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Quantifying animals' home ranges is a key problem in ecology and has important conservation and wildlife management applications. Kernel density estimation (KDE) is a workhorse technique for range delineation problems that is both statistically efficient and nonparametric. KDE assumes that the data are independent and identically distributed (IID). However, animal tracking data, which are routinely used as inputs to KDEs, are inherently autocorrelated and violate this key assumption. As we demonstrate, using realistically autocorrelated data in conventional KDEs results in grossly underestimated home ranges. We further show that the performance of conventional KDEs actually degrades as data quality improves, because autocorrelation strength increases as movement paths become more finely resolved. To remedy these flaws with the traditional KDE method, we derive an autocorrelated KDE (AKDE) from first principles to use autocorrelated data, making it perfectly suited for movement data sets. We illustrate the vastly improved performance of AKDE using analytical arguments, relocation data from Mongolian gazelles, and simulations based upon the gazelle's observed movement process. By yielding better minimum area estimates for threatened wildlife populations, we believe that future widespread use of AKDE will have significant impact on ecology and conservation biology.

Biological stoichiometry theory considers the balance of multiple chemical elements in living systems, whereas metabolic scaling theory considers how size affects metabolic properties from cells to ecosystems. We review recent developments integrating biological stoichiometry and metabolic scaling theories in the context of plant ecology and global change. Although vascular plants exhibit wide variation in foliar carbon : nitrogen : phosphorus ratios, they exhibit a higher degree of 'stoichiometric homeostasis' than previously appreciated. Thus, terrestrial carbon : nitrogen : phosphorus stoichiometry will reflect the effects of adjustment to local growth conditions as well as species' replacements. Plant stoichiometry exhibits size scaling, as foliar nutrient concentration decreases with increasing plant size, especially for phosphorus. Thus, small plants have lower nitrogen : phosphorus ratios. Furthermore, foliar nutrient concentration is reflected in other tissues (root, reproductive, support), permitting the development of empirical models of production that scale from tissue to whole-plant levels. Plant stoichiometry exhibits large-scale macroecological patterns, including stronger latitudinal trends and environmental correlations for phosphorus concentration (relative to nitrogen) and a positive correlation between nutrient concentrations and geographic range size. Given this emerging knowledge of how plant nutrients respond to environmental variables and are connected to size, the effects of global change factors (such as carbon dioxide, temperature, nitrogen deposition) can be better understood.

An animal's trajectory is a fundamental object of interest in movement ecology, as it directly informs a range of topics from resource selection to energy expenditure and behavioral states. Optimally inferring the mostly unobserved movement path and its dynamics from a limited sample of telemetry observations is a key unsolved problem, however. The field of geostatistics has focused significant attention on a mathematically analogous problem that has a statistically optimal solution coined after its inventor, Krige. Kriging revolutionized geostatistics and is now the gold standard for interpolating between a limited number of autocorrelated spatial point observations. Here we translate Kriging for use with animal movement data. Our Kriging formalism encompasses previous methods to estimate animal's trajectories—the Brownian bridge and continuous‐time correlated random walk library—as special cases, informs users as to when these previous methods are appropriate, and provides a more general method when they are not. We demonstrate the capabilities of Kriging on a case study with Mongolian gazelles where, compared to the Brownian bridge, Kriging with a more optimal model was 10% more precise in interpolating locations and 500% more precise in estimating occurrence areas.

King Penguins (Aptenodytes patagonicus) live in remote locations, in large colonies with asynchronous breeding. These three factors hinder the design and conduct of King Penguin censuses, and assessments of trend often require piecing together mismatched surveys of different demographic components. This study introduces a new method to remotely census these populations year-round and correct population estimates for the King Penguin’s unique breeding phenology. We combined in situ ground counts with estimates based on high-resolution satellite imagery to catalog the distribution of breeding colonies and estimate population abundance across the island of South Georgia, in the south Atlantic. While most King Penguin populations are forecast to decline significantly over the next century, South Georgia is expected to experience more favorable conditions and represents an important refugium for the species, though the challenges of surveying King Penguins have precluded a comprehensive census. Due to the variable timing of both in situ and remote counts, we developed a discrete time, age- and stage-structured population model that provides stage- and day-specific correction factors for standardization of census counts. We estimate the current population of King Penguins on South Georgia as 405,425 (95% CI 102,624–2,375,061) breeding pairs and find that population trends that do not account for phenological biases persistently underestimate the population growth rate. Correction factors are highly sensitive to annual egg mortality and the total breeding population is best estimated using nest counts of early-breeding pairs. Future efforts to census King Penguin populations may minimize uncertainty by capturing more precise estimates of egg survival and optimizing the timing of ground and satellite censuses to occur during the settlement of early breeders. Accounting for the error associated with current uncertainty in model parameters, 18–32 years of census data would be required to accurately detect trends of a population with a 10% growth rate. While asynchronously breeding species present a unique challenge to population monitoring, careful accounting of within-season dynamics can be used to assemble a self-consistent time series from heterogeneous survey data.

