, and the highest MAE was observed in the tallest, densest forest (GEDI: 7.85 m; ICESat-2: 7.84 m (night) and 12.83 m (day)). Improvements in canopy height estimation would come from better discrimination of canopy photons from background noise for ICESat-2 and improvements in the algorithm for decomposing ground and canopy returns for GEDI. Both would benefit from methods to distinguish outlier samples.

Biodiversity-structure relationships (BSRs), which describe the correlation between biodiversity and three-dimensional forest structure, have been used to map spatial patterns in biodiversity based on forest structural attributes derived from lidar. However, with the advent of spaceborne lidar like the Global Ecosystem Dynamics Investigation (GEDI), investigators are confronted with how to predict biodiversity from discrete GEDI footprints, sampled discontinuously across the Earth surface and often spatially offset from where diversity was measured in the field. In this study, we used National Ecological Observation Network data in a hierarchical modeling framework to assess how spatially-coincident BSRs (where field-observed taxonomic diversity measurements and structural data from airborne lidar coincide at a single plot) compare with BSRs based on statistical aggregates of proximate, but spatially-dispersed GEDI samples of structure. Despite substantial ecoregional variation, results confirm cross-biome consistency in the relationship between plant/tree alpha diversity and spatially-coincident lidar data, including structural data from outside the field plot where diversity was measured. Moreover, we found that generalized forest structural profiles derived from GEDI footprint aggregates were consistently related to tree alpha diversity, as well as cross-biome patterns in beta and gamma diversity. These findings suggest that characteristic forest structural profiles generated from aggregated GEDI footprints are effective for BSR diversity prediction without incorporation of more standard predictors of biodiversity like climate, topography, or optical reflectance. Cross-scale comparisons between airborne- and GEDI-derived structural profiles provide guidance for balancing scale-dependent trade-offs between spatial proximity and sample size for BSR-based prediction with GEDI gridded products. This study fills a critical gap in our understanding of how generalized forest structural attributes can be used to infer specific field-observed biodiversity patterns, including those not directly observable from remote sensing instruments. Moreover, it bolsters the empirical basis for global-scale biodiversity prediction with GEDI spaceborne lidar.

Community-based monitoring (CBM) is increasingly cited as a means of collecting valuable baseline data that can contribute to our understanding of environmental change whilst supporting Indigenous governance and self-determination in research. However, current environmental CBM models have specific limitations that impact program effectiveness and the progression of research stages beyond data collection. Here, we highlight key aspects that limit the progression of Arctic CBM programs which include funding constraints, organisational structures, and operational processes. Exemplars from collaborative environmental research conducted in the acutely climate change impacted Hamlet of Tuktoyaktuk, Inuvialuit Settlement Region (ISR), Canada, are used to identify co-developed solutions to address these challenges. These learnings from experience-based collaborations feed into a new solution-orientated model of environmental community-based research (CBR) that emphasises continuity between and community ownership in all research stages to enable a more complete research workflow. Clear recommendations are provided to develop a more coherent approach to achieving this model, which can be adapted to guide the development of successful environmental CBR programs in different research and place-based contexts.

The direction and magnitude of tundra vegetation productivity trends inferred from the normalized difference vegetation index (NDVI) have exhibited spatiotemporal heterogeneity over recent decades. This study examined the spatial and temporal drivers of Moderate Resolution Imaging Spectroradiometer Max NDVI (a proxy for peak growing season aboveground biomass) and time-integrated (TI)-NDVI (a proxy for total growing season productivity) on the Yamal Peninsula, Siberia, Russia between 2001 and 2018. A suite of remotely-sensed environmental drivers and machine learning methods were employed to analyze this region with varying climatological conditions, landscapes, and vegetation communities to provide insight into the heterogeneity observed across the Arctic. Summer warmth index, the timing of snowmelt, and physiognomic vegetation unit best explained the spatial distribution of Max and TI-NDVI on the Yamal Peninsula, with the highest mean Max and TI-NDVI occurring where summer temperatures were higher, snowmelt occurred earlier, and erect shrub and wetland vegetation communities were dominant. Max and TI-NDVI temporal trends were positive across the majority of the Peninsula (57.4% [5.0% significant] and 97.6% [13.9% significant], respectively) between 2001 and 2018. Max and TI-NDVI trends had variable relationships with environmental drivers and were primarily influenced by coastal-inland gradients in summer warmth and soil moisture. Both Max and TI-NDVI were negatively impacted by human modification, highlighting how human disturbances are becoming an increasingly important driver of Arctic vegetation dynamics. These findings provide insight into the potential future of Arctic regions experiencing warming, moisture regime shifts, and human modification, and demonstrate the usefulness of considering multiple NDVI metrics to disentangle the effects of individual drivers across heterogeneous landscapes. Further, the spatial heterogeneity in the direction and magnitude of interannual covariation between Max NDVI, TI-NDVI, and climatic drivers highlights the difficulty in generalizing the effects of individual drivers on Arctic vegetation productivity across large regions.

