Browsing by Author "Rovero, F."

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Hunting or habitat degradation? Decline of primate populations in Udzungwa Mountains, Tanzania: An analysis of threats
(Biological Conservation, 2012) Rovero, F.; Mtui, A. S.; Kitegile, A. S.; Nielsen, M. R.
Hunting 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.
Primates decline rapidly in unprotected forests: Evidence from a monitoring program with data constraints
(PLOS ONE, 2015-02) Rovero, F.; Mtui, A.; Kitegile, A.; Jacob, P.; Araldi, A.; Tenan, S.
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.