The role of microtopography and resident species in post-disturbance recovery of arid habitats in Hawai'i.

Habitat-suitability indices (HSI) have been employed in restoration to identify optimal sites for planting native species. Often, HSI are based on abiotic variables and do not include biotic interactions, even though similar abiotic conditions can favor both native and nonnative species. Biotic interactions such as competition may be especially important in invader-dominated habitats because invasive species often have fast growth rates and can exploit resources quickly. In this study, we test the utility of an HSI of microtopography derived from airborne LiDAR to predict post-disturbance recovery and native planting success in native shrub-dominated and nonnative, invasive grass-dominated dryland habitats in Hawai'i. The HSI uses high-resolution digital terrain models to classify sites' microtopography as high, medium, or low suitability, based on wind exposure and topographic position. We used a split-plot before-after-control-impact design to implement a disturbance experiment within native shrub (Dodonaea viscosa) and nonnative, invasive grass (Cenchrus clandestinus)-dominated ecosystems across three microtopography categories. In contrast to previous studies using the same HSI, we found that microtopography was a poor predictor of pre-disturbance conditions for soil nutrients, organic matter content, or foliar C:N, within both Dodonaea and Cenchrus vegetation types. In invader-dominated Cenchrus plots, microtopography helped predict cover, but not as expected (i.e., highest cover would be in high-suitability plots): D. viscosa had the greatest cover in low-suitability and C. clandestinus had the greatest cover in medium-suitability plots. Similarly, in native-dominated Dodonaea plots, microtopography was a poor predictor of D. viscosa, C. clandestinus, and total plant cover. Although we found some evidence that microtopography helped inform post-disturbance plant recovery of D. viscosa and total plant cover, vegetation type was a more important predictor. Important for considering the success of plantings, percent cover of D. viscosa decreased while percent cover of C. clandestinus increased within both vegetation types 20 months after disturbance. Our results are evidence that HSIs based on topographic features may prove most useful for choosing planting sites in harsh habitats or those already dominated by native species. In more productive habitats, competition from resident species may offset any benefits gained from "better" suitability sites.

Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science.

For decades, the dynamic nature of chlorophyll a fluorescence (ChlaF) has provided insight into the biophysics and ecophysiology of the light reactions of photosynthesis from the subcellular to leaf scales. Recent advances in remote sensing methods enable detection of ChlaF induced by sunlight across a range of larger scales, from using instruments mounted on towers above plant canopies to Earth-orbiting satellites. This signal is referred to as solar-induced fluorescence (SIF) and its application promises to overcome spatial constraints on studies of photosynthesis, opening new research directions and opportunities in ecology, ecophysiology, biogeochemistry, agriculture and forestry. However, to unleash the full potential of SIF, intensive cross-disciplinary work is required to harmonize these new advances with the rich history of biophysical and ecophysiological studies of ChlaF, fostering the development of next-generation plant physiological and Earth-system models. Here, we introduce the scale-dependent link between SIF and photosynthesis, with an emphasis on seven remaining scientific challenges, and present a roadmap to facilitate future collaborative research towards new applications of SIF.

Impact of a tropical forest blowdown on aboveground carbon balance.

Field measurements demonstrate a carbon sink in the Amazon and Congo basins, but the cause of this sink is uncertain. One possibility is that forest landscapes are experiencing transient recovery from previous disturbance. Attributing the carbon sink to transient recovery or other processes is challenging because we do not understand the sensitivity of conventional remote sensing methods to changes in aboveground carbon density (ACD) caused by disturbance events. Here we use ultra-high-density drone lidar to quantify the impact of a blowdown disturbance on ACD in a lowland rain forest in Costa Rica. We show that the blowdown decreased ACD by at least 17.6%, increased the number of canopy gaps, and altered the gap size-frequency distribution. Analyses of a canopy-height transition matrix indicate departure from steady-state conditions. This event will initiate a transient sink requiring an estimated 24-49 years to recover pre-disturbance ACD. Our results suggest that blowdowns of this magnitude and extent can remain undetected by conventional satellite optical imagery but are likely to alter ACD decades after they occur.

Genome assemblies for two Neotropical trees: Jacaranda copaia and Handroanthus guayacan.