The modern economic growth paradigm relies heavily on natural endowments. Renewable energy as a permanent energy source has the potential to reduce the ecological footprint (EF). We adopt the Vector Autoregressive model to examine the impact of renewable energy consumption on the energy EF and use the quantile regression method to test the heterogeneity and asymmetry between energy EF and photovoltaic, wind energy, and biomass energy. The results show that renewable energy has a long-term negative impact on the EF, and for every 1% increase in renewable energy consumption, the energy EF will decrease by 2.91%. The contribution of renewable energy consumption to reducing the EF is 1.34% on average. There is no two-way Granger causality between renewable energy consumption and energy EF. The reduction effect of wind energy consumption on the energy EF varies the most, followed by biomass energy and photovoltaic. In addition, under different energy EF distribution conditions, the impact of photovoltaic or wind energy or biomass energy consumption on the energy EF is different.

The stratified nature of tropical forest structure had been noted by early explorers, but until recent use of satellite-based LiDAR (GEDI, or Global Ecosystems Dynamics Investigation LiDAR), it was not possible to quantify stratification across all tropical forests. Understanding stratification is important because by some estimates, a majority of the world's species inhabit tropical forest canopies. Stratification can modify vertical microenvironment, and thus can affect a species' susceptibility to anthropogenic climate change. Here we find that, based on analyzing each GEDI 25 m diameter footprint in tropical forests (after screening for human impact), most footprints (60%–90%) do not have multiple layers of vegetation. The most common forest structure has a minimum plant area index (PAI) at ∼40 m followed by an increase in PAI until ∼15 m followed by a decline in PAI to the ground layer (described hereafter as a one peak footprint). There are large geographic patterns to forest structure within the Amazon basin (ranging between 60% and 90% one peak) and between the Amazon (79 ± 9% sd) and SE Asia or Africa (72 ± 14% v 73 ± 11%). The number of canopy layers is significantly correlated with tree height (r² = 0.12) and forest biomass (r² = 0.14). Environmental variables such as maximum temperature (T_max) (r² = 0.05), vapor pressure deficit (VPD) (r² = 0.03) and soil fertility proxies (e.g. total cation exchange capacity −r² = 0.01) were also statistically significant but less strongly correlated given the complex and heterogeneous local structural to regional climatic interactions. Certain boundaries, like the Pebas Formation and Ecoregions, clearly delineate continental scale structural changes. More broadly, deviation from more ideal conditions (e.g. lower fertility or higher temperatures) leads to shorter, less stratified forests with lower biomass.

Significant uncertainties persist concerning how Arctic soil tundra carbon emission responds to environmental changes. In this study, 24 cores were sampled from drier (high centre polygons and rims) and wetter (low centre polygons and troughs) permafrost tundra ecosystems. We examined how soil CO₂ and CH₄ fluxes responded to laboratory-based manipulations of soil temperature (and associated thaw depth) and water table depth, representing current and projected conditions in the Arctic. Similar soil CO₂ respiration rates occurred in both the drier and the wetter sites, suggesting that a significant proportion of soil CO₂ emission occurs via anaerobic respiration under water-saturated conditions in these Arctic tundra ecosystems. In the absence of vegetation, soil CO₂ respiration rates decreased sharply within the first 7 weeks of the experiment, while CH₄ emissions remained stable for the entire 26 weeks of the experiment. These patterns suggest that soil CO₂ emission is more related to plant input than CH₄ production and emission. The stable and substantial CH₄ emission observed over the entire course of the experiment suggests that temperature limitations, rather than labile carbon limitations, play a predominant role in CH₄ production in deeper soil layers. This is likely due to the presence of a substantial source of labile carbon in these carbon-rich soils. The small soil temperature difference (a median difference of 1 °C) and a more substantial thaw depth difference (a median difference of 6 cm) between the high and low temperature treatments resulted in a non-significant difference between soil CO₂ and CH₄ emissions. Although hydrology continued to be the primary factor influencing CH₄ emissions, these emissions remained low in the drier ecosystem, even with a water table at the surface. This result suggests the potential absence of a methanogenic microbial community in high-centre polygon and rim ecosystems. Overall, our results suggest that the temperature increases reported for these Arctic regions are not responsible for increases in carbon losses. Instead, it is the changes in hydrology that exert significant control over soil CO₂ and CH₄ emissions.

