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White spruce (Picea glauca) spans a massive range, yet the variability in respiratory physiology and related implications for tree carbon balance at the extremes of this distribution remain as enigmas. Working at both the most northern and southern extents of the distribution range more than 5000 km apart, we measured the short-term temperature response of dark respiration (R/T) at upper and lower canopy positions. R/T curves were fit to both polynomial and thermodynamic models so that model parameters could be compared among locations, canopy positions, and with previously published data. Respiration measured at 25°C (R

Light availability drives vertical canopy gradients in photosynthetic functioning and carbon (C) balance, yet patterns of variability in these gradients remain unclear. We measured light availability, photosynthetic CO2 and light response curves, foliar C, nitrogen (N) and pigment concentrations, and the photochemical reflectance index (PRI) on upper and lower canopy needles of white spruce trees (Picea glauca) at the species’ northern and southern range extremes. We combined our photosynthetic data with previously published respiratory data to compare and contrast canopy C balance between latitudinal extremes. We found steep canopy gradients in irradiance, photosynthesis, and leaf traits at the southern range limit, but a lack of variation across canopy positions at the northern range limit. Thus, unlike many tree species from tropical to mid-latitude forests, high latitude trees may not require vertical gradients of metabolic activity to optimize photosynthetic C gain. Consequently, accounting for self-shading is less critical for predicting gross primary productivity at northern relative to southern latitudes. Northern trees also had a significantly smaller net positive leaf C balance than southern trees suggesting that, regardless of canopy position, low photosynthetic rates coupled with high respiratory costs may ultimately constrain the northern range limit of this widely distributed boreal species.SUMMARY STATEMENTCanopy gradients in photosynthetic capacity of white spruce diminish at high compared to low latitudes. Low carbon balance in high latitude trees may determine the extent of northern treeline.

Individual tree detection (ITD) and crown delineation are two of the most relevant methods for extracting detailed and reliable forest information from LiDAR (Light Detection and Ranging) datasets. However, advanced computational skills and specialized knowledge have been normally required to extract forest information from LiDAR.The development of accessible tools for 3D forest characterization can facilitate rapid assessment by stakeholders lacking a remote sensing background, thus fostering the practical use of LiDAR datasets in forest ecology and conservation. This paper introduces the treetop application, an open-source web-based and R package LiDAR analysis tool for extracting forest structural information at the tree level, including cutting-edge analyses of properties related to forest ecology and management.We provide case studies of how treetop can be used for different ecological applications, within various forest ecosystems. Specifically, treetop was employed to assess post-hurricane disturbance in natural temperate forests, forest homogeneity in industrial forest plantations and the spatial distribution of individual trees in a tropical forest.treetop simplifies the extraction of relevant forest information for forest ecologists and conservationists who may use the tool to easily visualize tree positions and sizes, conduct complex analyses and download results including individual tree lists and figures summarizing forest structural properties. Through this open-source approach, treetop can foster the practical use of LiDAR data among forest conservation and management stakeholders and help ecological researchers to further understand the relationships between forest structure and function. The authors thank Nicholas L. Crookston for co‐developing the web‐LiDAR treetop tool, and the two anonymous reviewers for their helpful suggestions on the first version of the manuscript. This study is based on the work supported by the Department of Defence Strategic Environmental Research and Development Program (SERDP) under grants No. RC‐2243, RC19‐1064 and RC20‐1346 and USDA Forest Service (grand No. PRO00031122)

Warming-induced nutrient enrichment in the Arctic may lead to shifts in leaf-level physiological properties and processes with potential consequences for plant community dynamics and ecosystem function. To explore the physiological responses of Arctic tundra vegetation to increasing nutrient availability, we examined how a set of leaf nutrient and physiological characteristics of eight plant species (representing four plant functional groups) respond to a gradient of experimental nitrogen (N) and phosphorus (P) enrichment. Specifically, we examined a set of chlorophyll fluorescence measures related to photosynthetic efficiency, performance and stress, and two leaf nutrient traits (leaf %C and %N), across an experimental nutrient gradient at the Arctic Long Term Ecological Research site, located in the northern foothills of the Brooks Range, Alaska. In addition, we explicitly assessed the direct relationships between chlorophyll fluorescence and leaf %N. We found significant differences in physiological and nutrient traits between species and plant functional groups, and we found that species within one functional group (deciduous shrubs) have significantly greater leaf %N at high levels of nutrient addition. In addition, we found positive, saturating relationships between leaf %N and chlorophyll fluorescence measures across all species. Our results highlight species-specific differences in leaf nutrient traits and physiology in this ecosystem. In particular, the effects of a gradient of nutrient enrichment were most prominent in deciduous plant species, the plant functional group known to be increasing in relative abundance with warming in this ecosystem.

