The role of competition in structuring primate communities under different productivity regimes in the Amazon

The factors responsible for the formation of Amazonian primate communities are not well understood. Here we investigated the influence of interspecific competition in the assembly of these communities, specifically whether they follow an assembly rule known as "favored states". According to this rule, interspecific competition influences final species composition, resulting in functional groups that are equally represented in the community.We compiled presence-absence data for primate species at 39 Amazonian sites in Brazil, contrasting two regions with distinct productivity regimes: the eutrophic Juruá River basin and the oligotrophic Negro River basin. We tested two hypotheses: that interspecific competition is a mechanism that influences the structure of Amazonian primate communities, and that competition has had a greater influence on the structure of primate communities in regions with low productivity, where resources are more limited. We used null models to test the statistical significance of the results, and found a non-random pattern compatible with the favored states rule in the two regions. Our findings suggest that interspecific competition is an important force driving primate community assembly regardless of productivity regimes

ATLANTIC-PRIMATES: a dataset of communities and occurrences of primates in the Atlantic Forests of South America

Primates play an important role in ecosystem functioning and offer critical insights into human evolution, biology, behavior, and emerging infectious diseases. There are 26 primate species in the Atlantic Forests of South America, 19 of them endemic. We compiled a dataset of 5,472 georeferenced locations of 26 native and 1 introduced primate species, as hybrids in the genera Callithrix and Alouatta. The dataset includes 700 primate communities, 8,121 single species occurrences and 714 estimates of primate population sizes, covering most natural forest types of the tropical and subtropical Atlantic Forest of Brazil, Paraguay and Argentina and some other biomes. On average, primate communities of the Atlantic Forest harbor 2 ± 1 species (range = 1–6). However, about 40% of primate communities contain only one species. Alouatta guariba (N = 2,188 records) and Sapajus nigritus (N = 1,127) were the species with the most records. Callicebus barbarabrownae (N = 35), Leontopithecus caissara (N = 38), and Sapajus libidinosus (N = 41) were the species with the least records. Recorded primate densities varied from 0.004 individuals/km 2 (Alouatta guariba at Fragmento do Bugre, Paraná, Brazil) to 400 individuals/km 2 (Alouatta caraya in Santiago, Rio Grande do Sul, Brazil). Our dataset reflects disparity between the numerous primate census conducted in the Atlantic Forest, in contrast to the scarcity of estimates of population sizes and densities. With these data, researchers can develop different macroecological and regional level studies, focusing on communities, populations, species co-occurrence and distribution patterns. Moreover, the data can also be used to assess the consequences of fragmentation, defaunation, and disease outbreaks on different ecological processes, such as trophic cascades, species invasion or extinction, and community dynamics. There are no copyright restrictions. Please cite this Data Paper when the data are used in publications. We also request that researchers and teachers inform us of how they are using the data. © 2018 by the The Authors. Ecology © 2018 The Ecological Society of Americ

Primates facing climate crisis in a tropical forest hotspot will lose climatic suitable geographical range

Abstract Global climate changes affect biodiversity and cause species distribution shifts, contractions, and expansions. Climate change and disease are emerging threats to primates, and approximately one-quarter of primates’ ranges have temperatures over historical ones. How will climate changes influence Atlantic Forest primate ranges? We used habitat suitability models and measured potential changes in area and distributions shifts. Climate change expected in 2100 may change the distribution area of Atlantic Forest primates. Fourteen species (74%) are predicted to lose more than 50% of their distribution, and nine species (47%) are predicted to lose more than 75% of their distribution. The balance was negative, indicating a potential future loss, and the strength of the reduction in the distribution is related to the severity of climate change (SSP scenarios). Directional shifts were detected to the south. The projected mean centroid latitudinal shift is ~ 51 km to the south for 2100 SSP5-8.5 scenario. The possibility of dispersal will depend on suitable routes and landscape configuration. Greenhouse gas emissions should be urgently reduced. Our results also emphasize that no more forest loss is acceptable in Atlantic Forest, and restoration, canopy bridges, friendly agroecosystems, and monitoring of infrastructure projects are urgent to enable dealing with climate change

Downstream densities of <i>Hypostomus punctatus</i>.

<p>Fig 3. Temporal variation in the density of <i>Hypostomus punctatus</i> in the sites of Rio Ubatiba sites (A) 6, (B) 7, and (C) 8.</p

Pairwise Spearman correlations between the densities of <i>H</i>. <i>punctatus</i> and <i>P</i>. <i>maculicauda</i>, and the first and second PCA axes.

<p>The correlation coefficient, <i>r</i>, is shown above the diagonal, and tCe <i>P</i> values are shown below the diagonal (italics).</p

Frequency of electrofishing samples collection at the Rio Ubatiba between 1994 and 2009.

<p>Frequency of electrofishing samples collection at the Rio Ubatiba between 1994 and 2009.</p

The eight different models based on the density of <i>Hypostomus punctatus</i> at the five sampling sites as the response variable, with all the possible combinations of independent variables (density of <i>Parotocinclus maculicauda</i>, and first and second PCA axes) were fitted and compared using Akaike’s Information Criterion and the relative weight (<i>w</i>_i) for each model.

<p>The best model, with the highest <i>w</i>_<i>i</i> value, includes the predictors <i>P</i>. <i>maculicauda</i> density and the second PCA axis.</p