Thinking outside the plot: monitoring forest biodiversity for social-ecological research

Protecting biodiversity, either for its own sake or for its value to humanity, is a principal goal of conservation efforts worldwide. For this reason, many studies on the social science of resource management and governance seek to quantify biodiversity outcomes. Here, we focus on the International Forestry Resources and Institutions program to demonstrate some of the challenges of quantitative biodiversity assessment and suggest ways to overcome them. One of this program's research goals is to understand the causes of biodiversity loss, which is explicitly assessed using plot-based forest sampling. Plot-based methods to capture biodiversity changes require huge amounts of data. Even if sampling is sufficient, existing protocols can only capture changes in the types of species actually sampled, typically trees. Other elements of biodiversity are not censused, including animals, herbs, shrubs, fungi, and epiphytes that may provide medicine, food, wildlife habitat, trade items, or cultural goods. Using case studies of two sites in Uganda, we demonstrate that more spatially extensive surveys targeting multiple types of data can give a broader picture of forest status and changes than can plot-based sampling alone; many relevant variables can be observed while traveling among plot points with little additional effort. Reviewing the ecological literature, we identify correlates of forest status that can supplement plot-based sampling. These include large trees, epiphyte-laden trees, culturally or commercially valuable species, large stumps, and evidence of hunting and trapping. Further, data elicited from local resource users can play an important role in biodiversity monitoring. These findings suggest that effective biodiversity monitoring may be within easier reach than previously thought, although robust comparisons among sites remains a challenge, especially when climate, soils, or site history differ greatly

Línea base de estudio de la biodiversidad, servicios ambientales y valores para la conservación de bosques secundarios y maduros en el Corredor Biológico Osa

Proyecto de Investigación. Instituto Tecnológico de Costa Rica. Escuela de Ingeniería Forestal, 2011Durante el 2009 se seleccionaron 14 sitios para monitorear los cambios de estructura vertical y horizontal, así como la provisión de servicios ambientales en bosques de diferentes estados de sucesión en el Corredor Biológico Osa (CBO). Las edades comprendieron bosques de 5-15 años, 15-30 años, mayores de 30 años y bosque viejo o primario. En el 2010 se seleccionaron cuatro sitios más, y para el 2011 se completó una muestra de veinte sitios, distribuidos en Piro, Matapalo, Los Mogos y Bahía Chal, Puntarenas, Costa Rica. En cada sitio se estableció una parcela permanente de muestreo (PPM) de 5000 m2 (50x100m) para un total de 20 PPM en las cuáles se han llevado a cabo diferentes investigaciones. Se evaluó la recuperación de la composición florística, diversidad, estructura horizontal y vertical. Se identificaron botánicamente los árboles con diámetro a la altura del pecho (dap) ≥ 5 cm, y se determinó la altura total, el dap y el gremio ecológico de las especies. Además, se estudió el comportamiento fenológico de los bosques midiendo la temporalidad de la floración y fructificación. Se estimó la biomasa arriba del suelo con el uso de sensores remotos y se desarrolló un estudio de fragmentación y conectividad del CBO. Se determinó la fracción de carbono y el peso específico básico de cuatro especies forestales. Conjuntamente, se cuantificó la biomasa sobre el suelo y el contenido de carbono orgánico en el suelo. Además, con diversos datos generados se determinó un tamaño óptimo de parcela para estimar biomasa y se empleó la variable de área basal para estimar biomasa arriba del suelo. Los resultados generales indican que los bosques secundarios son de alto valor para la conservación, además resguardan y se desarrollan especímenes de bosques primarios, endémicas y con algún grado de amenaza. Se comprobó que los bosques primarios al norte del CBO, difieren a los del sur, y que la abundancia de especies esciófitas y palmas provocaron las diferencias con los bosques secundarios. La recuperación de la composición florística, estructura horizontal y vertical a lo largo de la sucesión, indicó que los bosques alterados se están desarrollando adecuadamente, con una tendencia asemejarse a las unidades de crecimiento primario. Se logró proponer una metodología para generar modelos de biomasa aérea con imágenes satelitales individuales. Se encontró que el CB muestra procesos de recuperación y deforestación de la cobertura en forma simultánea, reportándose una disminución en el número de parches de bosque durante el periodo en estudio. Además, se encontraron diferencias en el peso específico básico y la fracción de carbono en las especies en estudio y se observó que se deben tomar en consideración cuando se pretenda calcular el almacenamiento de carbono de una especie en particular. Se encontraron tendencias que mostraron el aumento en la biomasa conforme se incrementaba la etapa de sucesión del bosque y se encontró que el tamaño óptimo de parcela para estimar biomasa en los bosques húmedos tropicales de la Península de Osa, está entre 1000 y 1500 m2. Además de los resultados numéricos, se efectuaron dos ponencias en congresos nacionales y dos en congresos internacionales. Se ejecutaron ocho tesis de graduación y se participó en un encuentro de investigación realizado en el Área de Conservación Osa

