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Plinio Sist 10,88 | Bonaventure Sonke 60 | J. Daniel Soto 21 | Cintia Rodrigues de Souza 24 | Juliana Stropp 89 | Martin J. P. Sullivan 35 | Ben Swanepoel 34 | Hans ter Steege 25,90 | John Terborgh 91,92 | Nicolas Texier 93 | Takeshi Toma 94 | Renato Valencia 95 | Luis Valenzuela 75 | Leandro Valle Ferreira 96 | Fernando Cornejo Valverde 97 | Tinde R. Van Andel 25 | Rodolfo Vasque 77 | Hans Verbeeck 61 | Pandi Vivek 22 | Abstract Aim:Large tropical trees form the interface between ground and airborne observations, offering a unique opportunity to capture forest properties remotely and to investigate their variations on broad scales. However, despite rapid development of metrics to characterize the forest canopy from remotely sensed data, a gap remains between aerial and field inventories. To close this gap, we propose a new pan-tropical model to predict plot-level forest structure properties and biomass from only the largest trees.Location: Pan-tropical.Time period: Early 21st century. Major taxa studied: Woody plants.Methods: Using a dataset of 867 plots distributed among 118 sites across the tropics, we tested the prediction of the quadratic mean diameter, basal area, Lorey's height, community wood density and aboveground biomass (AGB) from the ith largest trees. Results:Measuring the largest trees in tropical forests enables unbiased predictions of plot-and site-level forest structure. The 20 largest trees per hectare predicted quadratic mean diameter, basal area, Lorey's height, community wood density and AGB with 12, 16, 4, 4 and 17.7% of relative error, respectively. Most of the remaining error in biomass prediction is driven by differences in the proportion of total biomass held in medium-sized trees (50-70 cm diameter at breast height), which shows some continental dependency, with American tropical forests presenting the highest proportion of total biomass in these intermediate-diameter classes relative to other continents. Main conclusions:Our approach provides new information on tropical forest structure and can be used to generate accurate field estimates of tropical forest carbon stocks to support the calibration and validation of current and forthcoming space missions. It will reduce the cost of field inventories and contribute to scientific understanding of tropical forest ecosystems and response to climate change. K E Y W O R D Scarbon, climate change, forest structure, large trees, pan-tropical, REDD+, tropical forest ecology

Growing threats to primates in tropical forests make robust and long-term population abundance assessments increasingly important for conservation. Concomitantly, monitoring becomes particularly relevant in countries with primate habitat. Yet monitoring schemes in these countries often suffer from logistic constraints and/or poor rigor in data collection, and a lack of consideration of sources of bias in analysis. To address the need for feasible monitoring schemes and flexible analytical tools for robust trend estimates, we analyzed data collected by local technicians on abundance of three species of arboreal monkey in the Udzungwa Mountains of Tanzania (two Colobus species and one Cercopithecus), an area of international importance for primate endemism and conservation. We counted primate social groups along eight line transects in two forest blocks in the area, one protected and one unprotected, over a span of 11 years. We applied a recently proposed open metapopulation model to estimate abundance trends while controlling for confounding effects of observer, site, and season. Primate populations were stable in the protected forest, while the colobines, including the endemic Udzungwa red colobus, declined severely in the unprotected forest. Targeted hunting pressure at this second site is the most plausible explanation for the trend observed. The unexplained variability in detection probability among transects was greater than the variability due to observers, indicating consistency in data collection among observers. There were no significant differences in both primate abundance and detectability between wet and dry seasons, supporting the choice of sampling during the dry season only based on minimizing practical constraints. Results show that simple monitoring routines implemented by trained local technicians can effectively detect changes in primate populations in tropical countries. The hierarchical Bayesian model formulation adopted provides a flexible tool to determine temporal trends with full account for any imbalance in the data set and for imperfect detection.

Many individual researchers have used line transect counts to estimate forest primate abundance. They have devoted less attention to the interpretation of line transect data obtained by several observers, as is often the case in long-term monitoring programs. We present primate relative abundance data that 5 observers collected over 6 yr (not continuous) along 4 different transects each 4 km long in the Mwanihana Forest, Udzungwa Mountains National Park, Tanzania. Total distance walked during transect repetitions is ca. 700 km. The species we saw most frequently was the endemic Udzungwa red colobus Procolobus gordonorum (mean 0.59 groups/km walked), followed by the Angolan black-and-white colobus Colobus angolensis (0.43 groups/km) and Sykes's monkey Cercopithecus mitis (0.35 groups/km). We sighted the endemic Sanje mangabey Cercocebus galeritus sanjei and the yellow baboon Papio cynocephalus infrequently, the latter being confined to the deciduous forest parts of the transects.We analyzed sighting frequency by gross habitat type, transect, season, and observer. Interobserver differences in the relative abundance of each species were moderate and the few cases of significant variations were due to discordance of only 1 observer from the others. Estimated distances of primate group sightings differ significantly among observers, thus preventing us from deriving estimates of absolute density. Frequency distributions of distance-class intervals are not significantly different among observers, which may indicate gross interobserver consistency in the width of the area sampled. We conclude that unless consistency in data collection is checked, as we did for 2 observers who collected data simultaneously, potential interobserver differences remain an underlying source of variance in the results that cannot be separated from other sources of variance.