Change In Tropical Forests Research Articles

Land use and land cover change and its implications in Kagera river basin, East Africa

The Kagera basin has experienced major land use/cover changes in tropical forests, woodlands, and savannas due to the incessant conversion of land to agricultural and settlement use. While the land use/cover change has accelerated land degradation, biodiversity loss, and climate change in the region, few studies exist on the drivers and implications of land use/cover dynamics in the basin. The study quantified the historical land use/cover changes beginning from 1984 to 2011 and predicted future changes using multi-level data-sets. We also examined population growth and government policies as they relate to land cover/use change. Data-sets from disparate sources consisting of multi-temporal satellite images, digital elevation model, population, and relevant ancillary data were used in the study. A combination of post-classification change detection method, intensity analysis, and Markov chain models were used to analyze and evaluate historical land use/cover changes and predict future change scenarios. We observed a major expansion of agriculture land at the loss of woodland savanna. Overall, change is more pronounced and fast during the period 1984–1994 and relatively slow during 1994–2011. Category and transition intensities were more pronounced in 1984–1994 than 1994–2011. These changes suggest policy intervention from government and individual response during the period 1994–2011. In addition, the study identified population growth, settlement expansion, and local policies as key drivers of land use/cover change. Future scenarios indicate: (1) increase in agriculture land use, (2) loss in woodland savanna and forest cover, and (3) significant wetland loss to agriculture.

Improving estimates of biomass change in buttressed trees using tree taper models

SummaryRepeat censuses of tree plots are key tools for investigating forest carbon fluxes. Current measurement procedures for trees with buttresses or trunk irregularities – trees that account for a large fraction of tropical forest biomass –introduce substantial systematic error in plot‐level estimates of biomass change. The diameters of buttressed trees are measured above the standard height of 1·3 m, and the measurement heights on individual trees are often moved upwards as buttresses grow. Because tree trunks taper (diameter decreases with height), biomass growth in buttressed individuals tends to be underestimated. Methods have been introduced to correct biomass growth estimates in individual trees for increases in measurement height; however, these methods change the distribution of effective measurement heights over time, introducing biases in plot‐level estimates of biomass change.In this study, we developed and applied new methods to measure and model trunk taper, and to use taper models to correct estimates ofAGBchange for changing measurement heights. We measured trunk taper above buttresses in 190 stems on Barro Colorado Island, Panama (BCI), a site where more than half of forest biomass is in trees measured above standard height. We compared proposed taper models to see which best described our measured taper data, then used the best taper model to correct for changing measurement heights in the historical plot data. Specifically, for all diameter measurements taken above 1·3 m, we calculated equivalent diameters at 1·3 m and substituted these into allometric equations to examine biomass change over time.We found that measured taper was best fit by an exponential model with a rate parameter that varied with measured diameter (tree size), height of measurement (buttress height) and species. Whereas uncorrected data show a decrease in biomass of 0·21% year−1between 1985 and 2010 onBCI, taper‐corrected data show an increase of 0·18% year−1.The novel correction method presented here converts all measured diameters to one standard effective measurement height. This corrects for biases at the plot level and provides a stronger foundation for measuring biomass change in tropical forests.

Foreign capital, forest change and regulatory compliance in Congo Basin forests

Tropical forest change is driven by demand in distant markets. Equally, investments in tropical forest landscapes by capital originating from distant emerging economies are on the rise. Understanding how forest outcomes vary by investment source is therefore becoming increasingly important. We empirically evaluate the relationship between investment source and deforestation from 2000 to 2010 in the Republic of Congo. A Congolese forestry code was implemented in 2000 to mitigate degradation of production forests by standardizing all logging in the country according to sustainable forest management (SFM) guidelines. Following the implementation of this law, the majority (73%) of Congo’s production forests were managed by European (40%) and Asian (33%) companies. European concessions had the highest rates of total and core deforestation, followed by Asian concessions, indicating that the fragmentation of intact forests in Congo is strongly associated with industrial logging fueled by foreign capital. European concession holders were also far more likely to comply with SFM policies, followed by Asian concessions, suggesting that compliance with Sustainable Forest Management policies may not mitigate degradation in tropical production forests. Further evaluation of the relationship between investment source, regulatory compliance, and outcomes in tropical countries is essential for effective conservation of tropical forest ecosystems.

