Terrestrial carbon conservation can provide critical environmental, social, and climate benefits. Yet, the geographically complex mosaic of threats to, and opportunities for, conserving carbon in landscapes remain largely unresolved at national scales. Using a new high-resolution carbon mapping approach applied to Perú, a megadiverse country undergoing rapid land use change, we found that at least 0.8 Pg of aboveground carbon stocks are at imminent risk of emission from land use activities. Map-based information on the natural controls over carbon density, as well as current ecosystem threats and protections, revealed three biogeographically explicit strategies that fully offset forthcoming land-use emissions. High-resolution carbon mapping affords targeted interventions to reduce greenhouse gas emissions in rapidly developing tropical nations.
For tropical forest carbon to be commoditized, a consistent, globally verifiable system for reporting and monitoring carbon stocks and emissions must be achieved. We call for a global airborne LiDAR campaign that will measure the 3-D structure of each hectare of forested (and formerly forested) land in the tropics. We believe such a database could be assembled for only 5% of funding already pledged to offset tropical forest carbon emissions.
Biomass density is a key metric of vegetation abundance, but understanding how community assembly processes, such as environmental filtering and competitive exclusion, affect biomass distributions of coexisting species has proven logistically challenging. Here we apply airborne remote sensing to study the ecosystem-scale distribution of species-specific, woody plant biomass and its relation to topographic and hydrologic gradients in a South African savanna. We also spatially analyzed variation in biomass among species to understand patterns of coexistence, mapping the species and biomass over one million trees across 10500 ha. We found the biomass of dominant woody species to be weakly but significantly related to environmental filters, where a combination of 10 topographic and edaphic variables accounted for < 15% of the variance in the biomass of any given species. Distance to nearest stream was the only environmental variable significantly correlated to all species' biomass. Despite an overall negative trend observed between the biomass of species pairs, we found a number of regions where the biomass of two species was similar or equal, and all species pairs exhibited some level of co-occurrence. This suggests that even weak stabilizing mechanisms (e.g., environmental niches) can overcome fitness differences and balance competitive exclusion, enabling coexistence. Future work of repeated measurements of species-specific biomass will provide a novel advance in understanding woody plant community assembly processes in natural ecosystems. Characterizing the species composition of biomass is an important advance in understanding the balance of community assembly processes and its control over current species assemblages.
Tropical forests are important storehouses of carbon and biodiversity. In isolated island ecosystems such as the Hawaiian Islands, relative dominance of native and nonnative tree species may influence patterns of forest carbon stocks and biodiversity. We determined aboveground carbon density (ACD) across a matrix of lava flows differing in age, texture, and vegetation composition (i.e., native or nonnative dominated) in wet lowland forests of Hawaii Island. To do this at the large scales necessary to accurately capture the inherent heterogeneity of these forests, we collected LiDAR data across areas of interest and developed relationships between LiDAR metrics and field-based estimates of forest ACD. This approach enabled us to inventory, rather than merely sample, the entire populations (i.e., forests) of interest. Native Hawaiian wet lowland forests exhibited ACD values similar to those of intact tropical forests elsewhere. In general, ACD of these forests increased with increasing lava flow age, but patterns differed between native and nonnative forest stands. On the youngest lavas, native-dominated forest ACD averaged < 60 Mg/ha, compared to -100 Mg C/ha for nonnative-dominated forests. This difference was due to the presence of the nonnative, N2-fixing trees F. moluccana and C. equisetifolia in the nonnative-dominated forest stands, as well as the corresponding absence of N2-fixing trees in native-dominated forest stands. Following -500 years of primary succession and thereafter, however, both forest types exhibited ACD values averaging -130 Mg C/ha, although it took nonnative forests only 75 80 years of post-establishment succession to reach those values. Given the large areas of early-successional M. polymorpha-dominated forest on young lava flows, further spread of F. moluccana and C. equisetifolia populations would likely increase ACD stocks but would constitute a significant erosion of the invaluable contribution of Hawaii's native ecosystems to global biodiversity.
Theory and experiment agree that climate warming will increase carbon fluxes between terrestrial ecosystems and the atmosphere. The effect of this increased exchange on terrestrial carbon storage is less predictable, with important implications for potential feedbacks to the climate system. We quantified how increased mean annual temperature (MAT) affects ecosystem carbon storage in above- and belowground live biomass and detritus across a well-constrained 5.2 °C MAT gradient in tropical montane wet forests on the Island of Hawaii. This gradient does not systematically vary in biotic or abiotic factors other than MAT (i.e. dominant vegetation, substrate type and age, soil water balance, and disturbance history), allowing us to isolate the impact of MAT on ecosystem carbon storage. Live biomass carbon did not vary predictably as a function of MAT, while detrital carbon declined by ~14 Mg of carbon ha(-1) for each 1 °C rise in temperature - a trend driven entirely by coarse woody debris and litter. The largest detrital pool, soil organic carbon, was the most stable with MAT and averaged 48% of total ecosystem carbon across the MAT gradient. Total ecosystem carbon did not vary significantly with MAT, and the distribution of ecosystem carbon between live biomass and detritus remained relatively constant across the MAT gradient at ~44% and ~56%, respectively. These findings suggest that in the absence of alterations to precipitation or disturbance regimes, the size and distribution of carbon pools in tropical montane wet forests will be less sensitive to rising MAT than predicted by ecosystem models. This article also provides needed detail on how individual carbon pools and ecosystem-level carbon storage will respond to future warming.
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.
Patterns of tropical forest functional diversity express processes of ecological assembly at multiple geographic scales and aid in predicting ecological responses to environmental change. Tree canopy chemistry underpins forest functional diversity, but the interactive role of phylogeny and environment in determining the chemical traits of tropical trees is poorly known. Collecting and analyzing foliage in 2,420 canopy tree species across 19 forests in the western Amazon, we discovered (i) systematic, community-scale shifts in average canopy chemical traits along gradients of elevation and soil fertility; (ii) strong phylogenetic partitioning of structural and defense chemicals within communities independent of variation in environmental conditions; and (iii) strong environmental control on foliar phosphorus and calcium, the two rock-derived elements limiting CO2 uptake in tropical forests. These findings indicate that the chemical diversity of western Amazonian forests occurs in a regionally nested mosaic driven by long-term chemical trait adjustment of communities to large-scale environmental filters, particularly soils and climate, and is supported by phylogenetic divergence of traits essential to foliar survival under varying environmental conditions. Geographically nested patterns of forest canopy chemical traits will play a role in determining the response and functional rearrangement of western Amazonian ecosystems to changing land use and climate.