Vegetation species mapping using airborne imaging spectroscopy yields accurate results and is important for advancing conservation objectives and biogeographic studies. As these data become more readily available owing to the upcoming launch of spaceborne imaging spectrometers, it is necessary to understand how these data can be used to consistently classify species across large geographic scales. However, few studies have attempted to map species across multiple ecosystems; therefore, little is known regarding the effect of intra-specific variation on the mapping of a single species across a wide range of environments and among varying backgrounds of other non-target species. To explore this effect, we developed and tested species classification models for Metrosideros polymorpha, a highly polymorphic canopy species endemic to Hawai'i, which is found in a diverse array of ecosystems. We compared the accuracies of support vector machine (SVM) and random forest models trained on canopy reflectance data from each of eight distinct ecosystems (ecosystem-specific) and a universal model trained on data from all ecosystems. When applied to ecosystem-specific test datasets, the ecosystem-specific models outperformed the universal model; however, the universal model retained high (>81%) accuracies across all ecosystems. Additionally, we found that models from ecosystems with broad variation in M. polymorpha canopy traits, as estimated using chemometric equations applied to canopy spectra, accurately predicted M. polymorpha in other ecosystems. While species classifications across ecosystems can yield accurate results, these results will require sampling procedures that capture the intra-specific variation of the target species.

Animal-related outages (AROs) are a prevalent form of outages in electrical distribution systems. Animal-infrastructure interactions vary across species and regions, underlining the need to study the animal-outage relationship in more species and diverse systems. Animal activity has been an indicator of reliability in the electrical grid system by describing temporal patterns in AROs. However, these ARO models have been limited by a lack of available species activity data, instead approximating activity based on seasonal patterns and weather dependency in ARO records and characteristics of broad taxonomic groups, e.g. squirrels. We highlight available resources to fill the ecological data gap limiting joint analyses between ecology and energy sectors. Species distribution modeling (SDM), a common technique to model the distribution of a species across geographic space and time, paired with community science data, provided us with species-specific estimates of activity to analyze alongside spatio-temporal patterns of ARO severity. We use SDM estimates of activity for multiple outage-prone bird species to examine whether diverse animal activity patterns were important predictors of ARO severity by capturing existing variation within animal-outage relationships. Low dimensional representation and single patterns of bird activity were important predictors of ARO severity in Massachusetts. However, both patterns of summer migrants and overwintering species showed some degree of importance, indicating that multiple biological patterns could be considered in future models of grid reliability. Making the best available resources from quantitative ecology known to outside disciplines can allow for more interdisciplinary data analyses between ecological and non-ecological systems. This can result in further opportunities to examine and validate the relationships between animal activity and grid reliability in diverse systems.

In dryland ecosystems, vegetation within different plant functional groups exhibits distinct seasonal phenologies that are affected by the prevailing hydroclimatic forcing. The seasonal variability of precipitation, atmospheric evaporative demand, and streamflow influences root-zone water availability to plants in water-limited environments. Increasing interannual variations in climate forcing of the local water balance and uncertainty regarding climate change projections have raised the potential for phenological shifts and changes to vegetation dynamics. This poses significant risks to plant functional types across large areas, especially in drylands and within riparian ecosystems. Due to the complex interactions between climate, water availability, and seasonal plant water use, the timing and amplitude of phenological responses to specific hydroclimate forcing cannot be determined a priori, thus limiting efforts to dynamically predict vegetation greenness under future climate change. Here, we analyze two decades (1994–2021) of remote sensing data (soil adjusted vegetation index (SAVI)) as well as contemporaneous hydroclimate data (precipitation, potential evapotranspiration, depth to groundwater, and air temperature), to identify and quantify the key hydroclimatic controls on the timing and amplitude of seasonal greenness. We focus on key phenological events across four different plant functional groups occupying distinct locations and rooting depths in dryland SE Arizona: semi-arid grasses and shrubs, xeric riparian terrace and hydric riparian floodplain trees. We find that key phenological events such as spring and summer greenness peaks in grass and shrubs are strongly driven by contributions from antecedent spring and monsoonal precipitation, respectively. Meanwhile seasonal canopy greenness in floodplain and terrace vegetation showed strong response to groundwater depth as well as antecedent available precipitation (aaP = P − PET) throughout reaches of perennial and intermediate streamflow permanence. The timings of spring green-up and autumn senescence were driven by seasonal changes in air temperature for all plant functional groups. Based on these findings, we develop and test a simple, empirical phenology model, that predicts the timing and amplitude of greenness based on hydroclimate forcing. We demonstrate the feasibility of the model by exploring simple, plausible climate change scenarios, which may inform our understanding of phenological shifts in dryland plant communities and may ultimately improve our predictive capability of investigating and predicting climate-phenology interactions in the future.

The height of woody plants is a defining characteristic of forest and shrubland ecosystems because height responds to climate, soil and disturbance history. Orbiting LiDAR instruments, Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation LiDAR (GEDI), can provide near-global datasets of plant height at plot-level resolution. We evaluate canopy height measurements from ICESat-2 and GEDI with high resolution airborne LiDAR in six study sites in different biomes from dryland shrub to tall forests, with mean canopy height across sites of 0.5–40 m. ICESat-2 and GEDI provide reliable estimates for the relative height with RMSE and mean absolute error (MAE) of 7.49 and 4.64 m (all measurements ICESat-2) and 6.52 and 4.08 m (all measurements GEDI) for 98th percentile relative heights. Both datasets slightly overestimate the height of short shrubs (1–2 m at 5 m reference height), underestimate that of tall trees (by 6–7 m at 40 m reference height) and are highly biased (>3 m) for reference height <5 m, perhaps because of the difficulty of distinguishing canopy from ground signals. Both ICESat-2 and GEDI height estimates were only weakly sensitive to canopy cover and terrain slope (R² < 0.06) and had lower error for night compared to day samples (ICESat-2 RMSE night: 5.57 m, day: 6.82 m; GEDI RMSE night: 5.94 m, day: 7.03 m). For GEDI, the day versus night differences varied with differences in mean sample heights for the day and night samples and had little effect on bias. Accuracy of ICESat-2 and GEDI canopy heights varies among biomes, and the highest MAE was observed in the tallest, densest forest (GEDI: 7.85 m; ICESat-2: 7.84 m (night) and 12.83 m (day)). Improvements in canopy height estimation would come from better discrimination of canopy photons from background noise for ICESat-2 and improvements in the algorithm for decomposing ground and canopy returns for GEDI. Both would benefit from methods to distinguish outlier samples.

The direction and magnitude of tundra vegetation productivity trends inferred from the normalized difference vegetation index (NDVI) have exhibited spatiotemporal heterogeneity over recent decades. This study examined the spatial and temporal drivers of Moderate Resolution Imaging Spectroradiometer Max NDVI (a proxy for peak growing season aboveground biomass) and time-integrated (TI)-NDVI (a proxy for total growing season productivity) on the Yamal Peninsula, Siberia, Russia between 2001 and 2018. A suite of remotely-sensed environmental drivers and machine learning methods were employed to analyze this region with varying climatological conditions, landscapes, and vegetation communities to provide insight into the heterogeneity observed across the Arctic. Summer warmth index, the timing of snowmelt, and physiognomic vegetation unit best explained the spatial distribution of Max and TI-NDVI on the Yamal Peninsula, with the highest mean Max and TI-NDVI occurring where summer temperatures were higher, snowmelt occurred earlier, and erect shrub and wetland vegetation communities were dominant. Max and TI-NDVI temporal trends were positive across the majority of the Peninsula (57.4% [5.0% significant] and 97.6% [13.9% significant], respectively) between 2001 and 2018. Max and TI-NDVI trends had variable relationships with environmental drivers and were primarily influenced by coastal-inland gradients in summer warmth and soil moisture. Both Max and TI-NDVI were negatively impacted by human modification, highlighting how human disturbances are becoming an increasingly important driver of Arctic vegetation dynamics. These findings provide insight into the potential future of Arctic regions experiencing warming, moisture regime shifts, and human modification, and demonstrate the usefulness of considering multiple NDVI metrics to disentangle the effects of individual drivers across heterogeneous landscapes. Further, the spatial heterogeneity in the direction and magnitude of interannual covariation between Max NDVI, TI-NDVI, and climatic drivers highlights the difficulty in generalizing the effects of individual drivers on Arctic vegetation productivity across large regions.