Recovering from forest-to-pasture conversion: leaf decomposition in Central Amazonia, Brazil

Abstract:Land-use change in tropical forests can affect the micro-environment for decomposition under modified canopies. In turn, decomposition may have consequences for soil fertility and ecosystem sustainability. The effects of the conversion from primary forest to pastures on leaf-litter decomposition and its recovery in secondary forests 20 y after pasture abandonment were evaluated using litterbags and a general mixed model approach. Litterbags containing leaf-litter of two contrasting qualities were placed in those three ecosystems and the mass remaining after 90 d was quantified. The mass remaining was 50% lower in high- than in low-quality leaves. The effects of the decomposition environment depended on the substrate quality. Although differences among ecosystems were not significant for low-quality leaves, high-quality leaves decomposed more slowly in pastures than in forests (mean dry mass remaining was 38% in pastures, 14% in secondary and 12% in primary forest). The decomposition of common substrates did not differ among forests. Results show that the conversion of primary forest to pasture may produce conditions for slower decomposition; however, these conditions appear be restored to a great extent in 20-y-old secondary forests.

Rapid structural and compositional change in an old-growth subtropical forest: using plant traits to identify probable drivers.

Recent studies have shown directional changes in old-growth tropical forests, but changes are complex and diverse, and their drivers unclear. Here, we report rapid net structural and compositional changes in an old-growth subtropical forest and we assess the functional nature of these changes to test hypothetical drivers including recovery from past disturbances, reduction in ungulate browsing, CO2 fertilization, and increases in rainfall and temperature. The study relies on 15 years of demographic monitoring within 8 ha of subtropical montane forest in Argentina. Between 1992 and 2007, stem density markedly increased by 50% (12 stems ha−1 y−1) and basal area by 6% (0.13 m2 ha−1 y−1). Increased stem density resulted from enhanced recruitment of understory treelets (Piper tucumanum, Eugenia uniflora, Allophylus edulis) into small size classes. Among 27 common tree species, net population growth was negatively correlated with maximum tree size and longevity, and positively correlated with leaf size and leaf nutrient content, especially so when initial population size was controlled for. Changes were inconsistent with predictions derived from past disturbances (no increase in shade-tolerant or long-lived late-succesional species), rainfall or temperature increase (no increase in evergreen or deciduous species, respectively). However, the increase in nutrient-rich soft-leaved species was consistent with exclusion of large herbivores two decades before monitoring started; and CO2 fertilization could help explain the disproportionate increase in small stems. Reductions in populations of large vertebrates have been observed in many otherwise undisturbed tropical forests, and our results suggest they can have important structural and functional repercussions in these forests.

Carbon stocks in tropical forests decrease with liana density

Tropical forests are experiencing structural changes that may reduce carbon storage potential. The recent increase in liana abundance and biomass is one such potential change. Lianas account for approximately 25 per cent of woody stems and may have a strong impact on tree dynamics because severe liana infestation reduces tree growth and increases tree mortality. Based on forest inventory data from 0.1 ha plots, we evaluated the association between above-ground carbon stocks and liana abundance in 145 tropical forests worldwide. Liana abundance was negatively associated with carbon stocks of large trees (greater than 10 cm diameter), while it was not related to small trees (10 cm diameter or less). Results suggest that liana abundance may have pervasive effects on carbon stocks in tropical forests, as large trees store about 90 per cent of total forest carbon. We stress the need to include liana abundance in carbon stocks estimates, as this can enhance the accuracy of predictions of global changes in tropical forests.