Genetic consequences of tropical second-growth forest regeneration

Secondary forests are more extensive than old-growth forests in many tropical regions, yet the genetic composition of colonizing populations is poorly understood. We analyzed the parentage of a founder population of 130 individuals of the canopy palm Iriartea deltoidea in a 24-year-old second-growth forest in lowland Costa Rica. Among 66 trees in adjacent old-growth forest, only two individuals contributed 56% of the genes in founders. Second-growth trees had lower genetic diversity and larger patches of similar genotypes than old-growth trees. Recovery of genetic diversity of populations in tropical second-growth forests may take many generations and will require continued dispersal from genetically diverse source populations

Beyond carbon: Redefining forests and people in the global ecosystem services market

The need to reduce emissions from deforestation and forest degradation is more urgent now than ever. International efforts through REDD+, CDM and voluntary carbon markets aim to encourage complementary activities of forest preservation, reforestation, afforestation and sustainable forest management. Many existing programs for sustainable forest management, agriculture and development dovetail with payment for ecosystem services (PES) programs in their similar concerns regarding the allocation of rights and responsibilities, agreements on service provision, and the verification and quantification of benefits. Recent efforts to link biodiversity conservation with national scale REDD+ initiatives depend on the explicit regulatory linkage of biodiversity preservation goals with carbon targets. We emphasize the need to include biodiversity conservation and sustainable development as integral components of forest carbon projects. As fundamental social, political and cultural issues have yet to be addressed in the current market structure, we urge a better understanding of the tradeoffs between the full suite of ecosystem services provided by different forest types. Here, we provide a conceptual framework for the integration of payment for ecosystem services programs with biodiversity conservation and sustainable development

Resilience and alternative stable states of tropical forest landscapes under shifting cultivation regimes

Shifting cultivation is a traditional agricultural practice in most tropical regions of the world and has the potential to provide for human livelihoods while hosting substantial biodiversity. Little is known about the resilience of shifting cultivation to increasing agricultural demands on the landscape or to unexpected disturbances. To investigate these issues, we develop a simple social-ecolgical model and implement it with literature-derived ecological parameters for six shifting cultivation landscapes from three continents. Analyzing the model with the tools of dynamical systems analysis, we show that such landscapes exhibit two stable states, one characterized by high forest cover and agricultural productivity, and another with much lower values of these traits. For some combinations of agricultural pressure and ecological parameters both of these states can potentially exist, and the actual state of the forest depends critically on its historic state. In many cases, the landscapes' 'ecological resilience', or amount of forest that could be destroyed without shifting out of the forested stability domain, declined substantially at lower levels of agricultural pressure than would lead to maximum productiviy. A measure of 'engineering resilience',- the recovery time from standardized disturbances, was independent of ecological resilience. These findings suggest that maximization of short-term agricultural output may have counterproductive impacts on the long-term productivity of shifting cultivation landscapes and the persistence of forested areas

Unveiling the species-rank abundance distribution by generalizing the Good-Turing sample coverage theory

