| dc.description |
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. |
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