Analysis of multispecies point patterns by usingmultivariate log-Gaussian Cox processes

Multivariate log-Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far been applied in bivariate cases only. We move beyond the bivariate case to model multispecies point patterns of tree locations. In particular we address the problems of identifying parsimonious models and of extracting biologically relevant information from the models fitted. The latent multivariate Gaussian field is decomposed into components given in terms of random fields common to all species and components which are species specific. This allows a decomposition of variance that can be used to quantify to what extent the spatial variation of a species is governed by common or species-specific factors. Cross-validation is used to select the number of common latent fields to obtain a suitable trade-off between parsimony and fit of the data. The selected number of common latent fields provides an index of complexity of the multivariate covariance structure. Hierarchical clustering is used to identify groups of species with similar patterns of dependence on the common latent fields.We thank the Joint Editor, the Associate Editor and the two referees for constructive comments that helped to improve both content and exposition of this paper. Abdollah Jalilian and Rasmus Waagepetersen's research was supported by the Danish Natural Science Research Council, grant 09-072331 ‘Point process modelling and statistical inference’, Danish Council for Independent Research—Natural Sciences, grant 12-124675, ‘Mathematical and statistical analysis of spatial data’, and by Centre for Stochastic Geometry and Advanced Bioimaging, funded by a grant from the Villum Foundation. Yongtao Guan's research was supported by National Science Foundation grant DMS-0845368, by National Institutes of Health grant 1R01CA169043 and by the VELUX Visiting Professor programme. Jorge Mateu's research was supported by grants P1-1B2012-52 and MTM2013-43917-P. The BCI forest dynamics research project was made possible by National Science Foundation grants to Stephen P. Hubbell: DEB-0640386, DEB-0425651, DEB-0346488, DEB-0129874, DEB-00753102, DEB-9909347, DEB-9615226, DEB-9405933, DEB-9221033, DEB-9100058, DEB-8906869, DEB-8605042, DEB-8206992 and DEB-7922197, support from the Center for Tropical Forest Science, the Smithsonian Tropical Research Institute, the John D. and Catherine T. MacArthur Foundation, the Mellon Foundation, the Celera Foundation and numerous private individuals, and through the hard work of over 100 people from 10 countries over the past two decades. The plot project is part of the Center for Tropical Forest Science, a global network of large-scale demographic tree plots. The BCI soils data set was collected and analysed by J. Dalling, R. John, K. Harms, R. Stallard and J. Yavitt with support from National Science Foudation grants DEB021104, DEB021115, DEB0212284, DEB0212818 and Office of International Science and Engineering grant 0314581, Smithsonian Tropical Research Institute and Center for Tropical Forest Science. Paolo Segre and Juan Di Trani provided assistance in the field. The covariates dem, grad, mrvbf, solar and twi were computed in SAGA GIS by Tomislav Hengl (http://spatial-analyst.net/). We thank Dr Joseph Wright for sharing data on dispersal modes and life forms for the BCI tree specie

KORISNOT I OPASNOST OD TRANSGENIČNIH BILJAKA

Surveying endangered species is necessary to evaluate conservation effectiveness. Camera trapping and biometric computer vision are recent technological advances. They have impacted on the methods applicable to field surveys and these methods have gained significant momentum over the last decade. Yet, most researchers inspect footage manually and few studies have used automated semantic processing of video trap data from the field. The particular aim of this study is to evaluate methods that incorporate automated face detection technology as an aid to estimate site use of two chimpanzee communities based on camera trapping. As a comparative baseline we employ traditional manual inspection of footage. Our analysis focuses specifically on the basic parameter of occurrence where we assess the performance and practical value of chimpanzee face detection software. We found that the semi-automated data processing required only 2–4% of the time compared to the purely manual analysis. This is a non-negligible increase in efficiency that is critical when assessing the feasibility of camera trap occupancy surveys. Our evaluations suggest that our methodology estimates the proportion of sites used relatively reliably. Chimpanzees are mostly detected when they are present and when videos are filmed in high-resolution: the highest recall rate was 77%, for a false alarm rate of 2.8% for videos containing only chimpanzee frontal face views. Certainly, our study is only a first step for transferring face detection software from the lab into field application. Our results are promising and indicate that the current limitation of detecting chimpanzees in camera trap footage due to lack of suitable face views can be easily overcome on the level of field data collection, that is, by the combined placement of multiple high-resolution cameras facing reverse directions. This will enable to routinely conduct chimpanzee occupancy surveys based on camera trapping and semi-automated processing of footage. RESEARCH HIGHLIGHTS Using semi-automated ape face detection technology for processing camera trap footage requires only 2–4% of the time compared to manual analysis and allows to estimate site use by chimpanzees relatively reliably

Bayesian spatial NBDA for diffusion data with home-base coordinates

Network-based diffusion analysis (NBDA) is a statistical method that allows the researcher to identify and quantify a social influence on the spread of behaviour through a population. Hitherto, NBDA analyses have not directly modelled spatial population structure. Here we present a spatial extension of NBDA, applicable to diffusion data where the spatial locations of individuals in the population, or of their home bases or nest sites, are available. The method is based on the estimation of inter-individual associations (for association matrix construction) from the mean inter-point distances as represented on a spatial point pattern of individuals, nests or home bases. We illustrate the method using a simulated dataset, and show how environmental covariates (such as that obtained from a satellite image, or from direct observations in the study area) can also be included in the analysis. The analysis is conducted in a Bayesian framework, which has the advantage that prior knowledge of the rate at which the individuals acquire a given task can be incorporated into the analysis. This method is especially valuable for studies for which detailed spatially structured data, but no other association data, is available. Technological advances are making the collection of such data in the wild more feasible: for example, bio-logging facilitates the collection of a wide range of variables from animal populations in the wild. We provide an R package, spatialnbda, which is hosted on the Comprehensive R Archive Network (CRAN). This package facilitates the construction of association matrices with the spatial x and y coordinates as the input arguments, and spatial NBDA analyses

