Completeness of rapid assessments of medium and large mammal diversity in the northwestern Amazon in Colombia

Abstract

Rapid assessments are the most common information source on biodiversity in the northwestern Amazon in Colombia due to limited resources and logistic constraints. These assessments are essential for decision-making on environmental policies in this region, that has been strongly impacted by the transformation of its natural ecosystems. Several local camera-trapping rapid assessments of medium and large-sized mammals (MLM) have been conducted in the Colombian Amazon, but they are difficult to compare. We analyzed information of 16 of these rapid assessments of MLM to provide the first list of MLM in the northwestern Amazon in Colombia. We also evaluated the accuracy of four estimators (ICE, Chao-2, Jackknife-1, and Jackknife-2), and the minimum sampling effort for the estimation of MLM richness in local surveys in the region. We report 26 species of MLM for the Colombian Amazon (between five and 13 species per locality), which is an underestimation of MLM richness in the region. The best estimator of MLM richness was the Jackknife-1, due to its precision and the lower influence of singletons. We recommend a minimum sampling effort of 350 camera trap-days. Although rapid assessments do not allow a robust estimation of MLM richness, they record the most common species (or core species) per locality and their abundance variation. The evaluation of the effect of habitat transformation on MLM and the estimation of population parameters of rare species require more intensive studies.

Massive Bird Nest Losses: A Neglected Threat for Passerine Birds in Atlantic Forest Fragments from the Pernambuco Endemism Center

“…Because species richness (alpha diversity) is a proxy of sampling effort, uncovering all of the species in a target community can be a challenging task, especially in megadiverse habitats such as the Atlantic Forest [41]. Among the innumerous methods developed to estimate expected species richness based on collected data, the non-parametric estimators, e.g., Chao2, ICE, Jackknife1, and Jackknife2, have been widely used in studies involving camera trap data [42][43][44]. Here, we chose a Jackknife estimator because this class of estimator performed better than others in two comparative studies that used camera traps for faunal surveys in tropical forests [42,44].…”

“…Among the innumerous methods developed to estimate expected species richness based on collected data, the non-parametric estimators, e.g., Chao2, ICE, Jackknife1, and Jackknife2, have been widely used in studies involving camera trap data [42][43][44]. Here, we chose a Jackknife estimator because this class of estimator performed better than others in two comparative studies that used camera traps for faunal surveys in tropical forests [42,44]. Specifically, we used Jackknife 2, which is a model that takes into account the numbers of singletons (species represented by only one individual) and doubletons (species represented by only two individuals) to generate expected species richness [45,46], as both were present in our dataset.…”

“…Although in some regular camera trap surveys the cameras can be arranged in the field across pre-defined grids or transects [42,48,49], in many other works, the cameras are distributed randomly across sites with animal traces, respecting only a minimum distance interval [43,44,50], and our study involving widespread nests may not differ from the latter. Although each camera-day can be treated as a sampling unit for species richness estimations, here, for graphical purposes, we partitioned our dataset into 50 camera-days subsets that were used as sampling unities for Jackknife2 calculations (see also [49]).…”