Based on a sample of individuals, we focus on inferring the vector of species relative abundance of an entire assemblage and propose a novel estimator of the complete species-rank abundance distribution (RAD). Nearly all previous estimators of the RAD use the conventional plug-in estimator pi(sample relative abundance) of the true relative abundance piof species i. Because most biodiversity samples are incomplete, the plug-in estimators are applied only to the subset of species that are detected in the sample. Using the concept of sample coverage and its generalization, we propose a new statistical framework to estimate the complete RAD by separately adjusting the sample relative abundances for the set of species detected in the sample and estimating the relative abundances for the set of species undetected in the sample but inferred to be present in the assemblage. We first show that piis a positively biased estimator of pifor species detected in the sample, and that the degree of bias increases with increasing relative rarity of each species. We next derive a method to adjust the sample relative abundance to reduce the positive bias inherent in pi. The adjustment method provides a nonparametric resolution to the longstanding challenge of characterizing the relationship between the true relative abundance in the entire assemblage and the observed relative abundance in a sample. Finally, we propose a method to estimate the true relative abundances of the undetected species based on a lower bound of the number of undetected species. We then combine the adjusted RAD for the detected species and the estimated RAD for the undetected species to obtain the complete RAD estimator. Simulation results show that the proposed RAD curve can unveil the true RAD and is more accurate than the empirical RAD. We also extend our method to incidence data. Our formulas and estimators are illustrated using empirical data sets from surveys of forest spiders (for abundance data) and soil ciliates (for incidence data). The proposed RAD estimator is also applicable to estimating various diversity measures and should be widely useful to analyses of biodiversity and community structure

Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages

Aims: In ecology and conservation biology, the number of species counted in a biodiversity study is a key metric but is usually a biased underestimate of total species richness because many rare species are not detected. Moreover, comparing species richness among sites or samples is a statistical challenge because the observed number of species is sensitive to the number of individuals counted or the area sampled. For individual-based data, we treat a single, empirical sample of species abundances from an investigator-defined species assemblage or community as a reference point for two estimation objectives under two sampling models: estimating the expected number of species (and its unconditional variance) in a random sample of (i) a smaller number of individuals (multinomial model) or a smaller area sampled (Poisson model) and (ii) a larger number of individuals or a larger area sampled. For sample-based incidence (presence-absence) data, under a Bernoulli product model, we treat a single set of species incidence frequencies as the reference point to estimate richness for smaller and larger numbers of sampling units. Methods: The first objective is a problem in interpolation that we address with classical rarefaction (multinomial model) and Coleman rarefaction (Poisson model) for individual-based data and with sample-based rarefaction (Bernoulli product model) for incidence frequencies. The second is a problem in extrapolation that we address with sampling-theoretic predictors for the number of species in a larger sample (multinomial model), a larger area (Poisson model) or a larger number of sampling units (Bernoulli product model), based on an estimate of asymptotic species richness. Although published methods exist for many of these objectives, we bring them together here with some new estimators under a unified statistical and notational framework. This novel integration of mathematically distinct approaches allowed us to link interpolated (rarefaction) curves and extrapolated curves to plot a unified species accumulation curve for empirical examples. We provide new, unconditional variance estimators for classical, individual-based rarefaction and for Coleman rarefaction, long missing from the toolkit of biodiversity measurement. We illustrate these methods with datasets for tropical beetles, tropical trees and tropical ants. Important Findings: Surprisingly, for all datasets we examined, the interpolation (rarefaction) curve and the extrapolation curve meet smoothly at the reference sample, yielding a single curve. Moreover, curves representing 95% confidence intervals for interpolated and extrapolated richness estimates also meet smoothly, allowing rigorous statistical comparison of samples not only for rarefaction but also for extrapolated richness values. The confidence intervals widen as the extrapolation moves further beyond the reference sample, but the method gives reasonable results for extrapolations up to about double or triple the original abundance or area of the reference sample. We found that the multinomial and Poisson models produced indistinguishable results, in units of estimated species, for all estimators and datasets. For sample-based abundance data, which allows the comparison of all three models, the Bernoulli product model generally yields lower richness estimates for rarefied data than either the multinomial or the Poisson models because of the ubiquity of non-random spatial distributions in nature. © 2012 The Author. Published by Oxford University Press on behalf of the Institute of Botany, Chinese Academy of Sciences and the Botanical Society of China. All rights reserved

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p less than 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes