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Utilizing heterogeneous mobile sensors to actively gather information improves adaptability and reliability in extended environments. This article presents a cooperative multirobot multitarget search and tracking framework aimed at enhancing the efficiency of the heterogeneous sensor network, and consequently, improving the overall target tracking accuracy. The concept of normalized unused sensing capacity is introduced to quantify the information a sensor is currently gathering relative to its theoretical maximum. This measurement can be computed using entirely local information and is applicable to various sensor models, distinguishing it from previous literature on the subject. It is then utilized to develop a heuristics distributed coverage control strategy for a heterogeneous sensor network, adaptively balancing the workload based on each sensor's current unused capacity. The algorithm is validated through a series of robot operating system (ROS) and MATLAB simulations, demonstrating superior results compared to standard approaches that do not account for heterogeneity or current usage rates. 

Distributed multi-target tracking is a canonical task for multi-robot systems, encompassing applications from environmental monitoring to disaster response to surveillance. In many situations, the distribution of unknown objects in a search area is irregular, with objects are likely to distribute in clusters instead of evenly distributed. In this paper, we develop a novel distributed multi-robot multi-target tracking algorithm for effectively tracking clustered targets from noisy measurements. Our algorithm contains two major components. Firstly, both the instantaneous and cumulative target density are estimated, providing the best guess of current target states and long-term coarse distribution of clusters, respectively. Secondly, the power diagram is implemented in Lloyd’s algorithm to optimize task space assignment for each robot to trade-off between tracking detected targets in clusters and searching for potential targets outside clusters. We demonstrate the efficacy of our proposed method and show that our method outperforms of other candidates in tracking accuracy through a set of simulations. 

The metagenome embedded in urban sewage is an attractive new data source to understand urban ecology and assess human health status at scales beyond a single host. Analyzing the viral fraction of wastewater in the ongoing COVID-19 pandemic has shown the potential of wastewater as aggregated samples for early detection, prevalence monitoring, and variant identification of human diseases in large populations. However, using census-based population size instead of real-time population estimates can mislead the interpretation of data acquired from sewage, hindering assessment of representativeness, inference of prevalence, or comparisons of taxa across sites. Here, we show that taxon abundance and sub-species diversisty in gut-associated microbiomes are new feature space to utilize for human population estimation. Using a population-scale human gut microbiome sample of over 1,100 people, we found that taxon-abundance distributions of gut-associated multi-person microbiomes exhibited generalizable relationships with respect to human population size. Here and throughout this paper, the human population size is essentially the sample size from the wastewater sample. We present a new algorithm, MicrobiomeCensus, for estimating human population size from sewage samples. MicrobiomeCensus harnesses the inter-individual variability in human gut microbiomes and performs maximum likelihood estimation based on simultaneous deviation of multiple taxa’s relative abundances from their population means. MicrobiomeCensus outperformed generic algorithms in data-driven simulation benchmarks and detected population size differences in field data. New theorems are provided to justify our approach. This research provides a mathematical framework for inferring population sizes in real time from sewage samples, paving the way for more accurate ecological and public health studies utilizing the sewage metagenome. 

Abstract Major disturbances can temporarily remove factors that otherwise constrain population abundance and distribution. During such windows of relaxed top‐down and/or bottom‐up control, ungulate populations can grow rapidly, eventually leading to resource depletion and density‐dependent expansion into less‐preferred habitats. Although many studies have explored the demographic outcomes and ecological impacts of these processes, fewer have examined the individual‐level mechanisms by which they occur. We investigated these mechanisms in Gorongosa National Park, where the Mozambican Civil War devastated large‐mammal populations between 1977 and 1992. Gorongosa’s recovery has been marked by proliferation of waterbuck (Kobus ellipsiprymnus), an historically marginal 200‐kg antelope species, which is now roughly 20‐fold more abundant than before the war. We show that after years of unrestricted population growth, waterbuck have depleted food availability in their historically preferred floodplain habitat and have increasingly expanded into historically avoided savanna habitat. This expansion was demographically skewed: mixed‐sex groups of prime‐age individuals remained more common in the floodplain, while bachelors, loners, and subadults populated the savanna. By coupling DNA metabarcoding and forage analysis, we show that waterbuck in these two habitats ate radically different diets, which were more digestible and protein‐rich in the floodplain than in savanna; thus, although individuals in both habitats achieved positive net energy balance, energetic performance was higher in the floodplain. Analysis of daily activity patterns from high‐resolution GPS‐telemetry, accelerometry, and animal‐borne video revealed that savanna waterbuck spent less time eating, perhaps to accommodate their tougher, lower‐quality diets. Waterbuck in savanna also had more ectoparasites than those in the floodplain. Thus, plasticity in foraging behavior and diet selection enabled savanna waterbuck to tolerate the costs of density‐dependent spillover, at least in the short term; however, the already poorer energetic performance of these individuals implies that savanna occupancy may become prohibitively costly as heterospecific competitors and predators continue to recover in Gorongosa. Our results suggest that behavior can provide a leading indicator of the onset of density‐dependent limitation and the likelihood of subsequent population decline, but that reliable inference hinges on understanding the mechanistic basis of observed behavioral shifts.