Detecting tropical forest biomass dynamics from repeated airborne lidar measurements

Abstract. Reducing uncertainty of terrestrial carbon cycle depends strongly on the accurate estimation of changes of global forest carbon stock. However, this is a challenging problem from either ground surveys or remote sensing techniques in tropical forests. Here, we examine the feasibility of estimating changes of tropical forest biomass from two airborne lidar measurements of forest height acquired about 10 yr apart over Barro Colorado Island (BCI), Panama. We used the forest inventory data from the 50 ha Center for Tropical Forest Science (CTFS) plot collected every 5 yr during the study period to calibrate the estimation. We compared two approaches for detecting changes in forest aboveground biomass (AGB): (1) relating changes in lidar height metrics from two sensors directly to changes in ground-estimated biomass; and (2) estimating biomass from each lidar sensor and then computing changes in biomass from the difference of two biomass estimates, using two models, namely one model based on five relative height metrics and the other based only on mean canopy height (MCH). We performed the analysis at different spatial scales from 0.04 ha to 10 ha. Method (1) had large uncertainty in directly detecting biomass changes at scales smaller than 10 ha, but provided detailed information about changes of forest structure. The magnitude of error associated with both the mean biomass stock and mean biomass change declined with increasing spatial scales. Method (2) was accurate at the 1 ha scale to estimate AGB stocks (R2 = 0.7 and RMSEmean = 27.6 Mg ha−1). However, to predict biomass changes, errors became comparable to ground estimates only at a spatial scale of about 10 ha or more. Biomass changes were in the same direction at the spatial scale of 1 ha in 60 to 64% of the subplots, corresponding to p values of respectively 0.1 and 0.033. Large errors in estimating biomass changes from lidar data resulted from the uncertainty in detecting changes at 1 ha from ground census data, differences of approximately one year between the ground census and lidar measurements, and differences in sensor characteristics. Our results indicate that the 50 ha BCI plot lost a significant amount of biomass (−0.8 Mg ha−1 yr−1 ± 2.2(SD)) over the past decade (2000–2010). Over the entire island and during the same period, mean AGB change was 0.2 ± 2.4 Mg ha−1 yr−1 with old growth forests losing −0.7 Mg ha−1 yr−1 ± 2.2 (SD), and secondary forests gaining +1.8 Mg ha yr−1 ± 3.4 (SD) biomass. Our analysis suggests that repeated lidar surveys, despite taking measurement with different sensors, can estimate biomass changes in old-growth tropical forests at landscape scales (>10 ha).

The tropical forest in south east Asia: Monitoring and scenario modeling using synthetic aperture radar data

Tropical forests play a major role in storing large carbon stocks and regulating energy, and water fluxes, but such forest cover is decreasing rapidly in spite of the policy attention on reducing deforestation. High-resolution spatiotemporal maps are unavailable for the forests in majority of the tropical regions in Asia because of the persistent cloud cover and haze cover. Recent advances in radar remote sensing have provided weather-independent data of earth surface, thus offering an opportunity to monitor tropical forest change processes with relatively high spatiotemporal resolutions. In this research, we aim to examine the tropical deforestation process and develop a spatial model to simulate future forest patterns under various scenarios. Riau Province from central Sumatra of Indonesia is selected as the study area; this province has received much attention worldwide because the highest CO2 emission resulting from tropical deforestation has been recorded. Annual time series PALSAR data from 2007 to 2010 were analyzed for forest mapping and detecting land cover changes. A spatial model was calibrated using the Bayesian method. Modeling parameters were customized for the local subregions that allocate deforestation on the basis of their empirical relationships to physical and socioeconomic drivers. The model generated landscape spatial patterns mirrored the possible locations and extent of deforested areas by 2030 and provided time-series crucial information on forest landscape under various scenarios for future landscape management projects. The results suggested that the current deforestation process is in a critical stage where some subregions may face unprecedented stress on primary forest costing rivers and forest ecosystems by the end of 2020. The perspective views of Riau Province generated by the model highlighted the need for forest/environmental planning controls for the conservation of environmentally sensitive areas.

Environmental factors correlated with species diversity in different tropical rain forest types in Jianfengling, Hainan Island, China

