Christian Salas

Positive biodiversity-productivity relationship predominant in global forests.

Global biodiversity and productivity The relationship between biodiversity and ecosystem productivity has been explored in detail in herbaceous vegetation, but patterns in forests are far less well understood. Liang et al. have amassed a global forest data set from >770,000 sample plots in 44 countries. A positive and consistent relationship can be discerned between tree diversity and ecosystem productivity at landscape, country, and ecoregion scales. On average, a 10% loss in biodiversity leads to a 3% loss in productivity. This means that the economic value of maintaining biodiversity for the sake of global forest productivity is more than fivefold greater than global conservation costs. Science, this issue p. 196 Global forest inventory records suggest that biodiversity loss would result in a decline in forest productivity worldwide. INTRODUCTION The biodiversity-productivity relationship (BPR; the effect of biodiversity on ecosystem productivity) is foundational to our understanding of the global extinction crisis and its impacts on the functioning of natural ecosystems. The BPR has been a prominent research topic within ecology in recent decades, but it is only recently that we have begun to develop a global perspective. RATIONALE Forests are the most important global repositories of terrestrial biodiversity, but deforestation, forest degradation, climate change, and other factors are threatening approximately one half of tree species worldwide. Although there have been substantial efforts to strengthen the preservation and sustainable use of forest biodiversity throughout the globe, the consequences of this diversity loss pose a major uncertainty for ongoing international forest management and conservation efforts. The forest BPR represents a critical missing link for accurate valuation of global biodiversity and successful integration of biological conservation and socioeconomic development. Until now, there have been limited tree-based diversity experiments, and the forest BPR has only been explored within regional-scale observational studies. Thus, the strength and spatial variability of this relationship remains unexplored at a global scale. RESULTS We explored the effect of tree species richness on tree volume productivity at the global scale using repeated forest inventories from 777,126 permanent sample plots in 44 countries containing more than 30 million trees from 8737 species spanning most of the global terrestrial biomes. Our findings reveal a consistent positive concave-down effect of biodiversity on forest productivity across the world, showing that a continued biodiversity loss would result in an accelerating decline in forest productivity worldwide. The BPR shows considerable geospatial variation across the world. The same percentage of biodiversity loss would lead to a greater relative (that is, percentage) productivity decline in the boreal forests of North America, Northeastern Europe, Central Siberia, East Asia, and scattered regions of South-central Africa and South-central Asia. In the Amazon, West and Southeastern Africa, Southern China, Myanmar, Nepal, and the Malay Archipelago, however, the same percentage of biodiversity loss would lead to greater absolute productivity decline. CONCLUSION Our findings highlight the negative effect of biodiversity loss on forest productivity and the potential benefits from the transition of monocultures to mixed-species stands in forestry practices. The BPR we discover across forest ecosystems worldwide corresponds well with recent theoretical advances, as well as with experimental and observational studies on forest and nonforest ecosystems. On the basis of this relationship, the ongoing species loss in forest ecosystems worldwide could substantially reduce forest productivity and thereby forest carbon absorption rate to compromise the global forest carbon sink. We further estimate that the economic value of biodiversity in maintaining commercial forest productivity alone is

Mapping tree density at a global scale

166 billion to

Ratio Estimation with Measurement Error in the Auxiliary Variate

490 billion per year. Although representing only a small percentage of the total value of biodiversity, this value is two to six times as much as it would cost to effectively implement conservation globally. These results highlight the necessity to reassess biodiversity valuation and the potential benefits of integrating and promoting biological conservation in forest resource management and forestry practices worldwide. Global effect of tree species diversity on forest productivity. Ground-sourced data from 777,126 global forest biodiversity permanent sample plots (dark blue dots, left), which cover a substantial portion of the global forest extent (white), reveal a consistent positive and concave-down biodiversity-productivity relationship across forests worldwide (red line with pink bands representing 95% confidence interval, right). The biodiversity-productivity relationship (BPR) is foundational to our understanding of the global extinction crisis and its impacts on ecosystem functioning. Understanding BPR is critical for the accurate valuation and effective conservation of biodiversity. Using ground-sourced data from 777,126 permanent plots, spanning 44 countries and most terrestrial biomes, we reveal a globally consistent positive concave-down BPR, showing that continued biodiversity loss would result in an accelerating decline in forest productivity worldwide. The value of biodiversity in maintaining commercial forest productivity alone—US

Statistical analysis of ratio estimators and their estimators of variances when the auxiliary variate is measured with error

166 billion to 490 billion per year according to our estimation—is more than twice what it would cost to implement effective global conservation. This highlights the need for a worldwide reassessment of biodiversity values, forest management strategies, and conservation priorities.

Influencia de la heterogeneidad del paisaje en la ocurrencia de incendios forestales en Chile Central

The global extent and distribution of forest trees is central to our understanding of the terrestrial biosphere. We provide the first spatially continuous map of forest tree density at a global scale. This map reveals that the global number of trees is approximately 3.04 trillion, an order of magnitude higher than the previous estimate. Of these trees, approximately 1.30 trillion exist in tropical and subtropical forests, with 0.74 trillion in boreal regions and 0.66 trillion in temperate regions. Biome-level trends in tree density demonstrate the importance of climate and topography in controlling local tree densities at finer scales, as well as the overwhelming effect of humans across most of the world. Based on our projected tree densities, we estimate that over 15 billion trees are cut down each year, and the global number of trees has fallen by approximately 46% since the start of human civilization.