Sleep and nesting behavior in primates: A review

Sleep is a universal behavior in vertebrate and invertebrate animals, suggesting it originated in the very first life forms. Given the vital function of sleep, sleeping patterns and sleep architecture follow dynamic and adaptive processes reflecting trade‐offs to different selective pressures. Here, we review responses in sleep and sleep‐related behavior to environmental constraints across primate species, focusing on the role of great ape nest building in hominid evolution. We summarize and synthesize major hypotheses explaining the proximate and ultimate functions of great ape nest building across all species and subspecies; we draw on 46 original studies published between 2000 and 2017. In addition, we integrate the most recent data brought together by researchers from a complementary range of disciplines in the frame of the symposium “Burning the midnight oil” held at the 26th Congress of the International Primatological Society, Chicago, August 2016, as well as some additional contributors, each of which is included as a “stand‐alone” article in this “Primate Sleep” symposium set. In doing so, we present crucial factors to be considered in describing scenarios of human sleep evolution: (a) the implications of nest construction for sleep quality and cognition; (b) the tree‐to‐ground transition in early hominids; (c) the peculiarities of human sleep. We propose bridging disciplines such as neurobiology, endocrinology, medicine, and evolutionary ecology, so that future research may disentangle the major functions of sleep in human and nonhuman primates, namely its role in energy allocation, health, and cognition

Spatial point pattern analysis of gorilla nest sites in the Kagwene Sanctuary, Cameroon: Towards understanding the nesting behavior of a critically endangered subspecies

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Gorilla nest site data from the Kagwene sanctuary, Cameroon were analyzed to foster an understanding of the nesting behavior of Cross River Gorillas. The main objective of the study was to verify the pattern of nest site distribution in the sanctuary, the influence of environmental covariates and possible interaction between nest sites and between nest sites of two gorilla groups – the Major and Minor groups. Spatial point pattern analysis methods were implemented in the R software environment for this purpose. Overall, we sought to fit models that could best estimate an intensity function for nest site distribution in the sanctuary. Resulting models revealed that nest site distribution does not conform to a Poisson process, and that the data can be better described by a combination of environmental factors and interaction between nest sites. Univariate models fitted to different nest site categories proved to be more useful than bivariate models in defining intensity functions for nest site distribution. The final model category chosen for the data therefore constituted a combination of the effect of covariates and higher-order interaction between nest sites. This set of models, chosen through their AIC values, showed that nest site distribution in the sanctuary exhibits characteristics of attraction so that clustered patterns are observed. Gorillas tend to create hotspots for nest site location, with southern parts of the sanctuary proving to be very much avoided. Intensity tends to increase with increasing distance to the centre of the sanctuary. Coefficients obtained from the models also showed that gorillas prefer constructing nests close to transition vegetation, on steep slopes and generally on east-facing slopes. In the dry season however, colonizing forests exert a greater attraction on nest sites, which can be attributed to the fact that transition zones experience such edge effects as bushfires, and plants that provide food (such as Zingiberaceae) do not bear fruit in this season. These can be assumed to be specific habitat requirements of this subspecies of gorillas. Also, intensity drops with increasing elevation. Interaction parameters obtained from the bivariate models also suggested that there is attraction between nest sites of the Major (sites with more than 6 nests) and the Minor groups. This analysis is the first of its kind for this subspecies, and we recommend that further models can be fitted to include a wider range of covariates (both anthropogenic and natural) as they may be available to expand the scope of the models

Understanding the nesting spatial behaviour of gorillas in the Kagwene Sanctuary, Cameroon

We use spatial point pattern methods to analyse gorilla nest site data, and to enhance our understanding of the nesting behaviour of the Gorilla gorilla diehli in the Kagwene Sanctuary, Cameroon. Data were split into different seasons and different gorilla groups to better understand gorilla nesting behaviour at these different scales. Gorilla nest site distribution was found to be inhomogeneous and clustered, as a result of the inhomogeneity in the distribution of the environmental factors (such as elevation, slope, vegetation and aspect), and because of the interaction between nest sites. The proposed models reflected therefore a combination of the effect of environmental factors and interaction between nest sites. Predictions from these models showed that there is less space available for gorilla nest site location in the dry season than in the rainy season. It also showed that the Minor gorilla group has a bigger niche than the Major group, suggesting a nesting disadvantage in the larger size group. We also found that nest site locations of Major gorilla groups attract Minor groups, and vice versa

The status of apes across Africa and Asia

First paragraph: This chapter provides information on the conservation and welfare of great apes and gibbons. It focuses on the distribution and environmental conditions in which apes live in both Africa and Asia. The information presented is drawn from various sources, especially from the A.P.E.S. Portal (http:// apesportal.eva.mpg.de), and can be used by decision-makers and stakeholders to contribute to the development of informed policies and effective planning. Although reference is made to particular great ape and gibbon taxa in some parts of the report, discussions are tailored to address issues about apes in general (not necessarily species specific). Because data quality and availability are not uniform across all ape taxa, regions, or even countries, we refer to specific cases for which data are available and reliable. The current chapter has not yet been expanded to fully include the gibbons and, as such, data mining for this family is still limited; however, additional data collection will occur in between this and the subsequent edition of State of the Apes to ensure that gibbons are well represented in future