The lack of genomic resources for tropical canopy trees is impeding several research avenues in tropical forest biology. We present genome assemblies for two Neotropical hardwood species, Jacaranda copaia and Handroanthus (formerly Tabebuia) guayacan, that are model systems for research on tropical tree demography and flowering phenology. For each species, we combined Illumina short-read data with in vitro proximity-ligation (Chicago) libraries to generate an assembly. For Jacaranda copaia, we obtained 104X physical coverage and produced an assembly with N50/N90 scaffold lengths of 1.020/0.277 Mbp. For H. guayacan, we obtained 129X coverage and produced an assembly with N50/N90 scaffold lengths of 0.795/0.165 Mbp. J. copaia and H. guayacan assemblies contained 95.8% and 87.9% of benchmarking orthologs, although they constituted only 77.1% and 66.7% of the estimated genome sizes of 799 and 512 Mbp, respectively. These differences were potentially due to high repetitive sequence content (>59.31% and 45.59%) and high heterozygosity (0.5% and 0.8%) in each species. Finally, we compared each new assembly to a previously sequenced genome for Handroanthus impetiginosus using whole-genome alignment. This analysis indicated extensive gene duplication in H. impetiginosus since its divergence from H. guayacan.

Diversity, distribution and dynamics of large trees across an old-growth lowland tropical rain forest landscape.

Large trees, here defined as ≥60 cm trunk diameter, are the most massive organisms in tropical rain forest, and are important in forest structure, dynamics and carbon cycling. The status of large trees in tropical forest is unclear, with both increasing and decreasing trends reported. We sampled across an old-growth tropical rain forest landscape at the La Selva Biological Station in Costa Rica to study the distribution and performance of large trees and their contribution to forest structure and dynamics. We censused all large trees in 238 0.50 ha plots, and also identified and measured all stems ≥10 cm diameter in 18 0.50 ha plots annually for 20 years (1997-2017). We assessed abundance, species diversity, and crown conditions of large trees in relation to soil type and topography, measured the contribution of large trees to stand structure, productivity, and dynamics, and analyzed the decadal population trends of large trees. Large trees accounted for 2.5% of stems and ~25% of mean basal area and Estimated Above-Ground Biomass, and produced ~10% of the estimated wood production. Crown exposure increased with stem diameter but predictability was low. Large tree density was about twice as high on more-fertile flat sites compared to less fertile sites on slopes and plateaus. Density of large trees increased 27% over the study interval, but the increase was restricted to the flat more-fertile sites. Mortality and recruitment differed between large trees and smaller stems, and strongly suggested that large tree density was affected by past climatic disturbances such as large El Niño events. Our results generally do not support the hypothesis of increasing biomass and turnover rates in tropical forest. We suggest that additional landscape-scale studies of large trees are needed to determine the generality of disturbance legacies in tropical forest study sites.

New Opportunities for Forest Remote Sensing Through Ultra-High-Density Drone Lidar.

Current and planned space missions will produce aboveground biomass density data products at varying spatial resolution. Calibration and validation of these data products is critically dependent on the existence of field estimates of aboveground biomass and coincident remote sensing data from airborne or terrestrial lidar. There are few places that meet these requirements, and they are mostly in the northern hemisphere and temperate zone. Here we summarize the potential for low-altitude drones to produce new observations in support of mission science. We describe technical requirements for producing high-quality measurements from autonomous platforms and highlight differences among commercially available laser scanners and drone aircraft. We then describe a case study using a heavy-lift autonomous helicopter in a temperate mountain forest in the southern Czech Republic in support of calibration and validation activities for the NASA Global Ecosystem Dynamics Investigation. Low-altitude flight using drones enables the collection of ultra-high-density point clouds using wider laser scan angles than have been possible from traditional airborne platforms. These measurements can be precise and accurate and can achieve measurement densities of thousands of points · m-2. Analysis of surface elevation measurements on a heterogeneous target observed 51 days apart indicates that the realized range accuracy is 2.4 cm. The single-date precision is 2.1-4.5 cm. These estimates are net of all processing artifacts and geolocation errors under fully autonomous flight. The 3D model produced by these data can clearly resolve branch and stem structure that is comparable to terrestrial laser scans and can be acquired rapidly over large landscapes at a fraction of the cost of traditional airborne laser scanning.

The GEDI Simulator: A Large-Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions.

