Calibrating occupancy to density estimations to assess abundance and vulnerability of a threatened primate in Tanzania

The current decline of mammals worldwide makes quantitative population assessments crucial, especially for range‐restricted and threatened species. However, robust abundance estimations are challenging for elusive or otherwise difficult to detect species. Alternative metrics requiring only presence/absence data, that is, occupancy, are possible but calibration with independent density estimates should be foreseen, although rarely performed. Here, we calibrated density estimates from acoustic surveys to occupancy estimates from camera‐trapping detections to derive the abundance of the endangered Sanje mangabey (Cercocebus sanjei) across its entire range in the Udzungwa Mountains of Tanzania. We found marked occupancy–density relationships for the two forest blocks where this primate occurs and used them to derive spatially explicit density estimates. Occupancy increased in montane forest zones at mid‐elevation but decreased slightly with proximity to forest borders. We predicted an average density (±SE) of 0.26 ± 0.05 groups/km2 in the national park and 0.24 ± 0.06 in the nature reserve. Accordingly, and given the much larger area of the reserve, the average predicted individual abundance was 1555 ± 325 and 2471 ± 571 in the national park and nature reserve, respectively. We found higher density and abundance in the nature reserve compared with previous studies. Given the past disturbance and poorer protection in the nature reserve relative to the national park, our results instill optimism for the status of the species, although occupancy analysis highlighted the potential vulnerability of this primate to human disturbance. Our approach appears valuable for spatially explicit density estimations of elusive species, and provides robust assessments of vulnerability and identification of priority areas for conservation of threatened populations

Hunting or habitat degradation? Decline of primate populations in Udzungwa Mountains, Tanzania:an analysis of threats

Biological ConservationHunting and habitat degradation are universal threats to primates across the tropics, thus deciphering the relative impact of threats on population relative abundance is critical to predicting extinction risk and providing conservation recommendations. We studied diurnal primates over a period of nearly 6 years in the Udzungwa Mountains of Tanzania, a site of global importance for primate conservation. We assessed how population relative abundance of five species (of which two are endemic and IUCNEndangered) differed between two forest blocks that are similar in size and habitat types but contrast strongly in protection level, and how abundance changed during 2004–2009. We also measured habitat and disturbance parameters and, in the unprotected forest, evaluated hunting practices. We found significant differences in primates’ abundance between protected and unprotected forests, with the greater contrast being the lower abundance of colobine monkeys (Udzungwa red colobus and Angolan colobus) in the unprotected forest. At this site moreover, colobines declined to near-extinction over the study period. In contrast, two cercopithecines (Sanje mangabey and Sykes’ monkey) showed slightly higher abundance in the unprotected forest and did not decline significantly. We argue that escalating hunting in the unprotected forest has specifically impacted the canopy-dwelling colobus monkeys, although habitat degradation may also have reduced their abundance. In contrast, cercopithecines did not seem affected by the current hunting, and their greater ecological adaptability may explain the relatively higher abundance in the unprotected forest. We provide recommendations towards the long-term protection of the area

Pan-tropical prediction of forest structure from the largest trees

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

Pan-tropical prediction of forest structure from the largest trees

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 above ground 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