Commercial, shipborne tourism along the Antarctic Peninsula grew exponentially between 1989–90 and 2007–08, raising concern about the impact such activity may have on the environment of the region. Previous analyses of Antarctic tourism have focused narrowly on patterns of visitation and potential impacts at terrestrial landing sites. Here, using 19 years of passenger landing statistics and five years of reconstructed ship itineraries, we explore patterns of tourism activities in the Antarctic Peninsula region using a spatially explicit network theory analysis of ship itineraries. We find that passenger landings and marine traffic are highly concentrated at a few specific locations and that growth in tourism activity occurred disproportionally rapidly at these sites relative to growth in visitation of the Peninsula as a whole. We conclude by discussing the pros and cons of spatially concentrated tourism activity and the associated implications for ecosystem management.

Home-range estimation is an important application of animal tracking data that is frequently complicated by autocorrelation, sampling irregularity, and small effective sample sizes. We introduce a novel, optimal weighting method that accounts for temporal sampling bias in autocorrelated tracking data. This method corrects for irregular and missing data, such that oversampled times are downweighted and undersampled times are upweighted to minimize error in the home-range estimate. We also introduce computationally efficient algorithms that make this method feasible with large data sets. Generally speaking, there are three situations where weight optimization improves the accuracy of home-range estimates: with marine data, where the sampling schedule is highly irregular, with duty cycled data, where the sampling schedule changes during the observation period, and when a small number of home-range crossings are observed, making the beginning and end times more independent and informative than the intermediate times. Using both simulated data and empirical examples including reef manta ray, Mongolian gazelle, and African buffalo, optimal weighting is shown to reduce the error and increase the spatial resolution of home-range estimates. With a conveniently packaged and computationally efficient software implementation, this method broadens the array of data sets with which accurate space-use assessments can be made.

Basic knowledge of species biology and ecology is essential for the assessment of species conservation status and planning for efficient conservation strategies; however, this information is not always readily available. Here we use movement behavior to understand the ecology and social biology of the poorly known southern three-banded armadillo (Tolypeutes matacus). We used VHF and GPS telemetry to monitor 26 individuals from two sites in the Pantanal wetlands of Brazil. We characterized armadillo activity patterns, evaluated the relationship between sex and body mass with home range size and mean daily distance traveled, and examined home and core range overlap. Three-banded armadillos were active on average for 5.5 ± 2.8 h/day, with most of their activity concentrated in the first half of the night. Adult males were heavier and had larger home ranges than adult females. Home range size scaled positively with body mass for males, but not for females. Core ranges for females overlapped little (< 1%) regardless of age, but home ranges for males overlapped both with other males (12%) and females (18%). Our data suggest that three-banded armadillos are mainly a nocturnal species. Home range and spacing patterns point to a generally asocial behavior and a polygynous or promiscuous mating system. We hope that the data generated as a result of this project will contribute to this species' conservation in Brazil and elsewhere by guiding future management and research efforts.

1. Phosphorus content was measured in adult insects and arachnids from 170 species collected in the Sonoran Desert. 2. Across insect body sizes spanning four orders of magnitude, phosphorus content was inversely related to body mass. The largest species (∼1 g dry) had phosphorus contents that were only about 60% (0.62% P absolute) as high as phosphorus contents of the smallest species (∼0.0001 g dry; 0.97%P). Negative phosphorus allometry was observed within each of seven insect orders and within arachnids. 3. Phosphorus contents of insect predators and herbivores were statistically indistinguishable. 4. More recently derived orders tended to have lower phosphorus contents -- with the exception of the most recently derived group (Panorpida = Diptera + Lepidoptera), which had high phosphorus contents.

Neither linear nor two-dimensional frameworks may be the most appropriate for fish and other species constrained to disperse within river-creek systems. In particular, the hierarchical, dendritic structures of riverine networks are not well captured by existing spatial models. Here I use a simple geometric model and metapopulation modeling to make three points concerning the ecological consequences of dendritic landscapes. First, connectivity patterns of river-creek networks differ from linear landscapes, and these differences in connectivity can either enhance or reduce metapopulation persistence compared to linear systems, depending on the details of dispersal. Second, habitat fragmentation in dendritic landscapes has different (and arguably more severe) consequences on fragment size than in either linear or two-dimensional systems, resulting in both smaller fragments and higher variance in fragment size. Third, dendritic landscapes can induce striking mismatches between the geometry of dispersal and the geometry of disturbance, and as is the case for arid-lands fishes, such mismatches can be important for population persistence.

Here we study the spatial dynamics of a coinvading consumer‐resource pair. We present a theoretical treatment with extensive empirical data from a long‐studied field system in which native herbivorous insects attack a population of lupine plants recolonizing a primary successional landscape created by the 1980 volcanic eruption of Mount St. Helens. Using detailed data on the life history and interaction strengths of the lupine and one of its herbivores, we develop a system of integrodifference equations to study plant‐herbivore invasion dynamics. Our analyses yield several new insights into the spatial dynamics of coinvasions. In particular, we demonstrate that aspects of plant population growth and the intensity of herbivory under low‐density conditions can determine whether the plant population spreads across a landscape or is prevented from doing so by the herbivore. In addition, we characterize the existence of threshold levels of spatial extent and/or temporal advantage for the plant that together define critical values of “invasion momentum,” beyond which herbivores are unable to reverse a plant invasion. We conclude by discussing the implications of our findings for successional dynamics and the use of biological control agents to limit the spread of pest species.