NASA's Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar mission which will produce near global (51.6°S to 51.6°N) maps of forest structure and above-ground biomass density during its 2-year mission. GEDI uses a waveform simulator for calibration of algorithms and assessing mission accuracy. This paper implements a waveform simulator, using the method proposed in Blair and Hofton (1999; https://doi.org/10.1029/1999GL010484), and builds upon that work by adding instrument noise and by validating simulated waveforms across a range of forest types, airborne laser scanning (ALS) instruments, and survey configurations. The simulator was validated by comparing waveform metrics derived from simulated waveforms against those derived from observed large-footprint, full-waveform lidar data from NASA's airborne Land, Vegetation, and Ice Sensor (LVIS). The simulator was found to produce waveform metrics with a mean bias of less than 0.22 m and a root-mean-square error of less than 5.7 m, as long as the ALS data had sufficient pulse density. The minimum pulse density required depended upon the instrument. Measurement errors due to instrument noise predicted by the simulator were within 1.5 m of those from observed waveforms and 70-85% of variance in measurement error was explained. Changing the ALS survey configuration had no significant impact on simulated metrics, suggesting that the ALS pulse density is a sufficient metric of simulator accuracy across the range of conditions and instruments tested. These results give confidence in the use of the simulator for the pre-launch calibration and performance assessment of the GEDI mission.

Prediction of forest aboveground net primary production from high-resolution vertical leaf-area profiles.

Temperature and precipitation explain about half the variation in aboveground net primary production (ANPP) among tropical forest sites, but determinants of remaining variation are poorly understood. Here, we test the hypothesis that the amount of leaf area, and its vertical arrangement, predicts ANPP when other variables are held constant. Using measurements from airborne lidar in a lowland Neotropical rain forest, we quantify vertical leaf-area profiles and develop models of ANPP driven by leaf area and other measurements of forest structure. Vertical leaf-area profiles predict 38% of the variation among plots. This number is 4.5 times greater than models using total leaf area (disregarding vertical arrangement) and 2.1 times greater than models using canopy height alone. Furthermore, ANPP predictions from vertical leaf-area profiles were less biased than alternate metrics. Variation in ANPP not attributable to temperature or precipitation can be predicted by the vertical distribution of leaf area in this system.

Density-dependent adult recruitment in a low-density tropical tree.

The Janzen-Connell hypothesis is a well-known explanation for why tropical forests have large numbers of tree species. A fundamental prediction of the hypothesis is that the probability of adult recruitment is less in regions of high conspecific adult density, a pattern mediated by density-dependent mortality in juvenile life stages. Although there is strong evidence in many tree species that seeds, seedlings, and saplings suffer conspecific density-dependent mortality, no study has shown that adult tree recruitment is negatively density dependent. Density-dependent adult recruitment is necessary for the Janzen-Connell mechanism to regulate tree populations. Here, we report density-dependent adult recruitment in the population of Handroanthus guayacan, a wind-dispersed Neotropical canopy tree species. We use data from high-resolution remote sensing to track individual trees with proven capacity to flower in a lowland moist forest landscape in Panama and analyze these data in a Bayesian framework similar to capture-recapture analysis. We independently quantify probabilities of adult tree recruitment and detection and show that adult recruitment is negatively density dependent. The annualized probability of adult recruitment was 3.03% ⋅ year-1 Despite the detection of negative density dependence in adult recruitment, it was insufficient to stabilize the adult population of H. guayacan, which increased significantly in size over the decade of observation.

Chronosequence predictions are robust in a Neotropical secondary forest, but plots miss the mark.

Tropical secondary forests (TSF) are a global carbon sink of 1.6 Pg C/year. However, TSF carbon uptake is estimated using chronosequence studies that assume differently aged forests can be used to predict change in aboveground biomass density (AGBD) over time. We tested this assumption using two airborne lidar datasets separated by 11.5 years over a Neotropical landscape. Using data from 1998, we predicted canopy height and AGBD within 1.1 and 10.3% of observations in 2009, with higher accuracy for forest height than AGBD and for older TSFs in comparison to younger ones. This result indicates that the space-for-time assumption is robust at the landscape-scale. However, since lidar measurements of secondary tropical forest are rare, we used the 1998 lidar dataset to test how well plot-based studies quantify the mean TSF height and biomass in a landscape. We found that the sample area required to produce estimates of height or AGBD close to the landscape mean is larger than the typical area sampled in secondary forest chronosequence studies. For example, estimating AGBD within 10% of the landscape mean requires more than thirty 0.1 ha plots per age class, and more total area for larger plots. We conclude that under-sampling in ground-based studies may introduce error into estimations of the TSF carbon sink, and that this error can be reduced by more extensive use of lidar measurements.

Multidecadal stability in tropical rain forest structure and dynamics across an old-growth landscape.

Have tropical rain forest landscapes changed directionally through recent decades? To answer this question requires tracking forest structure and dynamics through time and across within-forest environmental heterogeneity. While the impacts of major environmental gradients in soil nutrients, climate and topography on lowland tropical rain forest (TRF) structure and function have been extensively analyzed, the effects of the shorter environmental gradients typical of mesoscale TRF landscapes remain poorly understood. To evaluate multi-decadal performance of an old-growth TRF at the La Selva Biological Station, Costa Rica, we established 18 0.5-ha annually-censused forest inventory plots in a stratified-random design across major landscape edaphic gradients. Over the 17-year study period, there were moderate differences in stand dynamics and structure across these gradients but no detectable difference in woody productivity. We found large effects on forest structure and dynamics from the mega-Niño event at the outset of the study, with subdecadal recovery and subsequent stabilization. To extend the timeline to >40 years, we combined our findings with those from earlier studies at this site. While there were annual to multiannual variations in the structure and dynamics, particularly in relation to local disturbances and the mega-Niño event, at the longer temporal scale and broader spatial scale this landscape was remarkably stable. This stability contrasts notably with a current hypothesis of increasing biomass and dynamics of TRF, which we term the Bigger and Faster Hypothesis (B&FHo). We consider possible reasons for the contradiction and conclude that it is currently not possible to independently assess the vast majority of previously published B&FHo evidence due to restricted data access.

Adult mortality in a low-density tree population using high-resolution remote sensing.

We developed a statistical framework to quantify mortality rates in canopy trees observed using time series from high-resolution remote sensing. By timing the acquisition of remote sensing data with synchronous annual flowering in the canopy tree species Handroanthus guayacan, we made 2,596 unique detections of 1,006 individual adult trees within 18,883 observation attempts on Barro Colorado Island, Panama (BCI) during an 11-yr period. There were 1,057 observation attempts that resulted in missing data due to cloud cover or incomplete spatial coverage. Using the fraction of 123 individuals from an independent field sample that were detected by satellite data (109 individuals, 88.6%), we estimate that the adult population for this species on BCI was 1,135 individuals. We used a Bayesian state-space model that explicitly accounted for the probability of tree detection and missing observations to compute an annual adult mortality rate of 0.2%·yr-1 (SE = 0.1, 95% CI = 0.06-0.45). An independent estimate of the adult mortality rate from 260 field-checked trees closely matched the landscape-scale estimate (0.33%·yr-1 , SE = 0.16, 95% CI = 0.12-0.74). Our proof-of-concept study shows that one can remotely estimate adult mortality rates for canopy tree species precisely in the presence of variable detection and missing observations.

Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy.

Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods--binary support vector machine (SVM) and biased SVM--for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer's accuracies of 94-97% for the three focal species, and field validation of the predicted crown objects indicated that these had user's accuracies of 94-100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems.

Primary Succession on a Hawaiian Dryland Chronosequence.

We used measurements from airborne imaging spectroscopy and LiDAR to quantify the biophysical structure and composition of vegetation on a dryland substrate age gradient in Hawaii. Both vertical stature and species composition changed during primary succession, and reveal a progressive increase in vertical stature on younger substrates followed by a collapse on Pleistocene-aged flows. Tall-stature Metrosideros polymorpha woodlands dominated on the youngest substrates (hundreds of years), and were replaced by the tall-stature endemic tree species Myoporum sandwicense and Sophora chrysophylla on intermediate-aged flows (thousands of years). The oldest substrates (tens of thousands of years) were dominated by the short-stature native shrub Dodonaea viscosa and endemic grass Eragrostis atropioides. We excavated 18 macroscopic charcoal fragments from Pleistocene-aged substrates. Mean radiocarbon age was 2,002 years and ranged from < 200 to 7,730. Genus identities from four fragments indicate that Osteomeles spp. or M. polymorpha once occupied the Pleistocene-aged substrates, but neither of these species is found there today. These findings indicate the existence of fires before humans are known to have occupied the Hawaiian archipelago, and demonstrate that a collapse in vertical stature is prevalent on the oldest substrates. This work contributes to our understanding of prehistoric fires in shaping the trajectory of primary succession in Hawaiian drylands.