White spruce (Picea glauca) spans a massive range from arctic treeline to temperate forests, yet the variability in respiratory physiology and related implications for tree carbon balance at the extremes of this distribution remain as enigmas. Working at both the most northern and southern extents of the white spruce distribution range more than 5000 km apart, we measured the short- term temperature response of dark respiration (R/T) at upper and lower canopy positions. R/T curves were fit to both polynomial and thermodynamic models so that model parameters could be compared among locations, canopy positions, and with previously published data. Respiration measured at 25°C (R25) was 68% lower at the southern location than at the northern location (0.73±0.15 vs. 2.27±0.02 μmol m-2 s-1), resulting in a significantly lower (p< 0.01) intercept in R/T response in temperate trees. Only at the southern location did upper canopy leaves have a steeper temperature response than lower canopy leaves, likely reflecting steeper canopy gradients in light. No differences were observed in the maximum temperature of respiration. At the northern range limit respiration is nearly twice that of the average R25 reported in a global leaf respiration database. This large carbon cost likely challenges tree survival and contributes to restricting the location of the northern treeline. We predict that without significant thermal acclimation, foliage respiration will increase with projected end-of-the-century warming and will likely constrain the future range limits of this important boreal species.Summary StatementWhite spruce (Picea glauca) needle respiration at the northern limit of the species range is three times higher than at the southern range limit (when measured at 25 °C). This high carbon cost likely challenges tree survival and contributes to the location of the northern treeline.

Arctic Treeline is the transition from the boreal forest to the treeless tundra and may be determined by growing season temperatures. The physiological mechanisms involved in determining the relationship between the physical and biological environment and the location of treeline are not fully understood. In Northern Alaska we studied the relationship between temperature and leaf respiration in 36 white spruce (Picea glauca) trees, sampling both the upper and lower canopy, to test two research hypotheses (H0). The first H01 is that canopy position will not influence leaf respiration. The associated alternative hypothesis (HA) is that the upper canopy leaves which are more directly coupled to the atmosphere will experience more challenging environmental conditions and thus have higher respiration rates to facilitate metabolic function. The second H02 is that tree size will not influence leaf respiration. The associated HA is that saplings (stems that are 5-10 cm DBH (diameter at breast height)) will have higher respiration rates than trees (stems [≥] 10 cm DBH) since saplings represent the transition from seedlings growing in the more favorable aerodynamic boundary layer, to trees which are fully coupled to the atmosphere but of sufficient size to persist. Respiration did not change with canopy position, however respiration at 25{degrees}C was 42% higher in saplings compared to trees (3.43 {+/-} 0.19 vs. 2.41 {+/-} 0.14 mol m-2 s-1). Furthermore, there were significant differences in the temperature response of respiration, and seedlings reached their maximum respiration rates at 59{degrees}C, more than two degrees higher than trees. Our results demonstrate that the respiratory characteristics of white spruce saplings at treeline are extreme, imposing a significant carbon cost that may contribute to their lack of perseverance beyond treeline. In the absence of thermal acclimation, the rate of leaf respiration could increase by 57% by the end of the century, posing further challenges to the ecology of this massive ecotone. This paper is dedicated to the memory of James N. Siedow, Professor of Botany at Duke University. I am honored to have learned from, and to have been inspired by Jim. I will always be grateful for the time he spent helping me, the depth of education he gave me, and his steady mentoring as part of my thesis committee. His devotion to science and push for deeper knowledge of plant respiration set an example for all of us, particularly those who were lucky enough to study with him. Jim was also a memorable Father Christmas at the departmental holiday parties, always keeping us laughing with his particular brand of quick wit and sarcasm. - Kevin L. Griffin