Aims Environmental factors are key factors impacting species distribution and determining species richness. An important question is whether the major environmental factor are the same in old-growth forests and secondary forests produced by logging. This question is very important for monitoring long-term changes in secondary tropical rain forests. Methods A unique, robust data set consisting of 164625 m2 quadrats in a 160 km2 tropical rainforest was set up from August 2007 to June 2009 in the middle of the Jianfengling Natural Reserve, Hainan Island, China. Forest logging history was determined and 17 environmental factors were measured for each quadrat. First, we analyzed the relationship among these 17 factors. Then, these quadrats were classified into three forest types with different logging history: old-growth forests, diameter-limit logged forests and clear-cut forests. Canonical correspondence analysis (CCA) was used to analyze the environmental factors impacting species distributions. Step-forward multiple regression was used with and without considering spatial autocorrelation in the data set to disclose which environmental factors determined species richness. Important findings The environmental factors impacting species distribution and determining species richness changed because of differences of forests logging history. Elevation is the second most important factor influencing patterns of species diversity. CCA showed that species distribution in old-growth forests is closely related toelevation, soil exchangeable calcium content, soil exchangeable magnesium content and four soil physical factors (soil density, maximum water holding content, capillary water-holding content and capillary porosity). Importance of soil total phosphorous content and available phosphorous content was greater in logged forests, but the importance of soil exchangeable calcium content and exchangeable magnesium content was lower in clear-cut forests. Multiple regression analysis also showed that species richness was significantly correlated with elevation and soil exchangeable calcium in the old-growth forests. While species richness was correlated with elevation, soil total phosphorous content and soil available potassium content in the diameter-limit logged forests and was only correlated with elevation in the clear-cut forests. Furthermore, it is suggested it is better to compare the spatial autocorrelation models with other models to describe the relationship between environmental factors and species richness, even it does not always exist in the ecological data set with spatial characteristics.

Local Perceptions of Climate Variability and Change in Tropical Forests of Papua, Indonesia

Boissière, M., B. Locatelli, D. Sheil, M. Padmanaba, and E. Sadjudin. 2013. Local perceptions of climate variability and change in tropical forests of Papua, Indonesia. Ecology and Society 18(4): 13. https://doi.org/10.5751/ES-05822-180413

Conversion of the Amazon rainforest to agriculture results in biotic homogenization of soil bacterial communities

The Amazon rainforest is the Earth's largest reservoir of plant and animal diversity, and it has been subjected to especially high rates of land use change, primarily to cattle pasture. This conversion has had a strongly negative effect on biological diversity, reducing the number of plant and animal species and homogenizing communities. We report here that microbial biodiversity also responds strongly to conversion of the Amazon rainforest, but in a manner different from plants and animals. Local taxonomic and phylogenetic diversity of soil bacteria increases after conversion, but communities become more similar across space. This homogenization is driven by the loss of forest soil bacteria with restricted ranges (endemics) and results in a net loss of diversity. This study shows homogenization of microbial communities in response to human activities. Given that soil microbes represent the majority of biodiversity in terrestrial ecosystems and are intimately involved in ecosystem functions, we argue that microbial biodiversity loss should be taken into account when assessing the impact of land use change in tropical forests.

A comparison of forest cover maps in Mainland Southeast Asia from multiple sources: PALSAR, MERIS, MODIS and FRA

The uncertainty in tracking tropical forest extent and changes substantially affects our assessment of the consequences of forest change on the global carbon cycle, biodiversity and ecosystem services. Recently cloud-free imagery useful for tropical forest mapping from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) has become available. We used PALSAR 50-m orthorectified mosaic imagery in 2009 and a decision tree method to conduct land cover classification and generate a 2009 forest map, which was evaluated using 2106 field photos from the Global Geo-referenced Field Photo Library (http://www.eomf.ou.edu/photos). The resulting land cover classification had a high overall accuracy of 93.3% and a Kappa Coefficient of 0.9. The PALSAR-based forest map was then compared with three existing forest cover products at three scales (regional, national, and continental): the Food and Agriculture Organization of the United Nations (FAO) Forest Resources Assessments (FRA) 2010, Global Land Cover Map with MERIS (GlobCover) 2009, and the MODIS Terra+Aqua Land Cover Type product (MCD12Q1) 2009. The intercomparison results show that these four forest datasets differ. The PALSAR-based forest area estimate is within the range (6.1–9.0×105km2) of the other three products and closest to the FAO FRA 2010 estimate. The spatial disagreements of the PALSAR-based forest, MCD12Q1 forest and GlobCover forest are evident; however, the PALSAR-based forest map provides more details (50-m spatial resolution) and high accuracy (the Producer's and the User's Accuracies were 88% and 95%, respectively) and PALSAR can be used to evaluate MCD12Q1 2009 and GlobCover 2009 forest maps. Given the higher spatial resolution, PALSAR-based forest products could further improve the modeling accuracy of carbon cycle in tropical forests.