Home range use and movement patterns of non-native feral goats in a tropical island montane dry landscape.

Advances in wildlife telemetry and remote sensing technology facilitate studies of broad-scale movements of ungulates in relation to phenological shifts in vegetation. In tropical island dry landscapes, home range use and movements of non-native feral goats (Capra hircus) are largely unknown, yet this information is important to help guide the conservation and restoration of some of the world's most critically endangered ecosystems. We hypothesized that feral goats would respond to resource pulses in vegetation by traveling to areas of recent green-up. To address this hypothesis, we fitted six male and seven female feral goats with Global Positioning System (GPS) collars equipped with an Argos satellite upload link to examine goat movements in relation to the plant phenology using the Normalized Difference Vegetation Index (NDVI). Movement patterns of 50% of males and 40% of females suggested conditional movement between non-overlapping home ranges throughout the year. A shift in NDVI values corresponded with movement between primary and secondary ranges of goats that exhibited long-distance movement, suggesting that vegetation phenology as captured by NDVI is a good indicator of the habitat and movement patterns of feral goats in tropical island dry landscapes. In the context of conservation and restoration of tropical island landscapes, the results of our study identify how non-native feral goats use resources across a broad landscape to sustain their populations and facilitate invasion of native plant communities.

Integrating physiological threshold experiments with climate modeling to project mangrove species' range expansion.

Predictions of climate-related shifts in species ranges have largely been based on correlative models. Due to limitations of these models, there is a need for more integration of experimental approaches when studying impacts of climate change on species distributions. Here, we used controlled experiments to identify physiological thresholds that control poleward range limits of three species of mangroves found in North America. We found that all three species exhibited a threshold response to extreme cold, but freeze tolerance thresholds varied among species. From these experiments, we developed a climate metric, freeze degree days (FDD), which incorporates both the intensity and the frequency of freezes. When included in distribution models, FDD accurately predicted mangrove presence/absence. Using 28 years of satellite imagery, we linked FDD to observed changes in mangrove abundance in Florida, further exemplifying the importance of extreme cold. We then used downscaled climate projections of FDD to project that these range limits will move northward by 2.2-3.2 km yr(-1) over the next 50 years.

Mapping habitat suitability for at-risk plant species and its implications for restoration and reintroduction.

The conservation of species at risk of extinction requires data to support decisions at landscape to regional scales. There is a need for information that can assist with locating suitable habitats in fragmented and degraded landscapes to aid the reintroduction of at-risk plant species. In addition, desiccation and water stress can be significant barriers to the success of at-risk plant reintroduction programs. We examine how airborne light detection and ranging (LiDAR) data can be used to model microtopographic features that reduce water stress and increase resource availability, providing information for landscape planning that can increase the success of reintroduction efforts for a dryland landscape in Hawaii. We developed a topographic habitat-suitability model (HSM) from LiDAR data that identifies topographic depressions that are protected from prevailing winds (high-suitability sites) and contrasts them with ridges and other exposed areas (low-suitability sites). We tested in the field whether high-suitability sites had microclimatic conditions that indicated better-quality habitat compared to low-suitability sites, whether plant-response traits indicated better growing conditions in high-suitability sites, whether the locations of individuals of existing at-risk plant species corresponded with our habitat-suitability classes, and whether the survival of planted individuals of a common native species was greater in high-suitability, compared to low-suitability, planting sites. Mean wind speed in a high-suitability field site was over five times lower than in a low-suitability site, and soil moisture and leaf wetness were greater, indicating less stress and greater resource availability in high-suitability areas. Plant height and leaf nutrient content were greater in high-suitability areas. Six at-risk species showed associations with high-suitability areas. The survival of planted individuals was less variable among high-suitability plots. These results suggest that plant establishment and survival is associated with the habitat conditions identified by our model. The HSM can improve the survival of planted individuals, reduce the cost of restoration and reintroduction programs through targeted management activities in high-suitability areas, and expand the ability of managers to make landscape-scale decisions regarding land-use, land acquisition, and species recovery.