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.
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Environmental Research: Ecology is a multidisciplinary, open access journal devoted to addressing important macroscale challenges at the interface of ecology, biodiversity and conservation. The journal bridges scientific progress and methodological advances with assessments of environmental change impacts on ecosystems, and the responses of those ecosystems to change, including resilience, vulnerability and adaptation. For detailed information about subject coverage see the About the journal section.
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Qiuyan Yu et al 2024 Environ. Res.: Ecology 3 025001
Margaret C Stanley and Josie A Galbraith 2024 Environ. Res.: Ecology 3 023001
There is an increasing disconnect between people and nature as we become more urbanised. Intensification in cities often results in a reduction of natural areas, more homogenised and manicured green spaces, and loss of biota. Compared to people in rural areas, urban dwellers are less likely visit natural areas and recognise and value biota. Reconnecting people with nature in the city not only benefits human mental and physical wellbeing but can also have positive effects on how people value biodiversity and act on conservation issues. However, in some contexts, the push to reconnect people with nature may have unintended negative outcomes on biodiversity, particularly if place-specific nature is not used in urban greening. In the current biodiversity crisis, using vegetation and green space design that is not reflective of the environmental context of a city can further disconnect residents, particularly Indigenous people, from their local environment and species, and further entrench extinction of experience and loss of environmental values. This disconnect can result in residents applying wildlife gardening practices, such as bird feeding, that are not specific to place, and benefit introduced species over indigenous species. Furthermore, cities are gateways for invasive species, and using species in greening projects that are not locally sourced has already left cities and their surrounding regions with a large weed legacy. Using place-specific nature and green space in cities can be less resource intensive, highly beneficial for biodiversity and give residents a unique sense of place. Rather than simply adding 'more nature' in cities, the messaging should be more complex, emphasising the need for urban greening to be context specific to avoid negative impacts on biodiversity and ecological and cultural services.
C R Hakkenberg et al 2023 Environ. Res.: Ecology 2 035005
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.
Yu Nan et al 2023 Environ. Res.: Ecology 2 015003
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.
Christopher E Doughty et al 2023 Environ. Res.: Ecology 2 035002
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 (Tmax) (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.
Louise Mercer et al 2023 Environ. Res.: Ecology 2 045001
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.
Morgan S Tassone et al 2024 Environ. Res.: Ecology 3 015003
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.
K Best et al 2023 Environ. Res.: Ecology 2 045003
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.
Yayoi Takeuchi et al 2023 Environ. Res.: Ecology 2 035003
The complex stratification of tropical forests is a key feature that directly contributes to high aboveground biomass (AGB) and species diversity. This study aimed to explore the vertical patterns of AGB and tree species diversity in the tropical forest of Pasoh Forest Reserve, Malaysia. To achieve this goal, we used a combination of field surveys and drone technology to gather data on species diversity, tree height (H), and tree diameter at breast height (D). As all trees in the 6 ha plot were tagged and identified, we used the data to classify the taxonomy and calculate species diversity indices. We used unmanned aerial vehicle-based structure-from-motion photogrammetry to develop a Digital Canopy Height Model to accurately estimate H. The collected data and previous datasets were then used to develop Bayesian height–diameter (HD) models that incorporate taxonomic effects into conventional allometric and statistical models. The best models were selected based on their performance in cross-validation and then used to estimate AGB per tree and the total AGB in the plot. Results showed that taxonomic effects at the family and genus level improved the HD models and consequent AGB estimates. The AGB was the highest in the higher layers of the forest, and AGB was largely contributed by larger trees, especially specific families such as Dipterocarpaceae, Euphorbiaceae, and Fabaceae. In contrast, species diversity was the highest in the lower layers, whereas functional diversity was the highest in the middle layers. These contrasting patterns of AGB and species diversity indicate different roles of forest stratification and layer-specific mechanisms in maintaining species diversity. This study highlights the importance of considering taxonomic effects when estimating AGB and species diversity in tropical forests. These findings underscore the need for a more comprehensive understanding of the complex stratification of tropical forests and its impact on the forest ecosystem.
Jolanda M H Verspagen et al 2022 Environ. Res.: Ecology 1 015001
Although environmental impacts on the biodiversity and species composition of lakes have been studied in great detail at local and regional scales, unraveling the big picture of how lake communities respond to environmental variation across large spatial scales has received less attention. We performed a comprehensive analysis to assess how the phytoplankton community composition varies among >1000 lakes across the conterminous United States of America. Our results show that lake-to-lake similarity in species composition was low even at the local scale, and slightly decreased with geographical distance. Analysis of the compositional data by Dirichlet regression revealed that the geographical variation in phytoplankton community composition was best explained by total phosphorus (TP), water temperature, pH, and lake size. High TP concentrations were associated with high relative abundances of cyanobacteria and euglenophytes at the expense of other phytoplankton groups. High lake temperatures stimulated cyanobacteria, dinoflagellates, desmids and euglenophytes, whereas cryptophytes, golden algae and diatoms were relatively more abundant in colder lakes. Low lake pH correlated with high dissolved CO2 concentrations, which may explain why it benefitted phytoplankton groups with inefficient carbon concentrating mechanisms such as golden algae and euglenophytes. Conversely, the relative abundance of cyanobacteria showed a pronounced increase with lake pH. Large lakes showed higher relative abundances of cyanobacteria and diatoms, whereas small lakes showed higher relative abundances of chlorophytes, desmids and euglenophytes. Biodiversity increased with lake temperature, but decreased at high TP concentrations and pH. The key environmental variables identified by our study (high phosphorus loads, warm temperature, low pH) are associated with anthropogenic pressures such as eutrophication, global warming and rising atmospheric CO2 concentration. Hence, our results provide a comprehensive illustration of the major impact of these anthropogenic pressures on the biodiversity and taxonomic composition of lake phytoplankton communities.
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Margaret C Stanley and Josie A Galbraith 2024 Environ. Res.: Ecology 3 023001
There is an increasing disconnect between people and nature as we become more urbanised. Intensification in cities often results in a reduction of natural areas, more homogenised and manicured green spaces, and loss of biota. Compared to people in rural areas, urban dwellers are less likely visit natural areas and recognise and value biota. Reconnecting people with nature in the city not only benefits human mental and physical wellbeing but can also have positive effects on how people value biodiversity and act on conservation issues. However, in some contexts, the push to reconnect people with nature may have unintended negative outcomes on biodiversity, particularly if place-specific nature is not used in urban greening. In the current biodiversity crisis, using vegetation and green space design that is not reflective of the environmental context of a city can further disconnect residents, particularly Indigenous people, from their local environment and species, and further entrench extinction of experience and loss of environmental values. This disconnect can result in residents applying wildlife gardening practices, such as bird feeding, that are not specific to place, and benefit introduced species over indigenous species. Furthermore, cities are gateways for invasive species, and using species in greening projects that are not locally sourced has already left cities and their surrounding regions with a large weed legacy. Using place-specific nature and green space in cities can be less resource intensive, highly beneficial for biodiversity and give residents a unique sense of place. Rather than simply adding 'more nature' in cities, the messaging should be more complex, emphasising the need for urban greening to be context specific to avoid negative impacts on biodiversity and ecological and cultural services.
Qiuyan Yu et al 2024 Environ. Res.: Ecology 3 025001
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.
Morgan S Tassone et al 2024 Environ. Res.: Ecology 3 015003
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.
Manette E Sandor et al 2024 Environ. Res.: Ecology 3 015002
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.
M M Seeley et al 2024 Environ. Res.: Ecology 3 011001
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.
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Davide Vione 2023 Environ. Res.: Ecology 2 012001
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.
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Armstrong et al
Biodiversity, when viewed through the combined lenses of compositional, structural, and functional attributes, provides for a holistic understanding of the complexities found within community assemblages and ecosystems. However, advancement in our understanding of how ecosystem functional diversity interacts with structural and compositional diversity metrics is lacking, in part because universally applied methodologies to derive ecosystem functional classifications are still under development and vary widely across scales, extents and biomes. This study presents a methodology to construct ecosystem functional types (EFTs), or areas of the land surface that function similarly, using the MODIS NDVI record, for the terrestrial circumpolar Arctic. EFTs were derived from the seasonal dynamics of NDVI, over the Arctic tundra at 250m resolution and compared to bioclimate subzones and to structurally and compositionally defined vegetation units of the Circumpolar Arctic Vegetation Map (CAVM). Correspondence analyses of CAVM EFTs to previously delineated CAVM bioclimatic subzones, physiognomic (vegetation) units and floristic provinces revealed a general congruence, indicating convergence across composition, structure, and function; yet also demonstrated substantial functional variability even within bioclimate subzones and vegetation units. Strong latitudinal gradients in ecosystem function are present, with EFT richness ranging from low (34) in northernmost regions to high (45) in southernmost regions. Locally, the mountainous regions of northern Alaska, and eastern and western Siberia had high spatial variability in ecosystem functioning. Aside from these generalities, we found that EFTs varied widely within individual mapped vegetation units, successfully capturing the functional dimension of biodiversity across the circumpolar Arctic tundra.
Quinn et al
Increased environmental threats require proper monitoring of animal communities to understand where and when changes occur. Ecoacoustic tools that quantify natural acoustic environments use a combination of biophony (animal sound) and geophony (wind, rain, and other natural phenomena) to represent the natural soundscape and, in comparison to anthropophony (technological human sound) can highlight valuable landscapes to both human and animal communities. However, recording these sounds requires intensive deployment of recording devices and storage and interpretation of large amounts of data, resulting in large data gaps across the landscape and periods in which recordings are absent. Interpolating ecoacoustic metrics like biophony, geophony, anthropophony, and acoustic indices can bridge these gaps in observations and provide insight across larger spatial extents and during periods of interest. Here, we use seven ecoacoustic metrics and acoustically-derived bird species richness across a heterogeneous landscape composed of densely urbanized, suburban, rural, protected, and recently burned lands in Sonoma County, California, U.S.A., to explore spatiotemporal patterns in ecoacoustic measurements. Predictive models of ecoacoustic metrics driven by land-use / land-cover (LULC) remotely-sensed vegetation structure, anthropogenic impact, climate, geomorphology, and phenology variables capture landscape and daily differences in ecoacoustic patterns with varying performance (avg. R2 = 0.38 ± 0.11) depending on metric and period-of-day and provide interpretable patterns in sound related to human activity, weather phenomena, and animal activity. We also offer a case study on the use of the data-driven prediction of biophony to capture changes in soniferous species activity before (1-2 years prior) and after (1-2 years post) wildfires in our study area and find that biophony may depict the reorganization of acoustic communities following wildfires. This is demonstrated by an upward trend in activity 1-2 years post-wildfire, particularly in more severely burned areas. Overall, we provide evidence of the importance of climate, spaceborne-lidar-derived forest structure, and phenological time series characteristics when modeling ecoacoustic metrics to upscale site observations and map ecoacoustic biodiversity in areas without prior acoustic data collection. Resulting maps can identify areas of attention where changes in animal communities occur at the edge of human and natural disturbances.