Detection of Large-Scale Forest Canopy Change in Pan-Tropical Humid Forests 2000–2009 With the SeaWinds Ku-Band Scatterometer

We analyzed the 10-year record (1999-2009) of SeaWinds Ku-band microwave backscatter from humid tropical forest regions in South America, Africa, and Indonesia/Malaysia. While backscatter was relatively stable across much of the region, it declined by 1-2 dB in areas of known large-scale deforestation, and increased by up to 1-2 dB in areas of secondary forest or plantation forest growth and in major metropolitan areas. The reduction in backscatter over 142 18.5 km × 18.5 km blocks of tropical forest was correlated with gross forest cover loss (as determined from Landsat data analysis) (R = -0.78); this correlation improved when restricted to humid tropical forest blocks in South America with high initial forest cover (R = -0.93, n = 22). This study shows that scatterometer-based analyses can provide an important geophysical data record leading to robust identification of the spatial patterns and timing of large-scale change in tropical forests. The coarse spatial resolution of SeaWinds ( ~ 10 km) makes it unsuitable for mapping deforestation at the scale of land-use activity. However, due to a combination of instrument stability, sensitivity to canopy change and insensitivity to atmospheric effects, and straight-forward data processing, Ku-band scatterometery can provide a fully independent assessment of large-scale tropical forest canopy dynamics which may complement the interpretation of higher resolution optical remote sensing.

Combining long-term land cover time series and field observations for spatially explicit predictions on changes in tropical forest biodiversity

Combining spatially explicit land cover data from remote-sensing and faunal data from field observations is increasingly applied for landscape-scale habitat and biodiversity assessments, but without modelling changes quantitatively over time. In a novel approach, we used a long-term time series including historical map data to predict the influence of one century of tropical forest change on keystone species or indicator groups in the Kakamega–Nandi forests, western Kenya. Four time steps of land cover data between 1912/13 and 2003, derived from Landsat satellite imagery, aerial photography and old topographic maps, formed the basis for extrapolating species abundance data on the army ant Dorylus wilverthi, the guild of ant-following birds and three habitat guilds of birds differing in forest dependency. To predict the species' spatio-temporal distribution, we combined spatially explicit geographical information system (GIS)-based modelling with statistical modelling, that is, ordinary least square (OLS) regression models for D. wilverthi and simultaneous autoregressive (SAR) models for ant-following birds. We directly related bird habitat guilds to five forest classes as distinguished in the land cover time series. Extrapolation results over time predict dramatic losses in abundance for D. wilverthi (56%), ant-following birds (58%) and forest bird individuals in general (47%) due to a forest loss of 31% and small-scale fragmentation within the past century. Extrapolations based on a scenario of further deforestation revealed the negative consequences of clearing and splitting up continuous forest into isolated patches, whereas a reforestation scenario suggests the positive impact of natural forest regrowth and indigenous-tree planting. This study demonstrates the high potential of integrating remotesensing and field-based faunal data for landscape-scale quantitative assessments over time. In addition, it shows the suitability of extrapolation studies for evaluating measures of forest conservation.

A satellite data set for tropical forest area change assessment

A database of largely cloud-free (less than 2.5% of all sites have more than 5% cloud cover), geo-referenced 20 km × 20 km sample sites of 30 m resolution optical satellite imagery have been prepared for the 1990 and 2000 epochs. This spans the tropics with a systematic sample located at the degree confluence points of the geographic grid. The resulting 4016 sample pairs are to be used to measure changes in the area of forest cover between the two epochs. The primary data source was the National Aeronautics and Space Administration's (NASA's) global land survey (GLS) data sets. Visual screening of GLS images at all 4016 confluence points from each date identified 2868 suitable pairs where no better alternatives exist (71.6% of the sample). Better alternatives could be found for 26.6% of the sample, substituting cloudy or missing GLS data sets at one or the other epoch or both (GLS-1990 or GLS-2000). Gaps were filled from the United States Geological Survey (USGS) Landsat archives (1070 samples), data from other Landsat archives (53 samples) or with alternatives to Landsat, that is, 15 samples from Satellite Pour l'Observation de la Terre (SPOT). This increased the effective number of sample pairs to 3945 representing 98% of all target samples. No suitable image pairs were found for 71 confluence points, which were not randomly distributed, but mostly concentrated in the Congo basin, where around 15% of the region remains un-sampled. Variations in date of image acquisition and geometric fidelity are documented. Results highlight the importance of combining systematic data-processing schemes with targeted image acquisition and archiving strategies for global scale applications such as deforestation monitoring and shows that by replacing cloudy or missing GLS data with alternative imagery, the overall coverage of the sample sites within the ecological zones ‘Tropical rainforest’ and ‘Tropical mountain system’ can be improved by 16%.

Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and Kalimantan, Indonesia

Monitoring loss of humid tropical forests via remotely sensed imagery is critical for a number of environmental monitoring objectives, including carbon accounting, biodiversity, and climate modeling science applications. Landsat imagery, provided free of charge by the U.S. Geological Survey Center for Earth Resources Observation and Science (USGS/EROS), enables consistent and timely forest cover loss updates from regional to biome scales. The Indonesian islands of Sumatra and Kalimantan are a center of significant forest cover change within the humid tropics with implications for carbon dynamics, biodiversity maintenance and local livelihoods. Sumatra and Kalimantan feature poor observational coverage compared to other centers of humid tropical forest change, such as Mato Grosso, Brazil, due to the lack of ongoing acquisitions from nearby ground stations and the persistence of cloud cover obscuring the land surface. At the same time, forest change in Indonesia is transient and does not always result in deforestation, as cleared forests are rapidly replaced by timber plantations and oil palm estates. Epochal composites, where single best observations are selected over a given time interval and used to quantify change, are one option for monitoring forest change in cloudy regions. However, the frequency of forest cover change in Indonesia confounds the ability of image composite pairs to quantify all change. Transient change occurring between composite periods is often missed and the length of time required for creating a cloud-free composite often obscures change occurring within the composite period itself. In this paper, we analyzed all Landsat 7 imagery with <50% cloud cover and data and products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify forest cover loss for Sumatra and Kalimantan from 2000 to 2005. We demonstrated that time-series approaches examining all good land observations are more accurate in mapping forest cover change in Indonesia than change maps based on image composites. Unlike other time-series analyses employing observations with a consistent periodicity, our study area was characterized by highly unequal observation counts and frequencies due to persistent cloud cover, scan line corrector off (SLC-off) gaps, and the absence of a complete archive. Our method accounts for this variation by generating a generic variable space. We evaluated our results against an independent probability sample-based estimate of gross forest cover loss and expert mapped gross forest cover loss at 64 sample sites. The mapped gross forest cover loss for Sumatra and Kalimantan was 2.86% of the land area, or 2.86Mha from 2000 to 2005, with the highest concentration having occurred in Riau and Kalimantan Tengah provinces.

Uncertainty in the construction of global knowledge of tropical forests

Knowledge of tropical forest change remains uncertain, affecting our ability to produce accurate estimates of globally aggregated parameters to support clear global statements about ‘the tropical forests’. This paper reviews current methods for constructing global knowledge of changes in tropical forest area, carbon density, biodiversity and ecosystem services. It finds a deficiency in formal institutions for global measurement and constructing global knowledge. In their absence, informal institutions have proliferated, increasing the spread of estimates. This is exacerbated by dependence on inaccurate official statistics, which has limited construction of knowledge about forest area change through modelling. Employing the new concept of the Knowledge Exchange Chain shows the interdependence of different disciplines in constructing composite information. Limitations linked to compartmentalization and scale are present, as predicted by the ‘post-normal hypothesis’. Disciplinary compartmentalization has impeded construction of information about forest carbon and biodiversity change. There is growth in interdisciplinary research into modelling forest change and estimating carbon emissions using remote sensing data, but not in studying biodiversity. Continuing uncertainty has implications for implementing the Reduced Emissions from Deforestation and Degradation (REDD) scheme. Uncertainty could be reduced by expanding formal scientific institutions, e.g. by establishing an operational scientific global forest monitoring system, and devising formal generic rules for constructing global environmental knowledge.