The forest-tundra ecotone (FTE) is exhibiting myriad responses to rapid environmental change. Microstructural variability (at cm to m length scales) of vegetation canopies and geomorphic features may modulate the response of FTE vegetation to regional climate changes. Understanding the influence of microstructure on tree function at the FTE is particularly relevant during vulnerable early growth stages. During these stages, individual trees are tightly coupled to conditions of the surface boundary layer, which can be more conducive to growth than the conditions above the boundary layer. Until recently, however, it has been difficult to characterize microstructure in a replicable, transferable manner. This study builds upon substantial research on ecological responses of trees at the FTE to growth environment conditions by integrating high-resolution terrestrial lidar scanning (TLS) to characterize microstructure. Our main goal was to use TLS technology to understand the effects of microstructure on photosynthetic functioning (i.e., chlorophyll fluorescence) of small-stature white spruce (Picea glauca (Moench) Voss) trees at the FTE. Our specific objectives were to: 1) determine how much variance in photosynthetic functioning is explained by microstructure; 2) identify microstructural metrics that most strongly control variance in photosynthetic functioning; and 3) determine the scales at which microstructural metrics most strongly drive variance in photosynthetic functioning. Random Forest modeling demonstrated that 28% of variance in photosynthetic functioning can be explained through variation in fine-scale environmental conditions that are modulated by microstructure alone. Insolation and canopy roughness were the most important predictors of photosynthetic functioning, and the sensitivity of photosynthetic functioning to canopy roughness was scale-dependent. This suggests that microstructure affects spatial heterogeneity in the boundary layer that may influence carbon assimilation of small-stature spruce trees. This research emphasizes the importance of quantifying microstructure in study systems where fine-scale heterogeneity of the growth environment may modulate plant responses to regional climate change.

Relatively little is known of how the world's largest vegetation transition zone – the Forest Tundra Ecotone (FTE) – is responding to climate change. Newly available, satellite-derived time-series of the photochemical reflectance index (PRI) across North America and Europe could provide new insights into the physiological response of evergreen trees to climate change by tracking changes in foliar pigment pools that have been linked to photosynthetic phenology. However, before implementing these data for such purpose at these evergreen dominated systems, it is important to increase our understanding of the fine scale mechanisms driving the connection between PRI and environmental conditions. The goal of this study is thus to gain a more mechanistic understanding of which environmental factors drive changes in PRI during late-season phenological transitions at the FTE – including factors that are susceptible to climate change (i.e., air- and soil-temperatures), and those that are not (photoperiod). We hypothesized that late-season phenological changes in foliar pigment pools captured by PRI are largely driven by photoperiod as opposed to less predictable drivers such as air temperature, complicating the utility of PRI time-series for understanding climate change effects on the FTE. Ground-based, time-series of PRI were acquired from individual trees in combination with meteorological variables and photoperiod information at six FTE sites in Alaska. A linear mixed-effects modeling approach was used to determine the significance (α = 0.001) and effect size (i.e., standardized slope b*) of environmental factors on late-seasonal changes in the PRI signal. Our results indicate that photoperiod had the strongest, significant effect on late-season changes in PRI (b* = 0.08, p

Digital terrain models (DTMs) and vegetation canopy height models (CHMs) are used in a wide range of earth and environmental sciences. An increasing number of CHM products are available from active, passive, and photogrammetric remotely sensed data; however, high-resolution (≤5 m), wall-to-wall CHMs for the arctic and northern boreal domains that are suitable for detailed spatial analysis are lacking. Recently, a 5-m spatial resolution pan-arctic digital surface model – the ArcticDEM – was created using automated stereopair analysis of high-resolution satellite data. The ArcticDEM is unprecedented in extent and spatial resolution, yet the product generally follows the uppermost surface elevation (i.e., representing a digital surface model, DSM) without regard to whether the surface is comprised of vegetation or bare-earth terrain. To address this limitation, we developed and tested an approach to map vegetation canopy height at a 5-m spatial resolution (hereafter called the local ArcticCHM), and then subtracted these estimated canopy heights from the ArcticDEM in order to create a 5-m resolution DTM (local ArcticDTM). We selected three pilot study areas (total 58 km2) across a north-south gradient in Alaska, representing a range of vegetation types and topographic conditions. We estimated and mapped canopy height using randomForest and imputation modeling approaches, with the ArcticDEM and high spatial resolution multispectral satellite data (WorldView-2) used as predictors. Airborne laser scanning (ALS) data was used for model calibration and independent validation. Canopy height was reliably predicted across the three study areas, with the best models ranging from root mean square errors (RMSE) 2.2 to 2.6 m and R2 ranging from 0.59 to 0.76 relative to ALS-based CHM reference data. Similarly, the RMSE between the new local ArcticDTM product and ALS-based DTM reference data was 45–68% less than similar comparisons with the ArcticDEM. Our results offer a means to extend these local ArcticDTM and CHM products to establish high-resolution products elsewhere in Alaska of high value for a wide range of earth and environmental sciences research investigations.

Background Temperatures in arctic-boreal regions are increasing rapidly and pose significant challenges to moose (Alces alces), a heat-sensitive large-bodied mammal. Moose act as ecosystem engineers, by regulating forest carbon and structure, below ground nitrogen cycling processes, and predator-prey dynamics. Previous studies showed that during hotter periods, moose displayed stronger selection for wetland habitats, taller and denser forest canopies, and minimized exposure to solar radiation. However, previous studies regarding moose behavioral thermoregulation occurred in Europe or southern moose range in North America. Understanding whether ambient temperature elicits a behavioral response in high-northern latitude moose populations in North America may be increasingly important as these arctic-boreal systems have been warming at a rate two to three times the global mean. Methods We assessed how Alaska moose habitat selection changed as a function of ambient temperature using a step-selection function approach to identify habitat features important for behavioral thermoregulation in summer (June–August). We used Global Positioning System telemetry locations from four populations of Alaska moose (n = 169) from 2008 to 2016. We assessed model fit using the quasi-likelihood under independence criterion and conduction a leave-one-out cross validation. Results Both male and female moose in all populations increasingly, and nonlinearly, selected for denser canopy cover as ambient temperature increased during summer, where initial increases in the conditional probability of selection were initially sharper then leveled out as canopy density increased above ~ 50%. However, the magnitude of selection response varied by population and sex. In two of the three populations containing both sexes, females demonstrated a stronger selection response for denser canopy at higher temperatures than males. We also observed a stronger selection response in the most southerly and northerly populations compared to populations in the west and central Alaska. Conclusions The impacts of climate change in arctic-boreal regions increase landscape heterogeneity through processes such as increased wildfire intensity and annual area burned, which may significantly alter the thermal environment available to an animal. Understanding habitat selection related to behavioral thermoregulation is a first step toward identifying areas capable of providing thermal relief for moose and other species impacted by climate change in arctic-boreal regions.

Recent advancements in understanding remotely sensed solar‐induced chlorophyll fluorescence often suggest a linear relationship with gross primary productivity at large spatial scales. However, the quantum yields of fluorescence and photochemistry are not linearly related, and this relationship is largely driven by irradiance. This raises questions about the mechanistic basis of observed linearity from complex canopies that experience heterogeneous irradiance regimes at subcanopy scales. We present empirical data from two evergreen forest sites that demonstrate a nonlinear relationship between needle‐scale observations of steady‐state fluorescence yield and photochemical yield under ambient irradiance. We show that accounting for subcanopy and diurnal patterns of irradiance can help identify the physiological constraints on needle‐scale fluorescence at 70–80% accuracy. Our findings are placed in the context of how solar‐induced chlorophyll fluorescence observations from spaceborne sensors relate to diurnal variation in canopy‐scale physiology.

Ecological “big data” Human activities are rapidly altering the natural world. Nowhere is this more evident, perhaps, than in the Arctic, yet this region remains one of the most remote and difficult to study. Researchers have increasingly relied on animal tracking data in these regions to understand individual species' responses, but if we want to understand larger-scale change, we need to integrate our understanding across species. Davidson et al. introduce an open-source data archive that currently hosts more than 15 million location data points across 96 species and use it to show distinct climate change responses across species. Such ecological “big data” can lead to a wider understanding of change. Science , this issue p. 712

Conserving multiple facets of biodiversity is important for sustaining ecosystems. However, understanding relationships between faunal diversity and measurable ecosystem quantities, such as heterogeneity and productivity, across continental scales can be complicated by disparate methods. We developed standardized approaches using lidar data and spectral greenness data (via NDVI; Normalized Difference Vegetation Index) from 637 sampling plots across four sites in North America, Europe, and Asia to test the local effects of habitat heterogeneity and productivity on taxonomic, functional, and phylogenetic diversity of breeding bird assemblages using boosted generalized additive models. Our results revealed the 3-D (three dimensional) vegetation structure (horizontal and vertical) to be of similar importance as NDVI in multiple biodiversity measures, and the importance of 3-D structure was higher for functional and phylogenetic biodiversity measures than for taxonomic measures. We found congruent responses between functional and phylogenetic diversity; however, patterns of taxonomic diversity differed from those of functional/phylogenetic diversity for most predictors. For example, NDVI had positive relationships with taxonomic diversity, but negative relationships with functional/phylogenetic diversity. The effect of canopy density on taxonomic diversity was generally bell-shaped, whereas the relationship was U-shaped for functional and phylogenetic diversity. As a result, this study supports a silviculture strategy with a high variety of canopy densities and vertical variabilities across forest stands to create maximum benefits for regional biodiversity. Here, early succession stands and closed stands sustain functionally-rich bird assemblages, while stands with a medium canopy density promote species-rich assemblages.

Northern herbivore ranges are expanding in response to a warming climate. Forage quality also influences herbivore distributions, but less is known about the effects of climate change on plant biochemical properties. Remote sensing could enable landscape-scale estimations of forage quality, which is of interest to wildlife managers. Despite the importance of integrated forage quality metrics like digestible protein (DP) and digestible dry matter (DDM), few studies investigate remote sensing approaches to estimate these characteristics. We evaluated how well DP and DDM could be estimated using hyperspectral remote sensing and assessed whether incorporating shrub structural metrics affected by browsing would improve our ability to predict DP and DDM. We collected canopy-level spectra, destructive-vegetation samples, and flew unoccupied aerial vehicles (UAVs) in willow (Salix spp.) dominated areas in north central Alaska in July 2019. We derived vegetation canopy structural metrics from 3D point cloud data obtained from UAV imagery using structure-from-motion photogrammetry. The best performing model for DP included a spectral vegetation index (SVI) that used a red-edge and shortwave infrared band, and shrub height variability (hvar; Nagelkerke R 2 = 0.81, root mean square error RMSE = 1.42%, cross validation ρ = 0.88). DDM’s best model included a SVI with a blue and a red band, the normalized difference red-edge index, and hvar (adjusted R 2 = 0.73, RMSE = 4.16%, cross validation ρ = 0.80). Results from our study demonstrate that integrated forage quality metrics may be successfully quantified using hyperspectral remote sensing data, and that models based on those data may be improved by incorporating additional shrub structural metrics such as height variability. Modern airborne sensor platforms such as Goddard’s LiDAR, Hyperspectral & Thermal Imager provide opportunities to fuse data streams from both structural and optical data, which may enhance our ability to estimate and scale important foliar properties.

Ecologists have a long-term interest in understanding the relative influence of vegetation composition and vegetation structure on avian diversity. LiDAR remote sensing is useful in studying local patterns of avian diversity because it characterizes fine-scale vegetation structure across broad extents. We used LiDAR, aerial and satellite imagery, and avian field data to investigate the relative influence of vegetation structure and canopy composition on avian richness across an aspen-conifer forest gradient. Aspen enhances forest avian biodiversity but has been declining across western North America. We conducted bird surveys between 2013 and 2014 in plots with a range of aspen-conifer canopy composition. We found aspen to support higher avian richness than conifer, especially among cavity nesters. In contrast to other studies, we found weak relationships between vegetation structure and avian richness. Although primary cavity excavator richness was negatively influenced by canopy density, canopy ...

When the capacity for photosynthesis is constrained by unfavorable growing conditions, excess absorbed light is safely lost from leaves via thermal energy dissipation—a photoprotective mechanism ubiquitous among higher plants. The relatively low irradiance conditions yet stressful growing environment of the arctic tundra suggest contrasting hypotheses regarding the necessity for plant investment in photoprotection. To examine these hypotheses, the photoprotective pigments of the xanthophyll cycle were investigated in conjunction with non-photochemical quenching (NPQ) of chlorophyll fluorescence emission in two dominant arctic shrub species, Salix pulchra and Betula nana. The xanthophyll cycle pool sizes of S. pulchra leaves were substantially higher than those reported in most other higher plant species, whereas B. nana leaves maintain modestly high xanthophyll cycle pool sizes. In addition, high retention of de-epoxidized xanthophyll cycle pigments in both species and saturation of xanthophyll c...

Quantifying relationships between plant functional traits and abiotic gradients is valuable for evaluating potential responses of forest communities to climate change. However, the trajectories of change expected to occur in tropical forest functional characteristics as a function of future climate variation are largely unknown. We modeled community level trait values of Costa Rican rain forests as a function of current and future climate, and quantified potential changes in functional composition. We calculated per-plot community weighted mean (CWM) trait values for leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), leaf nitrogen (N) and phosphorus (P) content, and wood basic specific gravity (WSG), for tree and palm species in 127 0.25 ha plots. We modeled the response of CWM traits to current temperature and precipitation gradients using generalized additive modeling. We then predicted and mapped CWM traits values under current and future climate, and quantified potential changes under a global warming scenario (RCP8.5, year 2050). We calculated the area within the multi trait functional space occupied by forest plots under both current and future climate, and determined potential changes in functional space occupied by forest plots. Overall, precipitation predicted CWM traits better than temperature. Models indicated increases in CWM SLA, N and P, and a decrease in CWM LDMC under climate change. Lowland forest communities converged on a single direction of change towards more acquisitive CWM trait values, indicating a change in forest functional composition resulting from a changed climate. Functional space occupied by forest plots was reduced by 50% under the future climate. Functional composition changes may have further effects on forests ecosystem services. Assessing functional trait spatial-gradients can help bridge the gap between species-based biogeography and biogeochemical approaches to strengthen biodiversity and ecosystem services conservation efforts. This article is protected by copyright. All rights reserved.

Strategic planning to increase forest cover in Central American transboundary areas of the Mesoamerican Biological Corridor requires understanding land-cover and land-use change trends and drivers. We estimated forest cover change from remotely sensed land-cover and land-use classifications from 1986, 2001, and 2010, in the tri-national Trifinio Region, bordering El Salvador, Guatemala, and Honduras. Our analysis spanned subnational, national, regional, and protected border areas. We determined correlations with direct drivers of deforestation, developing a multilevel linear regression model. Higher population density significantly correlated with deforestation; coffee, agroforestry, and pasture replaced forests. The tri-national park retained forests compared to neighboring areas, but additionality requires more research. The literature on drivers suggests similar processes and factors in other tropical regions. Forest cover governance efficacy is highly variable. Results indicate relationship be...

Fungi are crucial to forest ecosystem function and provide important provisioning, regulating, supporting, and cultural ecosystem services. As major contributors to biomass decomposition, fungi are important to forest biogeochemical cycling and maintenance of vertebrate animal diversity. Many forest plant species live in a symbiotic relationship with a fungal partner that helps a host plant to acquire nutrients and water. In addition, edible fungi are recreationally as well as economically valuable. However, most fungi live in very cryptic locations (e.g. in soils and interior plant tissues) and are only visible when their ephemeral fruiting bodies are produced, making fungal occurrence difficult to detect and predict. While remote sensing has been used increasingly to identify and scale many forest characteristics (e.g. structure, function, and species diversity) related to myriad ecosystem services, the use of remotely sensed data in modelling the occurrence of fungi is largely unknown. We compared the performance of airborne lidar derived structural variables, including those associated with single tree detection, with variables derived from field inventories to model overall fungal species abundance as well as specific fungal guilds (i.e. a range of edibility from highly edible to very poisonous, and the number of fruiting bodies of saprotrophic and mutualistic ectomycorrhizal species) based on fruiting body sampling in a low range mountain forest (Bavarian Forest National Park). Lidar derived variables performed better than variables derived from field measurements to explain the abundance of all guilds combined, as well as the guilds of soil saprotrophic and ectomycorrhizal fungi, and the yield of highly edible fungi. Variables derived from field measurements performed better than lidar derived variables in explaining the yield of very poisonous fungi. Upscaling of yield and abundance of fruiting bodies to the whole study area opens the avenue for managers to identify areas of high interest by mushroom pickers, as opposed to those of potential danger to people and those that co-occur with sensitive species and habitats of conservation relevance. Moreover, the strong, guild-specific relationships found between the occurrence of fungi and lidar derived variables opens new avenues for scaling to large areas the occurrence of members of this cryptic kingdom.

Capturing and quantifying the world in three dimensions (x,y,z) using light detection and ranging (lidar) technology drives fundamental advances in the Earth and Ecological Sciences (EES). However, additional lidar dimensions offer the possibility to transcend basic 3-D mapping capabilities, including i) the physical time (t) dimension from repeat lidar acquisition and ii) laser return intensity (LRIλ) data dimension based on the brightness of single- or multi-wavelength (λ) laser returns. The additional dimensions thus add to the x,y, and z dimensions to constitute the five dimensions of lidar (x,y,z, t, LRIλ1… λn). This broader spectrum of lidar dimensionality has already revealed new insights across multiple EES topics, and will enable a wide range of new research and applications. Here, we review recent advances based on repeat lidar collections and analysis of LRI data to highlight novel applications of lidar remote sensing beyond 3-D. Our review outlines the potential and current challenges of time and LRI information from lidar sensors to expand the scope of research applications and insights across the full range of EES applications.

Crop biomass information is of great importance for a variety of applications, ranging from supporting farm management decisions to modeling the crop-environment system. Dimensionless spectral vegetation index values derived from satellite imagery are commonly used to derive crop biomass. However, the highly empirical nature of spectrally derived biomass estimates requires frequent and costly calibration with manually collected ground data. Recently, low cost, autonomously operating terrestrial laser scanners (ATLSs) have become available for near-surface applications. In contrast to the dimensionless nature of spectral index values, autonomous light detection and ranging (lidar) technology measures physical vegetation structure by recording the x, y, z coordinates of canopy components at very high spatial (

The Landsat 8 mission provides new opportunities for quantifying the distribution of above-ground carbon at moderate spatial resolution across the globe, and in particular drylands. Furthermore, coupled with structural information from space-based and airborne laser altimetry, Landsat 8 provides powerful capabilities for large-area, long-term studies that quantify temporal and spatial changes in above-ground biomass and cover. With the planned launch of ICESat-2 in 2017 and thus the potential to couple Landsat 8 and ICESat-2 data, we have unprecedented opportunities to address key challenges in drylands, including quantifying fuel loads, habitat quality, biodiversity, carbon cycling, and desertification. In this study, we explore the strengths of Landsat 8's Operational Land Imager (OLI) in estimating vegetation structure in a dryland ecosystem, and compare these results to Landsat 5's Thematic Mapper (TM). We also demonstrate the potential of OLI when coupled with light detection and ranging (lidar) in estimating vegetation cover and biomass in a dryland ecosystem. The OLI and TM predictions were similarly positive, indicating data from these sensors may be used in tandem for long-term time-series analysis. Results indicate shrub and herbaceous cover are well predicted with multi-temporal OLI data, and a combination of OLI and lidar derivatives improves most of these estimates and reduces uncertainty. For example, significant improvements were made for shrub cover (R2 = 0.64 and 0.78 using OLI only and both OLI and lidar data, respectively). Importantly, a time series of OLI, with some improvement from lidar, provides strong estimates of herbaceous cover (68% of the variance is explained with OLI alone). In contrast, OLI data explain roughly 59% of the variance in total shrub biomass, however approximately 71% of the variance is explained when combined with lidar derivatives. To estimate the potential synergies of OLI and ICESat-2 we used simulated ICESat-2 photon data to predict vegetation structure. In a shrubland environment with a vegetation mean height of 1 m and mean vegetation cover of 33%, vegetation photons are able to explain nearly 50% of the variance in vegetation height. These results, and those from a comparison site, suggest that a lower detection threshold of ICESat-2 may be in the range of 30% canopy cover and roughly 1 m height in comparable dryland environments and these detection thresholds could be used to combine future ICESat-2 photon data with OLI spectral data for improved vegetation structure. Overall, the synergistic use of Landsat 8 and ICESat-2 may improve estimates of above-ground biomass and carbon storage in drylands that meet these minimum thresholds, increasing our ability to monitor drylands for fuel loading and the potential to sequester carbon.

Understanding how species functional traits relate to the delivery of ecosystem services is essential to support on-going biodiversity conservation efforts. While much recent work has been conducted, relatively few studies relating functional ecology to ecosystem services has utilized field experiments, particularly for animal species. We used a functional diversity (FD) approach to study the effect of bird traits on the control of the coffee berry borer (Hypothenemus hampei) and the effect of canopy cover management on bird FD. We conducted an exclosure experiment to test the effect of bird FD on the difference of H. hampei infestation rates between coffee shrubs both exposed to, and excluded from bird foraging activity. We addressed the following questions: (1) is avian FD, at the plot level, a good predictor of H. hampei infestation? (2) do gleaner bird species contribute to the control of the pest? and (3) how does shade management affect avian FD? We found that (1) all four FD indices calculated using bird traits were significant predictors of H. hampei differences in infestation rates, (2) richness of gleaning bird species was also a significant predictor of differences in H. hampei infestation rates, and (3) the interaction between month and canopy cover management affects bird FD, however whether this affects delivery of the pest control service remains unclear due to the particular biology of H. hampei. In revealing the connection between avian traits and the removal and potential control of H. hampei, our study highlights the importance of bird diversity persisting in agricultural landscapes, and the necessity of integrating bird conservation to foster healthy production systems.