Modeling 3D Canopy Structure and Transmitted PAR Using Terrestrial LiDAR

SUMMARY With auxiliary information that is well correlated with the primary variable of interest, ratio estimation of the finite population total may be much more efficient than alternative estimators that do not make use of the auxiliary variate. The well-known properties of ratio estimators are perturbed when the auxiliary variate is measured with error. In this contribution we examine the effect of measurement error in the auxiliary variate on the design-based statistical properties of three common ratio estimators. We examine the case of systematic measurement error as well as measurement error that varies according to a fixed distribution. Aside from presenting expressions for the bias and variance of these estimators when they are contaminated with measurement error we provide numerical results based on a specific population. Under systematic measurement error, the biasing effect is asymmetric around zero, and precision may be improved or degraded depending on the magnitude of the error. Under variable measurement error, bias of the conventional ratio-of-means estimator increased slightly with increasing error dispersion, but far less than the increased bias of the conventional mean-of-ratios estimator. In similar fashion, the variance of the mean-of-ratios estimator incurs a greater loss of precision with increasing error dispersion compared with the other estimators we examine. Overall, the ratio-of-means estimator appears to be remarkably resistant to the effects of measurement error in the auxiliary variate.

Métodos estadísticos paramétricos y no paramétricos para predecir variables de rodal basados en Landsat ETM+: una comparación en un bosque de Araucaria araucana en Chile

Forest inventory relies heavily on sampling strategies. Ratio estimators use information of an auxiliary variable (x) to improve the estimation of a parameter of a target variable (y). We evaluated the effect of measurement error (ME) in the auxiliary variate on the statistical performance of three ratio estimators of the target parameter total τy. The analyzed estimators are: the ratio-of-means, mean-of-ratios, and an unbiased ratio estimator. Monte Carlo simulations were conducted over a population of more than 14,000 loblolly pine (Pinus taeda L.) trees, using tree volume (v) and diameter at breast height (d) as the target and auxiliary variables, respectively. In each simulation three different sample sizes were randomly selected. Based on the simulations, the effect of different types (systematic and random) and levels (low to high) of MEs in x on the bias, variance, and mean square error of three ratio estimators was assessed. We also assessed the estimators of the variance of the ratio estimators. The ratio-of-means estimator had the smallest root mean square error. The mean-of-ratios estimator was found quite biased (20%). When the MEs are random, neither the accuracy (i.e. bias) of any of the ratio estimators is greatly affected by type and level of ME nor its precision (i.e. variance). Positive systematic MEs decrease the bias but increase the variance of all the ratio estimators. Only the variance estimator of the ratio-of-means estimator is biased, being especially large for the smallest sample size, and larger for negative MEs, mainly if they are systematic.

Modelación del crecimiento de bosques: estado del arte

Forest fi res are recognized as a serious problem. Despite its importance, research into modelling of forest fi re occurrence is lacking for the southern hemisphere, in particular for Chile. We investigated how landscape heterogeneity affects the probability of the occurrence of forest fi res in Central Chile. We fi tted a logistic regression model which included climatic, topographic, human-related and land-cover variables. Estimated probabilities of forest fi re occurrence increased positively

Sugerencias para mejorar la calidad de las revistas científicas chilenas: una aplicación en las ciencias forestales con Bosque

Abstract The heterogeneity and 3-dimensional (3D) organization of forest canopy elements is highly linked with the spatial variability of within and below canopy light. Using terrestrial LiDAR we studied the influence of several parameters in efficiently building 3D canopy models, and quantified below canopy light in 2 forest stands using ray-tracing. A voxel-based approach was used for canopy modeling, and a series of forest scenes were built for calculation of simulated structural variables (e.g., leaf area index, canopy openness). Through hypothesis testing, we found that simulated variables were consistent with the observed ones depending on: forest type, voxel size utilized in 3D modeling, and the zenith angle ranges used for calculations. Following below canopy light simulations were performed considering these 3 aspects. On average, estimates of light being transmitted overestimated measured light, and variance in below canopy light was maximum at lower values of measured light. This study presented a method to objectively define 3D modeling parameters for an efficient characterization of canopy structure, allowing to simulate trends in radiation flux transmitted to the forest floor. Improvements in the modeling process and ray-tracing parameterization were suggested.

Corrigendum: Mapping tree density at a global scale

Los bosques de Araucaria araucana tienen una alta importancia ecologica y cientifica. Aunque existen varios estudios ecologicos llevados a cabo en bosques de A. araucana, muy pocos han producido modelos cuantitativos. Se compararon metodos estadisticos parametricos y no parametricos para predecir variables de rodal en funcion de variables derivadas de Landsat ETM+ para dos rodales de A. araucana en el centro-sur de Chile. Los metodos parametricos fueron regresion lineal multiple (MLR), minimos cuadrados generalizados con una estructura de correlacion no nula (GLS), modelo lineal de efectos mixtos (LME) y minimos cuadrados parciales (PLS); mientras que los metodos no parametricos fueron: k-esimo vecino mas cercano (k-NN) y vecino mas similar (MSN). En orden descendente, numero de arboles por hectarea (N), volumen bruto (V), area basal (G) y altura dominante (Hdom), fueron las variables mas complejas de modelar por todos los metodos. El modelo lineal de efectos mixtos con efectos aleatorios conocidos (LME1) tuvo el mejor desempeno, alcanzando una raiz cuadrada de las diferencias (RMSD) para N y V de 18,31 y 4,08 % versus 33,06 y 33,05 % para el segundo mejor metodo, respectivamente. Despues de LME1, GLS se comporto mejor, y tambien toma en consideracion la correlacion espacial de los datos. Las diferencias fueron mayores entre metodos no parametricos que para los parametricos, con una diferencia de 10-15 % entre k-NN y MSN. Aunque los resultados obtenidos favorecen a los metodos parametricos, se destaca que los metodos no parametricos son tambien utiles, y la eleccion entre ambos metodos depende del objetivo del estudio.