Effects of Plot Size and Census Interval on Descriptors of Forest Structure and Dynamics

ABSTRACTThis study was designed to explicitly formulate the effect of census interval and plot size on the variability of descriptors of tropical forest structure (stand density, basal area, aboveground biomass [AGB]) and dynamic (tree growth, mortality and recruitment rates, biomass fluxes). A unique dataset from a broad plot network (37.5 ha) surveyed every 2 yr over a 16‐yr period was used to develop and parameterize a new statistical model predicting the coefficients of variation for each forest descriptor. More than 90 percent of the inherent variability of these coefficients was predicted using a simple model including plot size and census interval in a Bayesian modeling framework. All descriptors of forest structure varied by &lt;10 percent for plot sizes &gt;2 ha. Among the descriptors of forest dynamics, AGB loss was the most variable. The number of 6.25 ha plots required to estimate its mean, over a 16‐yr period, within a 20 percent error of the mean remains above four. This contrasts with a relative constant flux of biomass entering the plot through tree growth and tree recruitment. Tree growth was remarkably well estimated with &lt;15 percent variability for a 2‐yr census in a plot of 2 ha. This study provides an easy method to assess dataset limitations in efforts to estimate descriptors of forest structure and dynamic, which is of primary importance to decipher any clear consequences of global change in tropical forests.

Landscape patterns and environmental controls of deciduousness in forests of central Panama

ABSTRACTAim Dry season deciduousness affects intra‐ and inter‐annual patterns of carbon, water and energy balance in seasonal tropical forests. Because it is affected by rainfall, temperature and solar radiation, deciduousness may be an indicator of the response of vegetation to climate change. Better understanding of how spatial patterns of deciduousness are affected by climate and other environmental gradients will improve the ability to predict responses to climate change. This study develops remote sensing methods for quantifying tropical forest deciduousness and examines the relationship between deciduousness and environmental factors in semi‐deciduous tropical forest.Location Central Panama.Methods I applied spectral mixture analysis (SMA) and the normalized difference vegetation index (NDVI) to Landsat images to predict deciduousness which was ground‐truthed with field observations of the percentage of overstorey deciduous trees. Using predicted deciduousness from SMA, patterns of deciduousness at three spatial scales were analysed. I determined how deciduousness varied spatially with rainfall and geological substrate.Results Both SMA and NDVI had strong correlations (r &gt; 0.9) with field observations of deciduousness. On a landscape scale, deciduousness increased as rainfall decreased, but geological substrate altered this relationship. On some geological substrates, deciduousness was much greater than expected for a given rainfall total or showed a slight but significant increase with rainfall. At an intermediate spatial scale, there were highly deciduous patches from 3 to 250 ha in size embedded in non‐deciduous forest, which may have resulted from topography, soil variation or past land use.Main conclusions Dry season deciduousness can be accurately quantified using satellite images indicating that remote sensing can be a valuable tool for detecting change and understanding ecosystem processes in tropical forests from landscape to regional scales.

Understanding deforestation in the southern Yucatán: insights from a sub-regional, multi-temporal analysis

The southern Yucatan has been identified as a deforestation hot spot. Land-change studies document the extent of forest loss at a regional scale, and case studies provide insights into the drivers of deforestation at the household level. Those studies have paid minimal attention to sub-regional analysis, especially to discrete land-management units above the household level. This analysis of upland forest change addresses the range of variation in deforestation among 96 ejidos (communal lands) and the Calakmul Biosphere Reserve, the two dominant land-tenure and land-management units in the region. Satellite imagery, census, and land-tenure data are used to establish the extent and location of deforestation patterns, and multivariate techniques are employed to identify biophysical and socioeconomic variables that explain such patterns. Results show that, for the 1984–1993 period, deforestation in the southern Yucatan was not as prevalent as implied by its hot spot designation. Three clusters of deforestation are identified. A logistic regression analysis establishes that size of forest holdings, population growth, and location in the precipitation gradient correlate with ejidos that experienced higher deforestation rates than the rest of the land-tenure units. For the 1993–2000 period, conservation programs and changes in the economic context of this “hollow frontier” contributed to reduce deforestation rates and extent. This analysis illustrates the spatio-temporal heterogeneity of much tropical forest change and caution that it should bring to simple formulations of modeling this change and prescribing policies to control it.