Spectral properties of foliage express fundamental chemical interactions of canopies with solar radiation. However, the degree to which leaf spectra track chemical traits across environmental gradients in tropical forests is unknown. We analyzed leaf reflectance and transmittance spectra in 2567 tropical canopy trees comprising 1449 species in 17 forests along a 3400-m elevation and soil fertility gradient from the Amazonian lowlands to the Andean treeline. We developed quantitative links between 21 leaf traits and 400-2500-nm spectra, and developed classifications of tree taxa based on spectral traits. Our results reveal enormous inter-specific variation in spectral and chemical traits among canopy trees of the western Amazon. Chemical traits mediating primary production were tightly linked to elevational changes in foliar spectral signatures. By contrast, defense compounds and rock-derived nutrients tracked foliar spectral variation with changing soil fertility in the lowlands. Despite the effects of abiotic filtering on mean foliar spectral properties of tree communities, the spectra were dominated by phylogeny within any given community, and spectroscopy accurately classified 85-93% of Amazonian tree species. Our findings quantify how tropical tree canopies interact with sunlight, and indicate how to measure the functional and biological diversity of forests with spectroscopy.
Forest inventory studies in the Amazon indicate a large terrestrial carbon sink. However, field plots may fail to represent forest mortality processes at landscape-scales of tropical forests. Here we characterize the frequency distribution of disturbance events in natural forests from 0.01?ha to 2,651?ha size throughout Amazonia using a novel combination of forest inventory, airborne lidar and satellite remote sensing data. We find that small-scale mortality events are responsible for aboveground biomass losses of ~1.7?Pg?C?y(-1) over the entire Amazon region. We also find that intermediate-scale disturbances account for losses of ~0.2?Pg?C?y(-1), and that the largest-scale disturbances as a result of blow-downs only account for losses of ~0.004?Pg?C?y(-1). Simulation of growth and mortality indicates that even when all carbon losses from intermediate and large-scale disturbances are considered, these are outweighed by the net biomass accumulation by tree growth, supporting the inference of an Amazon carbon sink.
Trees compete for space in the canopy, but where and how individuals or their component parts win or lose is poorly understood. We developed a stochastic model of three-dimensional dynamics in canopies using a hierarchical Bayesian framework, and analysed 267,533 positive height changes from 1.25 m pixels using data from airborne LiDAR within 43 ha on the windward flank of Mauna Kea. Model selection indicates a strong resident's advantage, with 97.9% of positions in the canopy retained by their occupants over 2 years. The remaining 2.1% were lost to a neighbouring contender. Absolute height was a poor predictor of success, but short stature greatly raised the risk of being overtopped. Growth in the canopy was exponentially distributed with a scaling parameter of 0.518. These findings show how size and spatial proximity influence the outcome of competition for space, and provide a general framework for the analysis of canopy dynamics.
Secondary forests cover large areas of the tropics and play an important role in the global carbon cycle. During secondary forest succession, simultaneous changes occur among stand structural attributes, soil properties, and species composition. Most studies classify tree species into categories based on their regeneration requirements. We use a high-resolution secondary forest chronosequence to assign trees to a continuous gradient in species successional status assigned according to their distribution across the chronosequence. Species successional status, not stand age or differences in stand structure or soil properties, was found to be the best predictor of leaf trait variation. Foliar ?(13)C had a significant positive relationship with species successional status, indicating changes in foliar physiology related to growth and competitive strategy, but was not correlated with stand age, whereas soil ?(13)C dynamics were largely constrained by plant species composition. Foliar ?(15)N had a significant negative correlation with both stand age and species successional status, - most likely resulting from a large initial biomass-burning enrichment in soil (15)N and (13)C and not closure of the nitrogen cycle. Foliar %C was neither correlated with stand age nor species successional status but was found to display significant phylogenetic signal. Results from this study are relevant to understanding the dynamics of tree species growth and competition during forest succession and highlight possibilities of, and potentially confounding signals affecting, the utility of leaf traits to understand community and species dynamics during secondary forest succession.
Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including--in the latter case--x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called "out-of-bag"), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha(-1) when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.
Malaria is a significant public health threat in the Brazilian Amazon. Previous research has shown that deforestation creates breeding sites for the main malaria vector in Brazil, Anopheles darlingi, but the influence of selective logging, forest fires, and road construction on malaria risk has not been assessed. To understand these impacts, we constructed a negative binomial model of malaria counts at the municipality level controlling for human population and social and environmental risk factors. Both paved and unpaved roadways and fire zones in a municipality increased malaria risk. Within the timber production states where 90% of deforestation has occurred, compared with areas without selective logging, municipalities where 0-7% of the remaining forests were selectively logged had the highest malaria risk (1.72, 95% CI 1.18-2.51), and areas with higher rates of selective logging had the lowest risk (0.39, 95% CI 0.23-0.67). We show that roads, forest fires, and selective logging are previously unrecognized risk factors for malaria in the Brazilian Amazon and highlight the need for regulation and monitoring of sub-canopy forest disturbance.
Gold mining has rapidly increased in western Amazonia, but the rates and ecological impacts of mining remain poorly known and potentially underestimated. We combined field surveys, airborne mapping, and high-resolution satellite imaging to assess road- and river-based gold mining in the Madre de Dios region of the Peruvian Amazon from 1999 to 2012. In this period, the geographic extent of gold mining increased 400%. The average annual rate of forest loss as a result of gold mining tripled in 2008 following the global economic recession, closely associated with increased gold prices. Small clandestine operations now comprise more than half of all gold mining activities throughout the region. These rates of gold mining are far higher than previous estimates that were based on traditional satellite mapping techniques. Our results prove that gold mining is growing more rapidly than previously thought, and that high-resolution monitoring approaches are required to accurately quantify human impacts on tropical forests.
Tree biomass is an integrated measure of net growth and is critical for understanding, monitoring, and modeling ecosystem functions. Despite the importance of accurately measuring tree biomass, several fundamental barriers preclude direct measurement at large spatial scales, including the facts that trees must be felled to be weighed and that even modestly sized trees are challenging to maneuver once felled. Allometric methods allow for estimation of tree mass using structural characteristics, such as trunk diameter. Savanna trees present additional challenges, including limited available allometry and a prevalence of multiple stems per individual. Here we collected airborne lidar data over a semiarid savanna adjacent to the Kruger National Park, South Africa, and then harvested and weighed woody plant biomass at the plot scale to provide a standard against which field and airborne estimation methods could be compared. For an existing airborne lidar method, we found that half of the total error was due to averaging canopy height at the plot scale. This error was eliminated by instead measuring maximum height and crown area of individual trees from lidar data using an object-based method to identify individual tree crowns and estimate their biomass. The best object-based model approached the accuracy of field allometry at both the tree and plot levels, and it more than doubled the accuracy compared to existing airborne methods (17% vs. 44% deviation from harvested biomass). Allometric error accounted for less than one-third of the total residual error in airborne biomass estimates at the plot scale when using allometry with low bias. Airborne methods also gave more accurate predictions at the plot level than did field methods based on diameter-only allometry. These results provide a novel comparison of field and airborne biomass estimates using harvested plots and advance the role of lidar remote sensing in savanna ecosystems.
The functional role of herbivores in tropical rainforests remains poorly understood. We quantified the magnitude of, and underlying controls on, carbon, nitrogen and phosphorus cycled by invertebrate herbivory along a 2800 m elevational gradient in the tropical Andes spanning 12°C mean annual temperature. We find, firstly, that leaf area loss is greater at warmer sites with lower foliar phosphorus, and secondly, that the estimated herbivore-mediated flux of foliar nitrogen and phosphorus from plants to soil via leaf area loss is similar to, or greater than, other major sources of these nutrients in tropical forests. Finally, we estimate that herbivores consume a significant portion of plant carbon, potentially causing major shifts in the pattern of plant and soil carbon cycling. We conclude that future shifts in herbivore abundance and activity as a result of environmental change could have major impacts on soil fertility and ecosystem carbon sequestration in tropical forests.
Mapping the aboveground biomass of tropical forests is essential both for implementing conservation policy and reducing uncertainties in the global carbon cycle. Two medium resolution (500m -- 1000m) pantropical maps of vegetation biomass have been recently published, and have been widely used by sub-national and national-level activities in relation to Reducing Emissions from Deforestation and forest Degradation (REDD+). Both maps use similar input data layers, and are driven by the same spaceborne LiDAR dataset providing systematic forest height and canopy structure estimates, but use different ground datasets for calibration and different spatial modelling methodologies. Here, we compare these two maps to each other, to the FAOs Forest Resource Assessment (FRA) 2010 country-level data, and to a high resolution (100m) biomass map generated for a portion of the Colombian Amazon.
High fidelity carbon mapping has the potential to greatly advance national resource management and to encourage international action toward climate change mitigation. However, carbon inventories based on field plots alone cannot capture the heterogeneity of carbon stocks, and thus remote sensing-assisted approaches are critically important to carbon mapping at regional to global scales. We advanced a high-resolution, national-scale carbon mapping approach applied to the Republic of Panama - one of the first UN REDD?+?partner countries.
Increases in the spatial extent and density of woody plants relative to herbaceous species have been observed across many ecosystems. These changes can have large effects on ecosystem carbon stocks and therefore are of interest for regional and national carbon inventories and for potential carbon sequestration or management activities. However, it is challenging to estimate the effect of woody plant encroachment on carbon because aboveground carbon stocks are very heterogeneous spatially and belowground carbon stocks exhibit complex and variable responses to changing plant cover. As a result, estimates of carbon stock changes with woody plant cover remain highly uncertain. In this study, we use a combination of plot- and remote sensing-based techniques to estimate the carbon impacts of piñon and juniper (PJ) encroachment in SE Utah across a variety of spatial scales with a specific focus on the role of spatial heterogeneity in carbon estimates.
Understanding how and why plant communities vary across space has long been a goal of ecology, yet parsing the relative importance of different influences has remained a challenge. Species-specific models are not generalizable, whereas broad plant functional type models lack important detail. Here we consider plant trait patterns at the local scale and ask whether plant chemical traits are more closely linked to environmental gradients or to changes in species composition. We used the visible-to-shortwave infrared (VSWIR) spectrometer of the Carnegie Airborne Observatory to develop maps of four plant chemical traits--leaf nitrogen per mass, leaf carbon per mass, leaf water concentration, and canopy water content--across a diverse Mediterranean-type ecosystem (Jasper Ridge Biological Preserve, CA). For all four traits, plant community alone was the strongest predictor of trait variation (explaining 46-61% of the heterogeneity), whereas environmental gradients accounted for just one fourth of the variation in the traits. This result emphasizes the critical role that species composition plays in mediating nutrient and carbon cycling within and among different communities. Environmental filtering and limits to similarity can act strongly, simultaneously, in a spatially heterogeneous environment, but the local-scale environmental gradients alone cannot account for the variation across this landscape.
An understanding of the spatial variability in tropical forest structure and biomass, and the mechanisms that underpin this variability, is critical for designing, interpreting, and upscaling field studies for regional carbon inventories. We investigated the spatial structure of tropical forest vegetation and its relationship to the hydrological network and associated topographic structure across spatial scales of 10-1000 m using high-resolution maps of LiDAR-derived mean canopy profile height (MCH) and elevation for 4930 ha of tropical forest in central Panama. MCH was strongly associated with the hydrological network: canopy height was highest in areas of positive convexity (valleys, depressions) close to channels draining 1 ha or more. Average MCH declined strongly with decreasing convexity (transition to ridges, hilltops) and increasing distance from the nearest channel. Spectral analysis, performed with wavelet decomposition, showed that the variance in MCH had fractal similarity at scales of ?30-600 m, and was strongly associated with variation in elevation, with peak correlations at scales of ?250 m. Whereas previous studies of topographic correlates of tropical forest structure conducted analyses at just one or a few spatial grains, our study found that correlations were strongly scale-dependent. Multi-scale analyses of correlations of MCH with slope, aspect, curvature, and Laplacian convexity found that MCH was most strongly related to convexity measured at scales of 20-300 m, a topographic variable that is a good proxy for position with respect to the hydrological network. Overall, our results support the idea that, even in these mesic forests, hydrological networks and associated topographical variation serve as templates upon which vegetation is organized over specific ranges of scales. These findings constitute an important step towards a mechanistic understanding of these patterns, and can guide upscaling and downscaling.
The Malaysian states of Sabah and Sarawak are global hotspots of forest loss and degradation due to timber and oil palm industries; however, the rates and patterns of change have remained poorly measured by conventional field or satellite approaches. Using 30 m resolution optical imagery acquired since 1990, forest cover and logging roads were mapped throughout Malaysian Borneo and Brunei using the Carnegie Landsat Analysis System. We uncovered ?364,000 km of roads constructed through the forests of this region. We estimated that in 2009 there were at most 45,400 km(2) of intact forest ecosystems in Malaysian Borneo and Brunei. Critically, we found that nearly 80% of the land surface of Sabah and Sarawak was impacted by previously undocumented, high-impact logging or clearing operations from 1990 to 2009. This contrasted strongly with neighbouring Brunei, where 54% of the land area remained covered by unlogged forest. Overall, only 8% and 3% of land area in Sabah and Sarawak, respectively, was covered by intact forests under designated protected areas. Our assessment shows that very few forest ecosystems remain intact in Sabah or Sarawak, but that Brunei, by largely excluding industrial logging from its borders, has been comparatively successful in protecting its forests.
Canopy gaps express the time-integrated effects of tree failure and mortality as well as regrowth and succession in tropical forests. Quantifying the size and spatial distribution of canopy gaps is requisite to modeling forest functional processes ranging from carbon fluxes to species interactions and biological diversity. Using high-resolution airborne Light Detection and Ranging (LiDAR), we mapped and analyzed 5,877,937 static canopy gaps throughout 125,581 ha of lowland Amazonian forest in Peru. Our LiDAR sampling covered a wide range of forest physiognomies across contrasting geologic and topographic conditions, and on depositional floodplain and erosional terra firme substrates. We used the scaling exponent of the Zeta distribution (?) as a metric to quantify and compare the negative relationship between canopy gap frequency and size across sites. Despite variable canopy height and forest type, values of ? were highly conservative (? mean ?=?1.83, s ?=?0.09), and little variation was observed regionally among geologic substrates and forest types, or at the landscape level comparing depositional-floodplain and erosional terra firme landscapes. ?-values less than 2.0 indicate that these forests are subjected to large gaps that reset carbon stocks when they occur. Consistency of ?-values strongly suggests similarity in the mechanisms of canopy failure across a diverse array of lowland forests in southwestern Amazonia.
Widespread occurrence of fires in Amazonian forests is known to be associated with extreme droughts, but historical data on the location and extent of forest fires are fundamental to determining the degree to which climate conditions and droughts have affected fire occurrence in the region. We used remote sensing to derive a 23-year time series of annual landscape-level burn scars in a fragmented forest of the eastern Amazon. Our burn scar data set is based on a new routine developed for the Carnegie Landsat Analysis System (CLAS), called CLAS-BURN, to calculate a physically based burn scar index (BSI) with an overall accuracy of 93% (Kappa coefficient 0.84). This index uses sub-pixel cover fractions of photosynthetic vegetation, non-photosynthetic vegetation, and shade/burn scar spectral end members. From 23 consecutive Landsat images processed with the CLAS-BURN algorithm, we quantified fire frequencies, the variation in fire return intervals, and rates of conversion of burned forest to other land uses in a 32 400 km2 area. From 1983 to 2007, 15% of the forest burned; 38% of these burned forests were subsequently deforested, representing 19% of the area cleared during the period of observation. While 72% of the fire-affected forest burned only once during the 23-year study period, 20% burned twice, 6% burned three times, and 2% burned four or more times, with the maximum of seven times. These frequencies suggest that the current fire return interval is 5-11 times more frequent than the estimated natural fire regime. Our results also quantify the substantial influence of climate and extreme droughts caused by a strong El Niño Southern Oscillation (ENSO) on the extent and likelihood of returning forest fires mainly in fragmented landscapes. These results are an important indication of the role of future warmer climate and deforestation in enhancing emissions from more frequently burned forests in the Amazon.
Escape from natural enemies is a widely held generalization for the success of exotic plants. We conducted a large-scale experiment in Hawaii (USA) to quantify impacts of ungulate removal on plant growth and performance, and to test whether elimination of an exotic generalist herbivore facilitated exotic success. Assessment of impacted and control sites before and after ungulate exclusion using airborne imaging spectroscopy and LiDAR, time series satellite observations, and ground-based field studies over nine years indicated that removal of generalist herbivores facilitated exotic success, but the abundance of native species was unchanged. Vegetation cover <1 m in height increased in ungulate-free areas from 48.7% +/- 1.5% to 74.3% +/- 1.8% over 8.4 years, corresponding to an annualized growth rate of lambda = 1.05 +/- 0.01 yr(-1) (median +/- SD). Most of the change was attributable to exotic plant species, which increased from 24.4% +/- 1.4% to 49.1% +/- 2.0%, (lambda = 1.08 +/- 0.01 yr(-1)). Native plants experienced no significant change in cover (23.0% +/- 1.3% to 24.2% +/- 1.8%, lambda = 1.01 +/- 0.01 yr(-1)). Time series of satellite phenology were indistinguishable between the treatment and a 3.0-km2 control site for four years prior to ungulate removal, but they diverged immediately following exclusion of ungulates. Comparison of monthly EVI means before and after ungulate exclusion and between the managed and control areas indicates that EVI strongly increased in the managed area after ungulate exclusion. Field studies and airborne analyses show that the dominant invader was Senecio madagascariensis, an invasive annual forb that increased from < 0.01% to 14.7% fractional cover in ungulate-free areas (lambda = 1.89 +/- 0.34 yr(-1)), but which was nearly absent from the control site. A combination of canopy LAI, water, and fractional cover were expressed in satellite EVI time series and indicate that the invaded region maintained greenness during drought conditions. These findings demonstrate that enemy release from generalist herbivores can facilitate exotic success and suggest a plausible mechanism by which invasion occurred. They also show how novel remote-sensing technology can be integrated with conservation and management to help address exotic plant invasions.
Measuring forest degradation and related forest carbon stock changes is more challenging than measuring deforestation since degradation implies changes in the structure of the forest and does not entail a change in land use, making it less easily detectable through remote sensing. Although we anticipate the use of the IPCC guidance under the United Framework Convention on Climate Change (UNFCCC), there is no one single method for monitoring forest degradation for the case of REDD+ policy. In this review paper we highlight that the choice depends upon a number of factors including the type of degradation, available historical data, capacities and resources, and the potentials and limitations of various measurement and monitoring approaches. Current degradation rates can be measured through field data (i.e. multi-date national forest inventories and permanent sample plot data, commercial forestry data sets, proxy data from domestic markets) and/or remote sensing data (i.e. direct mapping of canopy and forest structural changes or indirect mapping through modelling approaches), with the combination of techniques providing the best options. Developing countries frequently lack consistent historical field data for assessing past forest degradation, and so must rely more on remote sensing approaches mixed with current field assessments of carbon stock changes. Historical degradation estimates will have larger uncertainties as it will be difficult to determine their accuracy. However improving monitoring capacities for systematic forest degradation estimates today will help reduce uncertainties even for historical estimates.
Leaf mass per area (LMA) is a trait of central importance to plant physiology and ecosystem function, but LMA patterns in the upper canopies of humid tropical forests have proved elusive due to tall species and high diversity. We collected top-of-canopy leaf samples from 2873 individuals in 57 sites spread across the Neotropics, Australasia, and Caribbean and Pacific Islands to quantify environmental and taxonomic drivers of LMA variation, and to advance remote-sensing measures of LMA. We uncovered strong taxonomic organization of LMA, with species accounting for 70% of the global variance and up to 62% of the variation within a forest stand. Climate, growth habit, and site conditions are secondary contributors (1-23%) to the observed LMA patterns. Intraspecific variation in LMA averages 16%, which is a fraction of the variation observed between species. We then used spectroscopic remote sensing (400-2500 nm) to estimate LMA with an absolute uncertainty of 14-15 g/m2 (r2 = 0.85), or approximately 10% of the global mean. With radiative transfer modeling, we demonstrated the scalability of spectroscopic remote sensing of LMA to the canopy level. Our study indicates that remotely sensed patterns of LMA will be driven by taxonomic variation against a backdrop of environmental controls expressed at site and regional levels.
Airborne light detection and ranging (LiDAR) is fast turning the corner from demonstration technology to a key tool for assessing carbon stocks in tropical forests. With its ability to penetrate tropical forest canopies and detect three-dimensional forest structure, LiDAR may prove to be a major component of international strategies to measure and account for carbon emissions from and uptake by tropical forests. To date, however, basic ecological information such as height-diameter allometry and stand-level wood density have not been mechanistically incorporated into methods for mapping forest carbon at regional and global scales. A better incorporation of these structural patterns in forests may reduce the considerable time needed to calibrate airborne data with ground-based forest inventory plots, which presently necessitate exhaustive measurements of tree diameters and heights, as well as tree identifications for wood density estimation. Here, we develop a new approach that can facilitate rapid LiDAR calibration with minimal field data. Throughout four tropical regions (Panama, Peru, Madagascar, and Hawaii), we were able to predict aboveground carbon density estimated in field inventory plots using a single universal LiDAR model (r ( 2 ) = 0.80, RMSE = 27.6 Mg C ha(-1)). This model is comparable in predictive power to locally calibrated models, but relies on limited inputs of basal area and wood density information for a given region, rather than on traditional plot inventories. With this approach, we propose to radically decrease the time required to calibrate airborne LiDAR data and thus increase the output of high-resolution carbon maps, supporting tropical forest conservation and climate mitigation policy.
• Canopy chemistry and spectroscopy offer insight into community assembly and ecosystem processes in high-diversity tropical forests, but phylogenetic and environmental factors controlling chemical traits underpinning spectral signatures remain poorly understood. • We measured 21 leaf chemical traits and spectroscopic signatures of 594 canopy individuals on high-fertility Inceptisols and low-fertility Ultisols in a lowland Amazonian forest. The spectranomics approach, which explicitly connects phylogenetic, chemical and spectral patterns in tropical canopies, provided the basis for analysis. • Intracrown and intraspecific variation in chemical traits varied from 1.4 to 36.7% (median 9.3%), depending upon the chemical constituent. Principal components analysis showed that 14 orthogonal combinations were required to explain 95% of the variation among 21 traits, indicating the high dimensionality of canopy chemical signatures among taxa. Inceptisols and lianas were associated with high leaf nutrient concentrations and low concentrations of defense compounds. Independent of soils or plant habit, an average 70% (maximum 89%) of chemical trait variation was explained by taxonomy. At least 10 traits were quantitatively linked to remotely sensed signatures, which provided highly accurate species classification. • The results suggest that taxa found on fertile soils carry chemical portfolios with a deep evolutionary history, whereas taxa found on low-fertility soils have undergone trait evolution at the species level. Spectranomics provides a new connection between remote sensing and community assembly theory in high-diversity tropical canopies.
Despite the importance of fire in shaping savannas, it remains poorly understood how the frequency, seasonality, and intensity of fire interact to influence woody vegetation structure, which is a key determinant of savanna biodiversity. We provide a comprehensive analysis of vertical and horizontal woody vegetation structure across one of the oldest savanna fire experiments, using new airborne Light Detection and Ranging (LiDAR) technology. We developed and compared high-resolution woody vegetation height surfaces for a series of large experimental burn plots in the Kruger National Park, South Africa. These 7-ha plots (total area approximately 1500 ha) have been subjected to fire in different seasons and at different frequencies, as well as no-burn areas, for 54 years. Long-term exposure to fire caused a reduction in woody vegetation up to the 5.0-7.5 m height class, although most reduction was observed up to 4 m. Average fire intensity was positively correlated with changes in woody vegetation structure. More frequent fires reduced woody vegetation cover more than less frequent fires, and dry-season fires reduced woody vegetation more than wet-season fires. Spring fires from the late dry season reduced woody vegetation cover the most, and summer fires from the wet season reduced it the least. Fire had a large effect on structure in the densely wooded granitic landscapes as compared to the more open basaltic landscapes, although proportionally, the woody vegetation was more reduced in the drier than in the wetter landscapes. We show that fire frequency and fire season influence patterns of vegetation three-dimensional structure, which may have cascading consequences for biodiversity. Managers of savannas can therefore use fire frequency and season in concert to achieve specific vegetation structural objectives.
A broad regional understanding of tropical forest leaf photosynthesis has long been a goal for tropical forest ecologists, but it has remained elusive due to difficult canopy access and high species diversity. Here we develop an empirical model to predict sunlit, light-saturated, tropical leaf photosynthesis using leaf and simulated canopy spectra. To develop this model, we used partial least squares (PLS) analysis on three tropical forest datasets (159 species), two in Hawaii and one at the biosphere 2 laboratory (B2L). For each species, we measured light-saturated photosynthesis (A), light and CO(2) saturated photosynthesis (A(max)), respiration (R), leaf transmittance and reflectance spectra (400-2,500 nm), leaf nitrogen, chlorophyll a and b, carotenoids, and leaf mass per area (LMA). The model best predicted A [r(2) = 0.74, root mean square error (RMSE) = 2.9 ?mol m(-2) s(-1))] followed by R (r(2) = 0.48), and A(max) (r(2) = 0.47). We combined leaf reflectance and transmittance with a canopy radiative transfer model to simulate top-of-canopy reflectance and found that canopy spectra are a better predictor of A (RMSE = 2.5 ± 0.07 ?mol m(-2) s(-1)) than are leaf spectra. The results indicate the potential for this technique to be used with high-fidelity imaging spectrometers to remotely sense tropical forest canopy photosynthesis.
Efforts to mitigate climate change through the Reduced Emissions from Deforestation and Degradation (REDD) depend on mapping and monitoring of tropical forest carbon stocks and emissions over large geographic areas. With a new integrated use of satellite imaging, airborne light detection and ranging, and field plots, we mapped aboveground carbon stocks and emissions at 0.1-ha resolution over 4.3 million ha of the Peruvian Amazon, an area twice that of all forests in Costa Rica, to reveal the determinants of forest carbon density and to demonstrate the feasibility of mapping carbon emissions for REDD. We discovered previously unknown variation in carbon storage at multiple scales based on geologic substrate and forest type. From 1999 to 2009, emissions from land use totaled 1.1% of the standing carbon throughout the region. Forest degradation, such as from selective logging, increased regional carbon emissions by 47% over deforestation alone, and secondary regrowth provided an 18% offset against total gross emissions. Very high-resolution monitoring reduces uncertainty in carbon emissions for REDD programs while uncovering fundamental environmental controls on forest carbon storage and their interactions with land-use change.
