Inferring diversity patterns along an elevation gradient from stacked SDMs: A case study on Mesoamerican ferns

An enduring challenge in ecology is to characterise and understand species richness patterns in tropical regions. Species richness maps produced by stacking species distribution model (SDM) range maps could prove useful in this regard, but little attention has been given to this approach. Here we generate a species richness map by stacking the ranges of 86 Mesoamerican fern species modelled by MaxEnt from field data collected by the Sampled Red List Index for Plants project. Predicted species richness showed a hump-backed relationship with elevation, peaking at mid-elevation (1800–2000 m). A remarkably similar pattern was observed in a field survey conducted to validate the approach. Predicted species richness was also low in sites with high water deficits, as previously shown in the fern literature. Beta-diversity in the lowlands was greatest between sites with strongly contrasting water deficits, further emphasising the importance of this environmental variable. The stacked SDM approach was thus able to reproduce broad biogeographical patterns of species richness, despite many of the fern species being represented by fewer than 20 samples.

Syfert, M.M.; Brummitt, N.A.; Coomes, D.A.; Bystriakova, N.; Smith, M.J.

Global Ecology and Conservation, e00433
2018

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Estimating aboveground carbon density and its uncertainty in Borneo’s structurally complex tropical forests using airborne laser scanning

Borneo contains some of the world’s most biodiverse and carbon-dense tropical forest, but this 750000km2 island has lost 62% of its old-growth forests within the last 40 years. Efforts to protect and restore the remaining forests of Borneo hinge on recognizing the ecosystem services they provide, including their ability to store and sequester carbon. Airborne laser scanning (ALS) is a remote sensing technology that allows forest structural properties to be captured in great detail across vast geographic areas. In recent years ALS has been integrated into statewide assessments of forest carbon in Neotropical and African regions, but not yet in Asia. For this to happen new regional models need to be developed for estimating carbon stocks from ALS in tropical Asia, as the forests of this region are structurally and compositionally distinct from those found elsewhere in the tropics. By combining ALS imagery with data from 173 permanent forest plots spanning the lowland rainforests of Sabah on the island of Borneo, we develop a simple yet general model for estimating forest carbon stocks using ALS-derived canopy height and canopy cover as input metrics. An advanced feature of this new model is the propagation of uncertainty in both ALS- and ground-based data, allowing uncertainty in hectare-scale estimates of carbon stocks to be quantified robustly. We show that the model effectively captures variation in aboveground carbon stocks across extreme disturbance gradients spanning tall dipterocarp forests and heavily logged regions and clearly outperforms existing ALS-based models calibrated for the tropics, as well as currently available satellite-derived products. Our model provides a simple, generalized and effective approach for mapping forest carbon stocks in Borneo and underpins ongoing efforts to safeguard and facilitate the restoration of its unique tropical forests.

Jucker T. et al.

Biogeosciences 15 (12), 3811-3830
2018

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Topography shapes the structure, composition and function of tropical forest landscapes

Topography is a key driver of tropical forest structure and composition, as it constrains local nutrient and hydraulic conditions within which trees grow. Yet, we do not fully understand how changes in forest physiognomy driven by topography impact other emergent properties of forests, such as their aboveground carbon density (ACD). Working in Borneo – at a site where 70‐m‐tall forests in alluvial valleys rapidly transition to stunted heath forests on nutrient‐depleted dip slopes – we combined field data with airborne laser scanning and hyperspectral imaging to characterise how topography shapes the vertical structure, wood density, diversity and ACD of nearly 15 km2 of old‐growth forest. We found that subtle differences in elevation – which control soil chemistry and hydrology – profoundly influenced the structure, composition and diversity of the canopy. Capturing these processes was critical to explaining landscape‐scale heterogeneity in ACD, highlighting how emerging remote sensing technologies can provide new insights into long‐standing ecological questions.

Jucker, T.; Bongalov, B.; Burslem, D.F.R.P.; Nilus, R.; Dalponte, M.; Lewis, S.L., Phillips, O.L.; Qie, L.; Coomes, D.A.

