Vertebrate Zoology Section

Trento, Italy

Vertebrate Zoology Section

Trento, Italy

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Rovero F.,Tropical Biodiversity Section | Rovero F.,Udzungwa Ecological Monitoring Center | Mtui A.,Udzungwa Ecological Monitoring Center | Kitegile A.,Sokoine University of Agriculture | And 3 more authors.
PLoS ONE | Year: 2015

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. © 2015 Rovero et al.


PubMed | Tropical Biodiversity Section, Vertebrate Zoology Section, Sokoine University of Agriculture and Udzungwa Ecological Monitoring Center
Type: Journal Article | Journal: PloS one | Year: 2015

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.


PubMed | Provincia Autonoma di Trento (Italy), Vertebrate Zoology Section, French National Center for Scientific Research and European Commission - Joint Research Center Ispra
Type: Journal Article | Journal: Conservation biology : the journal of the Society for Conservation Biology | Year: 2016

The conservation of wildlife requires management based on quantitative evidence, and especially for large carnivores, unraveling cause-specific mortalities and understanding their impact on population dynamics is crucial. Acquiring this knowledge is challenging because it is difficult to obtain robust long-term data sets on endangered populations and, usually, data are collected through diverse sampling strategies. Integrated population models (IPMs) offer a way to integrate data generated through different processes. However, IPMs are female-based models that cannot account for mate availability, and this feature limits their applicability to monogamous species only. We extended classical IPMs to a two-sex framework that allows investigation of population dynamics and quantification of cause-specific mortality rates in nonmonogamous species. We illustrated our approach by simultaneously modeling different types of data from a reintroduced, unhunted brown bear (Ursus arctos) population living in an area with a dense human population. In a population mainly driven by adult survival, we estimated that on average 11% of cubs and 61% of adults died from human-related causes. Although the population is currently not at risk, adult survival and thus population dynamics are driven by anthropogenic mortality. Given the recent increase of human-bear conflicts in the area, removal of individuals for management purposes and through poaching may increase, reversing the positive population growth rate. Our approach can be generalized to other species affected by cause-specific mortality and will be useful to inform conservation decisions for other nonmonogamous species, such as most large carnivores, for which data are scarce and diverse and thus data integration is highly desirable.


Chambert T.,Pennsylvania State University | Chambert T.,U.S. Geological Survey | Kendall W.L.,U.S. Geological Survey | Hines J.E.,U.S. Geological Survey | And 6 more authors.
Methods in Ecology and Evolution | Year: 2015

With ongoing climate change, many species are expected to shift their spatial and temporal distributions. To document changes in species distribution and phenology, detection/non-detection data have proven very useful. Occupancy models provide a robust way to analyse such data, but inference is usually focused on species spatial distribution, not phenology. We present a multi-season extension of the staggered-entry occupancy model of Kendall et al. (2013, Ecology, 94, 610), which permits inference about the within-season patterns of species arrival and departure at sampling sites. The new model presented here allows investigation of species phenology and spatial distribution across years, as well as site extinction/colonization dynamics. We illustrate the model with two data sets on European migratory passerines and one data set on North American treefrogs. We show how to derive several additional phenological parameters, such as annual mean arrival and departure dates, from estimated arrival and departure probabilities. Given the extent of detection/non-detection data that are available, we believe that this modelling approach will prove very useful to further understand and predict species responses to climate change. © 2015 British Ecological Society.


Tattoni C.,Vertebrate Zoology Section | Rizzolli F.,Vertebrate Zoology Section | Pedrini P.,Vertebrate Zoology Section
Ecological Modelling | Year: 2012

Habitat suitability models are based on digital maps that very often describe the environment at a human scale and, hence miss ecological structures and features that are important for wildlife. LiDAR (Light Detection And Ranging) data, laser scanning acquired by remote sensing, can fill this gap by providing useful information not only on the spatial extent of habitat types but also information on the vertical height. The advantage of LiDAR derived variables lays also in the availability at a large scale, instead of just in the survey sites. In this work we evaluated the effect of three LiDAR derived variables (tree height, percentage of trees in open areas and length of ecotone) on the performance of habitat models, developed for four farmland bird species. For each species multiple runs of stepwise Logistic Regression (LR) and Maximum Entropy Models (Maxent) were performed. For each run we included and excluded the LiDAR variables and recorded the improvement in model performance using the AUC, AIC, Sensitivity, Specificity. Model results were applied in a GIS in order to create habitat suitability maps. Results for the RL models showed that for most of the species at least one LiDAR variable was selected and significant (p< 0.05). Additionally the inclusion LIDAR data gave a positive percentage of contribution to the AUC of the Maxent models. The models calculated using LiDAR derived variables identified a smaller area on the map, with a better overlap with open areas, thus showing a more realistic spatial pattern. The interpretation of these variable is also more straightforward, both from the ecological point of view and when defining management guidelines. © 2012 Elsevier B.V.

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