Drought varies spatially and temporally throughout the Amazon basin, challenging efforts to assess ecological impacts via field measurements alone. Remote sensing offers a range of regional insights into drought-mediated changes in cloud cover and rainfall, canopy physiology, and fire. Here, we summarize remote sensing studies of Amazônia which indicate that: fires and burn scars are more common during drought years; hydrological function including floodplain area is significantly affected by drought; and land use affects the sensitivity of the forest to dry conditions and increases fire susceptibility during drought. We highlight two controversial areas of research centering on canopy physiological responses to drought and changes in subcanopy fires during drought. By comparing findings from field and satellite studies, we contend that current remote sensing observations and techniques cannot resolve these controversies using current satellite observations. We conclude that studies integrating multiple lines of evidence from physiological, disturbance-fire, and hydrological remote sensing, as well as field measurements, are critically needed to narrow our uncertainty of basin-level responses to drought and climate change.
Global vegetation models predict the spread of woody vegetation in African savannas and grasslands under future climate scenarios, but they operate too broadly to consider hillslope-scale variations in tree-grass distribution. Topographically linked hydrology-soil-vegetation sequences, or catenas, underpin a variety of ecological processes in savannas, including responses to climate change. In this study, we explore the three-dimensional structure of hillslopes and vegetation, using high-resolution airborne LiDAR (Light Detection And Ranging), to understand the long-term effects of mean annual precipitation (MAP) on catena pattern. Our results reveal that the presence and position of hillslope hydrological boundaries, or seeplines, vary as a function of MAP through its long-term influence on clay redistribution. We suggest that changes in climate will differentially alter the structure of savannas through hydrological changes to the seasonally saturated grasslands downslope of seeplines. The mechanisms underlying future woody encroachment are not simply physiological responses to elevated temperatures and CO(2) levels but also involve hydrogeomorphological processes at the hillslope scale.
We compared forest canopy heights and nitrogen concentrations in long-term research sites and in 2 x 2 km landscapes surrounding these sites along a substrate age gradient in the Hawaiian Islands. Both remote airborne and ground-based measurements were used to characterize processes that control landscape-level variation in canopy properties. We integrated a waveform light detection and ranging (LiDAR) system, a high-resolution imaging spectrometer, and a global positioning system/inertial measurement unit to provide highly resolved images of ground topography, canopy heights, and canopy nitrogen concentrations (1) within a circle 50 m in radius focused on a long-term study site in the center of each landscape; (2) for the entire 2 x 2 km landscape regardless of land cover; and (3) after stratification, for our target cover class, native-dominated vegetation on constructional geomorphic surfaces throughout each landscape. Remote measurements at all scales yielded the same overall patterns as did ground-based measurements in the long-term sites. The two younger landscapes supported taller trees than did older landscapes, while the two intermediate-aged landscapes had higher canopy nitrogen (N) concentrations than did either young or old landscapes. However, aircraft-based analyses detected substantial variability in canopy characteristics on the landscape level, even within the target cover class. Canopy heights were more heterogeneous on the older landscapes, with coefficients of variation increasing from 23-41% to 69-78% with increasing substrate age. This increasing heterogeneity was associated with a larger patch size of canopy turnover and with dominance of most secondary successional stands by the mat-forming fern Dicranopteris linearis in the older landscapes.
The ranges of plants and animals are moving in response to recent changes in climate. As temperatures rise, ecosystems with nowhere to go, such as mountains, are considered to be more threatened. However, species survival may depend as much on keeping pace with moving climates as the climates ultimate persistence. Here we present a new index of the velocity of temperature change (km yr(-1)), derived from spatial gradients ( degrees C km(-1)) and multimodel ensemble forecasts of rates of temperature increase ( degrees C yr(-1)) in the twenty-first century. This index represents the instantaneous local velocity along Earths surface needed to maintain constant temperatures, and has a global mean of 0.42 km yr(-1) (A1B emission scenario). Owing to topographic effects, the velocity of temperature change is lowest in mountainous biomes such as tropical and subtropical coniferous forests (0.08 km yr(-1)), temperate coniferous forest, and montane grasslands. Velocities are highest in flooded grasslands (1.26 km yr(-1)), mangroves and deserts. High velocities suggest that the climates of only 8% of global protected areas have residence times exceeding 100 years. Small protected areas exacerbate the problem in Mediterranean-type and temperate coniferous forest biomes. Large protected areas may mitigate the problem in desert biomes. These results indicate management strategies for minimizing biodiversity loss from climate change. Montane landscapes may effectively shelter many species into the next century. Elsewhere, reduced emissions, a much expanded network of protected areas, or efforts to increase species movement may be necessary.
Size frequency distributions of canopy gaps are a hallmark of forest dynamics. But it remains unknown whether legacies of forest disturbance are influencing vertical size structure of landscapes, or space-filling in the canopy volume. We used data from LiDAR remote sensing to quantify distributions of canopy height and sizes of 434,501 canopy gaps in five tropical rain forest landscapes in Costa Rica and Hawaii. The sites represented a wide range of variation in structure and natural disturbance history, from canopy gap dynamics in lowland Costa Rica and Hawaii, to stages and types of stand-level dieback on upland Mauna Kea and Kohala volcanoes. Large differences in vertical canopy structure characterized these five tropical rain forest landscapes, some of which were related to known disturbance events. Although there were quantitative differences in the values of scaling exponents within and among sites, size frequency distributions of canopy gaps followed power laws at all sites and in all canopy height classes. Scaling relationships in gap size at different heights in the canopy were qualitatively similar at all sites, revealing a remarkable similarity despite clearly defined differences in species composition and modes of prevailing disturbance. These findings indicate that power-law gap-size frequency distributions are ubiquitous features of these five tropical rain forest landscapes, and suggest that mechanisms of forest disturbance may be secondary to other processes in determining vertical and horizontal size structure in canopies.