Ecology Letters 21, 989-1000
2018

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Blind image fusion for hyperspectral imaging with the directional total variation

Hyperspectral imaging is a cutting-edge type of remote sensing used for mapping vegetation properties, rock minerals and other materials. A major drawback of hyperspectral imaging devices is their intrinsic low spatial resolution. In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality. This is accomplished by solving a variational problem in which the regularization functional is the directional total variation. To accommodate for possible mis-registrations between the two images, we consider a non-convex blind super-resolution problem where both a fused image and the corresponding convolution kernel are estimated. Using this approach, our model can realign the given images if needed. Our experimental results indicate that the non-convexity is negligible in practice and that reliable solutions can be computed using a variety of different optimization algorithms. Numerical results on real remote sensing data from plant sciences and urban monitoring show the potential of the proposed method and suggests that it is robust with respect to the regularization parameters, mis-registration and the shape of the kernel.

Bungert, L.; Coomes, D.A.; Ehrhardt, M.J.; Rasch, J.; Reisenhofer, R.; Schönlieb, C.B.

Inverse Problems 34 (4), 044003
2018

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Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo

Forest carbon stocks in rapidly developing tropical regions are highly heterogeneous, which challenges efforts to develop spatially-explicit conservation actions. In addition to field-based biodiversity information, mapping of carbon stocks can greatly accelerate the identification, protection and recovery of forests deemed to be of high conservation value (HCV). We combined airborne Light Detection and Ranging (LiDAR) with satellite imaging and other geospatial data to map forest aboveground carbon density at 30 m (0.09 ha) resolution throughout the Malaysian state of Sabah on the island of Borneo. We used the mapping results to assess how carbon stocks vary spatially based on forest use, deforestation, regrowth, and current forest protections. We found that unlogged, intact forests contain aboveground carbon densities averaging over 200 Mg C ha−1, with peaks of 500 Mg C ha−1. Critically, more than 40% of the highest carbon stock forests were discovered outside of areas designated for maximum protection. Previously logged forests have suppressed, but still high, carbon densities of 60–140 Mg C ha−1. Our mapped distributions of forest carbon stock suggest that the state of Sabah could double its total aboveground carbon storage if previously logged forests are allowed to recover in the future. Our results guide ongoing efforts to identify HCV forests and to determine new areas for forest protection in Borneo.

Asner, G.P. et al.

Biological Conservation 217, 289-310
2018

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Extreme and Highly Heterogeneous Microclimates in Selectively Logged Tropical Forests

Microclimate within forests influences ecosystem fluxes and demographic rates. Anthropogenic disturbances, such as selective logging can affect within-forest microclimate through effects on forest structure, leading to indirect effects on forests beyond the immediate impact of logging. However, the scope and predictability of these effects remains poorly understood. Here we use a microclimate thermal proxy (sensitive to radiative, convective, and conductive heat fluxes) measured at the forest floor in three 1-ha forest plots spanning a logging intensity gradient in Malaysian Borneo. We show (1) that thermal proxy ranges and spatiotemporal heterogeneity are doubled between old growth and heavily logged forests, with extremes often exceeding 45°C, (2) that nearby weather station air temperatures provide estimates of maximum thermal proxy values that are biased down by 5–10°C, and (3) that lower canopy density, higher canopy height, and higher biomass removal are associated with higher maximum temperatures. Thus, logged forests are less buffered from regional climate change than old growth forests, and experience much higher microclimate extremes and heterogeneity. Better predicting the linkages between regional climate and its effects on within-forest microclimate will be critical for understanding the wide range of conditions experienced within tropical forests.

Blonder, B. et al.

Frontiers in Forests and Global Change 1, 5
2017

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Effect of Tree Phenology on LiDAR Measurement of Mediterranean Forest Structure

Retrieval of forest biophysical properties using airborne LiDAR is known to differ between leaf-on and leaf-off states of deciduous trees, but much less is understood about the within-season effects of leafing phenology. Here, we compare two LiDAR surveys separated by just six weeks in spring, in order to assess whether LiDAR variables were influenced by canopy changes in Mediterranean mixed-oak woodlands at this time of year. Maximum and, to a slightly lesser extent, mean heights were consistently measured, whether for the evergreen cork oak (Quercus suber) or semi-deciduous Algerian oak (Q. canariensis) woodlands. Estimates of the standard deviation and skewness of height differed more strongly, especially for Algerian oaks which experienced considerable leaf expansion in the time period covered. Our demonstration of which variables are more or less affected by spring-time leafing phenology has important implications for analyses of both canopy and sub-canopy vegetation layers from LiDAR surveys.

Simonson, W.; Allen, H.; Coomes, D.A.

Remote Sensing 10 (5), 659
2018

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Enhancing of accuracy assessment for forest above-ground biomass estimates obtained from remote sensing via hypothesis testing and overfitting evaluation

The evaluation of accuracy is essential for assuring the reliability of ecological models. Usually, the accuracy of above-ground biomass (AGB) predictions obtained from remote sensing is assessed by the mean differences (MD), the root mean squared differences (RMSD), and the coefficient of determination (R2) between observed and predicted values. In this article we propose a more thorough analysis of accuracy, including a hypothesis test to evaluate the agreement between observed and predicted values, and an assessment of the degree of overfitting to the sample employed for model training. Using the estimation of forest AGB from LIDAR and spectral sensors as a case study, we compared alternative prediction and variable selection methods using several statistical measures to evaluate their accuracy. We showed that the hypothesis tests provide an objective method to infer the statistical significance of agreement. We also observed that overfitting can be assessed by comparing the inflation in residual sums of squares experienced when carrying out a cross-validation. Our results suggest that this method may be more effective than analysing the deflation in R2. We proved that overfitting needs to be specifically addressed since, in light of MDRMSD and R2 alone, predictions may apparently seem reliable even in clearly unrealistic circumstances, for instance when including too many predictor variables. Moreover, Theil’s partial inequality coefficients, which are employed to resolve the proportions of the total errors due to the unexplained variance, the slope and the bias, may become useful to detect averaging effects common in remote sensing predictions of AGB. We concluded that statistical measures of accuracy, precision and agreement are necessary but insufficient for model evaluation. We therefore advocate for incorporating evaluation measures specifically devoted to testing observed-versus-predicted fit, and to assessing the degree of overfitting.

Valbuena, R.; Hernando, A.; Manzanera, J.A.; Görgens, E.B.; Almeida, D.R.A.; Mauro, F.; García-Abril, A.; Coomes, D.A.

Ecological Modelling 366, 15-26
2017

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Synergistic use of Landsat 8 OLI image and airborne LiDAR data for above-ground biomass estimation in tropical lowland rainforests

Here we estimated AGB of primary tropical forests using airborne LiDAR and Landsat 8 OLI. We found that Landsat 8 OLI’s textures show greater potential to estimate AGB compared with LiDAR, and a synergistic use of Landsat 8 OLI and LiDAR outperforms the independent-data models in estimating biomass of tropical lowland rainforests.

Phua, M.H. et al.

Forest Ecology and Management 406, 163-171
2017

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Detecting the fingerprint of drought across Europe’s forests: do carbon isotope ratios and stem growth rates tell similar stories?

We evaluated the effectiveness of using the isotopic ratio of 13C to 12C stored in wood, referred to as δ13C, and stem basal area increments (BAI) as indicators of drought in European trees. Using tree ring data from over 3000 trees, we found that while δ13C responded strongly and consistently to drought across a diverse group of tree species and environmental conditions, droughts have small effects on BAI. The results indicate that while δ13C provides a powerful indicator of past drought occurrence, by themselves carbon isotope ratios tell us little about how carbon sequestration and allocation to wood are affected by droughts since most tree species were able to sustain growth even under conditions of low soil water availability.

Jucker, T.; Grossiord, C.; Bonal, D.; Bouriaud, O.; Gessler, A.; Coomes, D.A.

Forest Ecosystems 4 (1), 24
2017

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