How species richness scales spatially is a foundational concept of community ecology, but how biotic interactions scale spatially is poorly known. Previous studies have proposed interactions-area relationships (IARs) based on two competing relationships for how the number of interactions scale with the number of species, the 'link-species scaling law' and the 'constant connectance hypothesis.' The link-species scaling law posits that the number of interactions per species remains constant as the size of the network increases. The constant connectance hypothesis says that the proportion of realized interactions remains constant with network size. While few tests of these IARs exist, evidence for the original interactions-species relationships are mixed. We propose a novel IAR and test it against the two existing IARs. We first present a general theory for how interactions scale spatially and the mathematical relationship between the IAR and the species richness-area curve. We then provide a new mathematical formulation of the IAR, accounting for connectance varying with area. Employing data from three mutualistic networks (i.e. a network which specifies interconnected and mutually-beneficial interactions between two groups of species), we evaluate three competing models of how interactions scale spatially: two previously published IAR models and our proposed IAR. We find the new IAR described by our theory-based equation fits the empirical datasets equally as well as the previously proposed IAR based on the link-species scaling law in one out of three cases and better than the previously-proposed models in two out of three cases. Our novel IAR improves upon previous models and quantifies mutualist interactions across space, which is paramount to understanding biodiversity and preventing its loss.

Vegetation species mapping using airborne imaging spectroscopy yields accurate results and is important for advancing conservation objectives and biogeographic studies. As these data become more readily available owing to the upcoming launch of spaceborne imaging spectrometers, it is necessary to understand how these data can be used to consistently classify species across large geographic scales. However, few studies have attempted to map species across multiple ecosystems; therefore, little is known regarding the effect of intra-specific variation on the mapping of a single species across a wide range of environments and among varying backgrounds of other non-target species. To explore this effect, we developed and tested species classification models for Metrosideros polymorpha, a highly polymorphic canopy species endemic to Hawai'i, which is found in a diverse array of ecosystems. We compared the accuracies of support vector machine (SVM) and random forest models trained on canopy reflectance data from each of eight distinct ecosystems (ecosystem-specific) and a universal model trained on data from all ecosystems. When applied to ecosystem-specific test datasets, the ecosystem-specific models outperformed the universal model; however, the universal model retained high (>81%) accuracies across all ecosystems. Additionally, we found that models from ecosystems with broad variation in M. polymorpha canopy traits, as estimated using chemometric equations applied to canopy spectra, accurately predicted M. polymorpha in other ecosystems. While species classifications across ecosystems can yield accurate results, these results will require sampling procedures that capture the intra-specific variation of the target species.

Riparian vegetation usually gets less focus in biodiversity assessments and yet species diversity is important knowledge when applying patch specific conservation value in the Niassa Special Reserve (NSR). This study assessed the composition and conservation status of riparian species in an exposed river basin downstream location. Purposive sampling was used in the selection of sites and respondents to maximize data collection. The study found 19 species belonging to 15 families with 52.63% of them having a frequency of ⩾50% in sampling plots. There were 10 species that are endemic to the sub-Sharan Africa Region. Fabaceae was the dominant family with 5 species. The species with the highest population was Flacourtia indica (Burm. f.) Merr. Species richness ranged from 0.35 to 0.98 with a mean of 0.66 ± 0.22. The IVI ranged from 34.70 (F. indica (Burm. f.) Merr) to 4.43 (Tribulus cistoides L.) with a mean of 15.79 ± 7.79. Threats of species loss and ecosystem disturbance were agriculture, infrastructure development and plant harvests. There was a reported decline in species availability over the previous 10 years by 18.7% of the respondents. The results added to existing studies and records of vegetation species of conservation value in areas exposed to loss in the NSR. This study advances research on vegetation range dynamics in the NSR and presents a need to mitigate human land use impacts on riparian vegetation species composition.

Reactions induced by sunlight (direct photolysis and indirect photochemistry) are important ecosystem services that aid freshwater bodies in removing contaminants, although they may also exacerbate pollution in some cases. Without photoinduced reactions, pollution problems would be considerably worse overall. The photochemical reaction rates depend on seasonality, depth, water chemistry (which also significantly affects the reaction pathways), and pollutant photoreactivity. Photochemical reactions are also deeply impacted by less studied factors, including hydrology, water dynamics, and precipitation regimes, which are key to understanding the main impacts of climate change on surface-water photochemistry. Climate change is expected in many cases to both exacerbate freshwater pollution, and enhance photochemical decontamination. Therefore, photochemical knowledge will be essential to understand the future evolution of freshwater environments.