Bird observation and sampling in tropical agroforestry landscapes of the Napu Valley in Central Sulawesi, Indonesia

We investigated the local bird community in Central Sulawesi (Indonesia), with focus on insectivorous species in the agroforestry landscapes adjacent to the Lore Lindu National Park. All study sites were situated at the northern tip of Napu Valley in Central Sulawesi, Indonesia. After an initial mapping of the study area, we selected 15 smallholder cacao plantations as sites for our study in March 2010. These sides were mainly used for bird and bat exclosure experiments. All sited were situated along a local gradient (shade availability on each plantation) and a landscape gradient (distance to primary forest), which were independent from each other. In September 2010 and from February until June 2011, we assessed the bird community on our 15 study sites using monthly point count and mist netting sampling. Point count (20 minutes between 07 am and 10 am and in between the net checking hours) and mist netting surveys (12 hours, between 05:30 am and 17:30 pm) were conducted simultaneously but only once per month on each study site, to avoid habituation of the local bird community to our surveys. Further, point counts were conducted at least 100 m apart from the mist netting sites, to avoid potential disturbance between the two methods. We discarded all observations beyond 50 m (including those individuals that flew over the canopy) from the statistical analysis, as well as recaptures of individuals within identical mist netting rounds.

Brief Announcement: Node Sampling Using Centrifugal Random Walks.

We propose distributed algorithms for sampling networks based on a new class of random walks that we call Centrifugal Random Walks (CRW). A CRW is a random walk that starts at a source and always moves away from it. We propose CRW algorithms for connected networks with arbitrary probability distributions, and for grids and networks with regular concentric connectivity with distance based distributions. All CRW sampling algorithms select a node with the exact probability distribution, do not need warm-up, and end in a number of hops bounded by the network diameter.

Node sampling using random centrifugal walks

Sampling a network with a given probability distribution has been identified as a useful operation. In this paper we propose distributed algorithms for sampling networks, so that nodes are selected by a special node, called the source, with a given probability distribution. All these algorithms are based on a new class of random walks, that we call Random Centrifugal Walks (RCW). A RCW is a random walk that starts at the source and always moves away from it. Firstly, an algorithm to sample any connected network using RCW is proposed. The algorithm assumes that each node has a weight, so that the sampling process must select a node with a probability proportional to its weight. This algorithm requires a preprocessing phase before the sampling of nodes. In particular, a minimum diameter spanning tree (MDST) is created in the network, and then nodes weights are efficiently aggregated using the tree. The good news are that the preprocessing is done only once, regardless of the number of sources and the number of samples taken from the network. After that, every sample is done with a RCW whose length is bounded by the network diameter. Secondly, RCW algorithms that do not require preprocessing are proposed for grids and networks with regular concentric connectivity, for the case when the probability of selecting a node is a function of its distance to the source. The key features of the RCW algorithms (unlike previous Markovian approaches) are that (1) they do not need to warm-up (stabilize), (2) the sampling always finishes in a number of hops bounded by the network diameter, and (3) it selects a node with the exact probability distribution.