Regional, high-resolution mapping of vegetation cover and biomass is central to understanding changes to the terrestrial carbon (C) cycle, especially in the context of C management. The third most extensive vegetation type in the United States is pinyon-juniper (P-J) woodland, yet the spatial patterns of tree cover and aboveground biomass (AGB) of P-J systems are poorly quantified. We developed a synoptic remote-sensing approach to scale up pinyon and juniper projected cover (hereafter "cover") and AGB field observations from plot to regional levels using fractional photosynthetic vegetation (PV) cover derived from airborne imaging spectroscopy and Landsat satellite data. Our results demonstrated strong correlations (P < 0.001) between field cover and airborne PV estimates (r2 = 0.92), and between airborne and satellite PV estimates (r2 = 0.61). Field data also indicated that P-J AGB can be estimated from canopy cover using a unified allometric equation (r2 = 0.69; P < 0.001). Using these multiscale cover-AGB relationships, we developed high-resolution, regional maps of P-J cover and AGB for the western Colorado Plateau. The P-J cover was 27.4% +/- 9.9% (mean +/- SD), and the mean aboveground woody C converted from AGB was 5.2 +/- 2.0 Mg C/ha. Combining our data with the southwest Regional Gap Analysis Program vegetation map, we estimated that total contemporary woody C storage for P-J systems throughout the Colorado Plateau (113 600 km2) is 59.0 +/- 22.7 Tg C. Our results show how multiple remote-sensing observations can be used to map cover and C stocks at high resolution in drylands, and they highlight the role of P-J ecosystems in the North American C budget.
Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions. Therefore, it is often important to systematically monitor the spread of species over a broad region. Remote sensing has been an important tool for large-scale ecological studies in the past three decades, but it was not commonly used to study alien invasive plants until the mid 1990s. We synthesize previous research efforts on remote sensing of invasive plants from spatial, temporal and spectral perspectives. We also highlight a recently developed state-of-the-art image fusion technique that integrates passive and active energies concurrently collected by an imaging spectrometer and a scanning-waveform light detection and ranging (LiDAR) system, respectively. This approach provides a means to detect the structure and functional properties of invasive plants of different canopy levels. Finally, we summarize regional studies of biological invasions using remote sensing, discuss the limitations of remote sensing approaches, and highlight current research needs and future directions.
Leaf chemical and spectral properties of 162 canopy species were measured at 11 tropical forest sites along a 6024 mm precipitation/yr and 8.7 degrees C climate gradient in Queensland, Australia. We found that variations in foliar nitrogen, phosphorus, chlorophyll a and b, and carotenoid concentrations, as well as specific leaf area (SLA), were expressed more strongly among species within a site than along the entire climate gradient. Integrated chemical signatures consisting of all leaf properties did not aggregate well at the genus or family levels. Leaf chemical diversity was maximal in the lowland tropical forest sites with the highest temperatures and moderate precipitation levels. Cooler and wetter montane tropical forests contained species with measurably lower variation in their chemical signatures. Foliar optical properties measured from 400 to 2500 nm were also highly diverse at the species level, and were well correlated with an ensemble of leaf chemical properties and SLA (r2 = 0.54-0.83). A probabilistic diversity model amplified the leaf chemical differences among species, revealing that lowland tropical forests maintain a chemical diversity per unit richness far greater than that of higher elevation forests in Australia. Modeled patterns in spectral diversity and species richness paralleled those of chemical diversity, demonstrating a linkage between the taxonomic and remotely sensed properties of tropical forest canopies. We conclude that species are the taxonomic unit causing chemical variance in Australian tropical forest canopies, and thus ecological and remote sensing studies should consider the role that species play in defining the functional properties of these forests.
Over the past 50 years, human agents of deforestation have changed in ways that have potentially important implications for conservation efforts. We characterized these changes through a meta-analysis of case studies of land-cover change in the tropics. From the 1960s to the 1980s, small-scale farmers, with state assistance, deforested large areas of tropical forest in Southeast Asia and Latin America. As globalization and urbanization increased during the 1980s, the agents of deforestation changed in two important parts of the tropical biome, the lowland rainforests in Brazil and Indonesia. Well-capitalized ranchers, farmers, and loggers producing for consumers in distant markets became more prominent in these places and this globalization weakened the historically strong relationship between local population growth and forest cover. At the same time, forests have begun to regrow in some tropical uplands. These changing circumstances, we believe, suggest two new and differing strategies for biodiversity conservation in the tropics, one focused on conserving uplands and the other on promoting environmental stewardship in lowlands and other areas conducive to industrial agriculture.
In recent decades the rate and geographic extent of land-use and land-cover change has increased throughout the worlds humid tropical forests. The pan-tropical geography of forest change is a challenge to assess, and improved estimates of the human footprint in the tropics are critical to understanding potential changes in biodiversity. We combined recently published and new satellite observations, along with images from Google Earth and a literature review, to estimate the contemporary global extent of deforestation, selective logging, and secondary regrowth in humid tropical forests. Roughly 1.4% of the biome was deforested between 2000 and 2005. As of 2005, about half of the humid tropical forest biome contained 50% or less tree cover. Although not directly comparable to deforestation, geographic estimates of selective logging indicate that at least 20% of the humid tropical forest biome was undergoing some level of timber harvesting between 2000 and 2005. Forest recovery estimates are even less certain, but a compilation of available reports suggests that at least 1.2% of the humid tropical forest biome was in some stage of long-term secondary regrowth in 2000. Nearly 70% of the regrowth reports indicate forest regeneration in hilly, upland, and mountainous environments considered marginal for large-scale agriculture and ranching. Our estimates of the human footprint are conservative because they do not resolve very small-scale deforestation, low-intensity logging, and unreported secondary regrowth, nor do they incorporate other impacts on tropical forest ecosystems, such as fire and hunting. Our results highlight the enormous geographic extent of forest change throughout the humid tropics and the considerable limitations of the science and technology available for such a synthesis.
African savannas are undergoing management intensification, and decision makers are increasingly challenged to balance the needs of large herbivore populations with the maintenance of vegetation and ecosystem diversity. Ensuring the sustainability of Africas natural protected areas requires information on the efficacy of management decisions at large spatial scales, but often neither experimental treatments nor large-scale responses are available for analysis. Using a new airborne remote sensing system, we mapped the three-dimensional (3-D) structure of vegetation at a spatial resolution of 56 cm throughout 1640 ha of savanna after 6-, 22-, 35-, and 41-year exclusions of herbivores, as well as in unprotected areas, across Kruger National Park in South Africa. Areas in which herbivores were excluded over the short term (6 years) contained 38%-80% less bare ground compared with those that were exposed to mammalian herbivory. In the longer-term (> 22 years), the 3-D structure of woody vegetation differed significantly between protected and accessible landscapes, with up to 11-fold greater woody canopy cover in the areas without herbivores. Our maps revealed 2 scales of ecosystem response to herbivore consumption, one broadly mediated by geologic substrate and the other mediated by hillslope-scale variation in soil nutrient availability and moisture conditions. Our results are the first to quantitatively illustrate the extent to which herbivores can affect the 3-D structural diversity of vegetation across large savanna landscapes.
Spatial variability in the effects of fire on savanna vegetation structure is seldom considered in ecology, despite the inherent heterogeneity of savanna landscapes. Much has been learned about the effects of fire on vegetation structure from long-term field experiments, but these are often of limited spatial extent and do not encompass different hillslope catena elements. We mapped vegetation three-dimensional (3-D) structure over 21 000 ha in nine savanna landscapes (six on granite, three on basalt), each with contrasting long-term fire histories (higher and lower fire frequency), as defined from a combination of satellite imagery and 67 years of management records. Higher fire frequency areas contained less woody canopy cover than their lower fire frequency counterparts in all landscapes, and woody cover reduction increased linearly with increasing difference in fire frequency (r2 = 0.58, P = 0.004). Vegetation height displayed a more heterogeneous response to difference in fire frequency, with taller canopies present in the higher fire frequency areas of the wetter sites. Vegetation 3-D structural differences between areas of higher and lower fire frequency differed between geological substrates and varied spatially across hillslopes. Fire had the greatest relative impact on vegetation structure on nutrient-rich basalt substrates, and it imparted different structural responses upon vegetation in upland, midslope, and lowland topographic positions. These results highlight the complexity of fire vegetation relationships in savanna systems, and they suggest that underlying landscape heterogeneity needs more explicit incorporation into fire management policies.
Herbivores cause treefalls in African savannas, but rates are unknown at large scales required to forecast changes in biodiversity and ecosystem processes. We combined landscape-scale herbivore exclosures with repeat airborne Light Detection and Ranging of 58 429 trees in Kruger National Park, South Africa, to assess sources of savanna treefall across nested gradients of climate, topography, and soil fertility. Elephants were revealed as the primary agent of treefall across widely varying savanna conditions, and a large-scale elephant trap predominantly removes maturing savanna trees in the 5-9 m height range. Treefall rates averaged 6 times higher in areas accessible to elephants, but proportionally more treefall occurred on high-nutrient basalts and in lowland catena areas. These patterns were superimposed on a climate-mediated regime of increasing treefall with precipitation in the absence of herbivores. These landscape-scale patterns reveal environmental controls underpinning herbivore-mediated tree turnover, highlighting the need for context-dependent science and management.
Lianas are an important growthform in tropical forests, and liana abundance and biomass may be increasing in some regions. Explanations for liana proliferation hinge upon physiological responses to changing resource conditions that would favour them over trees. Testing a chemical basis for such responses, we assessed 22 foliar traits in 778 lianas and 6496 trees at 48 tropical forest sites. Growthform differences in chemical allocation occurred on a leaf mass and area basis. Light capture-growth and maintenance-metabolism chemicals averaged 14.5 and 16.7% higher mass-based concentration in lianas than in trees globally, whereas structure and defence chemicals averaged 9.0% lower in lianas. Relative differences in chemical allocation by lianas and trees were mediated by climate with peak differences at about 2500 mm year(-1) and 25 °C. Differences in chemical traits suggest that liana expansion could be greatest in forests undergoing increased canopy-level irradiance via disturbance and climate change.
Industrial agricultural plantations are a rapidly increasing yet largely unmeasured source of tropical land cover change. Here, we evaluate impacts of oil palm plantation development on land cover, carbon flux, and agrarian community lands in West Kalimantan, Indonesian Borneo. With a spatially explicit land change/carbon bookkeeping model, parameterized using high-resolution satellite time series and informed by socioeconomic surveys, we assess previous and project future plantation expansion under five scenarios. Although fire was the primary proximate cause of 1989-2008 deforestation (93%) and net carbon emissions (69%), by 2007-2008, oil palm directly caused 27% of total and 40% of peatland deforestation. Plantation land sources exhibited distinctive temporal dynamics, comprising 81% forests on mineral soils (1994-2001), shifting to 69% peatlands (2008-2011). Plantation leases reveal vast development potential. In 2008, leases spanned ?65% of the region, including 62% on peatlands and 59% of community-managed lands, yet <10% of lease area was planted. Projecting business as usual (BAU), by 2020 ?40% of regional and 35% of community lands are cleared for oil palm, generating 26% of net carbon emissions. Intact forest cover declines to 4%, and the proportion of emissions sourced from peatlands increases 38%. Prohibiting intact and logged forest and peatland conversion to oil palm reduces emissions only 4% below BAU, because of continued uncontrolled fire. Protecting logged forests achieves greater carbon emissions reductions (21%) than protecting intact forests alone (9%) and is critical for mitigating carbon emissions. Extensive allocated leases constrain land management options, requiring trade-offs among oil palm production, carbon emissions mitigation, and maintaining community landholdings.
Aboveground biomass (AGB) reflects multiple and often undetermined ecological and land-use processes, yet detailed landscape-level studies of AGB are uncommon due to the difficulty in making consistent measurements at ecologically relevant scales. Working in a protected mediterranean-type landscape (Jasper Ridge Biological Preserve, California, USA), we combined field measurements with remotely sensed data from the Carnegie Airborne Observatorys light detection and ranging (lidar) system to create a detailed AGB map. We then developed a predictive model using a maximum of 56 explanatory variables derived from geologic and historic-ownership maps, a digital elevation model, and geographic coordinates to evaluate possible controls over currently observed AGB patterns. We tested both ordinary least-squares regression (OLS) and autoregressive approaches. OLS explained 44% of the variation in AGB, and simultaneous autoregression with a 100-m neighborhood improved the fit to an r2 = 0.72, while reducing the number of significant predictor variables from 27 variables in the OLS model to 11 variables in the autoregressive model. We also compared the results from these approaches to a more typical field-derived data set; we randomly sampled 5% of the data 1000 times and used the same OLS approach each time. Environmental filters including incident solar radiation, substrate type, and topographic position were significant predictors of AGB in all models. Past ownership was a minor but significant predictor, despite the long history of conservation at the site. The weak predictive power of these environmental variables, and the significant improvement when spatial autocorrelation was incorporated, highlight the importance of land-use history, disturbance regime, and population dynamics as controllers of AGB.
Accurate, high-resolution mapping of aboveground carbon density (ACD, Mg C ha-1) could provide insight into human and environmental controls over ecosystem state and functioning, and could support conservation and climate policy development. However, mapping ACD has proven challenging, particularly in spatially complex regions harboring a mosaic of land use activities, or in remote montane areas that are difficult to access and poorly understood ecologically. Using a combination of field measurements, airborne Light Detection and Ranging (LiDAR) and satellite data, we present the first large-scale, high-resolution estimates of aboveground carbon stocks in Madagascar.
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