Monitoring of climate-driven expansion of low-stature shrubs in Arctic tundra can be improved through application of high-resolution remote sensing. However, the destructive nature of harvest sampling that is usually performed for validation of these data is resource intensive and can limit future comparisons by destroying benchmark measurements. We compared aboveground shrub biomass estimates derived from terrestrial laser scanning (TLS) and airborne laser scanning (ALS) with the goal of determining whether TLS data can be used to accurately calibrate ALS estimates of shrub biomass in Arctic tundra. We used a leave-one-out cross-validation calibration of canopy volume against harvested shrub biomass to establish predictive relationships between TLS canopy volume and harvested shrub biomass, and between ALS canopy volume and TLS-derived shrub biomass estimates. TLS produced more accurate predictions of shrub biomass (R2 = 0.78; root mean square deviation [RMSD] = 102 g) than did ALS, but the accur...

The effects of climate change are more acute in the Arctic than any other region, and as such, arctic tundra wildlife habitats are changing in ways that are not yet well understood. Remote sensing tools are capable of assessing dynamics in wildlife-habitat associations over a wide range of spatial scales and in a variety of ecosystems. As already well-established in other ecosystems, Light Detection and Ranging (LiDaR) technology has the potential to greatly expand our understanding of tundra wildlife-habitat associations because unlike the most commonly used spectral vegetation index - the Normalized Difference Vegetation Index (NDVI) - LiDaR directly quantifies three-dimensional (3-D) vegetation structure. Only recently has airborne laser scanning (ALS) technology evolved to having vertical resolution and point densities high enough to quantify small differences in vegetation structure, such as characterize arctic tundra ecosystems. Our objective was to employ ALS metrics, airborne spectral data (for NDVI), and Landsat 7 ETM + data (for snow cover) to determine the relative importance of a suite of ecological characteristics - considered at multiple spatial scales - on habitat use and reproductive success of two of the most common migratory songbird species breeding in northern Alaska. Our most important findings are that (1) combining ALS metrics, but not NDVI, with Landsat derived snow cover data provided useful information, revealing that while Gambel's white-crowned sparrows use breeding territories in areas with high canopy volume and large snow-free areas, Lapland longspurs will establish territories in areas with low canopy volume, small patches of snow-free tundra, and minimal surface wetness and; (2) while habitat characteristics were important determinants of habitat use for both species at the territory scale, those in the immediate vicinity of nest-sites were not important. Contrary to expectation, we also found that the reproductive success of both species was unaffected by variation in our hypothesized metrics of shelter and food availability (canopy volume, standard deviation of micro-terrain height) at both the nest-site or territory scales. Our study is the first arctic demonstration of how ALS yields novel insights into wildlife-habitat associations, suggesting it has great potential in other ecosystems with similarly small - yet often ecologically important - ranges in 3-D vegetation structure.

Accurate monitoring of climate-driven expansion of low-stature shrubs in Arctic tundra requires high-resolution maps of shrub biomass that can accurately quantify the current baseline over relevant spatial and temporal extents. In this study, our goal was to use airborne lidar and imagery to build accurate high-resolution shrub biomass maps for an important research landscape in the American Arctic. In a leave-one-out cross-validation analysis, optimized lidar-derived canopy volume was a good single predictor of harvested shrub biomass (R2 = 0.62; RMSD = 219 g m− 2; slope = 1.08). However, model accuracy was improved by incorporating additional lidar-derived canopy metrics and airborne spectral metrics in a Random Forest regression approach (pseudo R2 = 0.71; RMSD = 197 g m− 2; slope = 1.02). The best Random Forest model was used to map shrub biomass at 0.80 m resolution across three lidar collection footprints (~ 12.5 km2 total) near Toolik Field Station on Alaska's North Slope. We characterized model uncertainty by creating corresponding maps of the coefficient of variation in Random Forest shrub biomass estimates. We also explore potential benefits of incorporating lidar-derived topographic metrics, and consider tradeoffs inherent in employing different data sources for high-resolution vegetation mapping efforts. This study yielded maps that provide valuable, high-resolution spatial estimates of aboveground shrub biomass and canopy volume in a rapidly changing tundra ecosystem.