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Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation (N̂area) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the hold-out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N̂area. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N̂area. While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an N̂area >1,000, where 30% had an N̂area <30. In this frequently encountered scenario of small N̂area, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.

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.

Archaeological applications of stable isotope data have become increasingly prevalent, and the use of these data continues to expand rapidly. Researchers are starting to find that recovering data for multiple elements provides additional insight and quantitative data for answering questions about past human activities and behaviors. Many stable isotope studies in archaeology, however, rarely move beyond comparisons of descriptive statistics such as mean, median, and standard deviation. Over the last decade, ecologists have formalized the concept of isotopic niche space, and corresponding isotopic niche overlap, to incorporate data from two or more isotopic systems into a single analysis. Additionally, several methods for quantifying isotopic niche space and overlap are now available. Here, I describe the evolution of the isotopic niche space concept and demonstrate the usefulness of it for archaeological research through three case studies using the recently developed rKIN package that allows for a comparison of different methods of isotopic niche space and overlap estimations. Two case studies apply these new measures to previously published studies, while a third case study illustrates its applicability to exploring new hypotheses and research directions. The benefits and limitations of quantifying isotopic niche space and overlap are discussed, as well as suggestions for data reporting and transparency when using these methods. Isotopic niche space and overlap metrics will allow archaeologists to extract more nuanced information from stable isotope datasets in their drive to understand more fully the histories of the human conditions.

Most assessments of the conservation status of Brazilian turtles use the IUCN geographic range criteria performed by the Minimum Convex Polygon (MCP). This technique often leads to over- or under-estimating the geographic distribution of rare, vulnerable, or endangered species. We aimed to demonstrate that using Species Distribution Models (SDM) on the geographic range assessment of turtles could be more accurate than using the minimum polygon convex. We reduced overestimation of species’ extent of occurrence by adding dispersal constraints, which avoids under- or over-estimating the impact of threatening events. The extent of occurrence derived from MCP was 31% higher than SDM on average, ranging from 4 to 311% higher. Using remaining habitat variables, we found that habitat loss within the predicted extent of occurrence increased by 79% from 1985 to 2019, and inferred population fragmentation increased by 161%. The distribution of turtles Acanthochelys radiolata, Acanthochelys spixii, Hydromedusa maximiliani, Hydromedusa tectifera, Mesoclemmys vanderhaegei, Phrynops williamsi, and Ranacephala hogei is severely fragmented, with most of their extent of occurrence being split into patches that are unavailable to the species persistence. Our findings highlight the importance of using SDM combined with dispersal constraints, which may further benefit from future information about the dispersal capacity of turtles. Furthermore, adding environmental layers to this combination makes it possible to discuss processes affected by habitat fragmentation, such as the fragmentation of species populations, an aspect essential to evaluate population viability and local extinctions.

Life space—the geographical extent of people’s movement within their communities—is closely linked to the physical and cognitive functions of older adults. It serves as a potential marker for mobility and health among aging populations, corresponding to the ability to age in place. Central to maintaining and expanding older adults’ life spaces are their out-of-home travel and activities. In a study with 1118 community-dwelling older adults in Singapore, participants’ travel patterns were tracked over a period of two weeks to identify how and when they travel, where they travel to, and why they travel (or what they do at out-of-home locations). Data was collected via a mobile application with precise location tracking and an accompanying log for participant input. This paper tests the feasibility and comprehensiveness of a list of activities that older adults engage in when they visit places outside their homes, using it to characterize travel motivations amongst older persons. Furthermore, it identifies the specific activities that are associated with larger life spaces, while controlling for sociodemographic differences in a hierarchical linear regression model. The findings suggest that the proposed list adequately represents older adults’ motivations for travel and activities within the community, making it suitable for applications in future research and analyses. Crucially, the results indicate that although trips to meet day-to-day needs were performed more frequently, trips for employment and social activities were the key drivers of larger life spaces or greater extents of travel.

Sharks play important functional roles in coral reef ecosystems. Studying reef shark populations’ spatial ecology also contributes important data for effective conservation planning. The purpose of this study was to define the home range of neonatal blacktip reef sharks (Carcharhinus melanopterus) around Moorea, French Polynesia, and compare estimates using both mark-recapture surveys and active acoustic telemetry. Mark-recapture surveys produced a minimum convex polygon (MCP) of 0.07 km2 that was significantly larger than the MCP derived from acoustic telemetry (0.02 km2). Acoustic telemetry produced 50 and 95% kernel utilization densities that were smaller (0.02 km2) and larger (0.14 km2) than home range estimates from mark-recapture surveys, respectively. Home range estimates from this study are the smallest that have been documented for neonatal blacktip reef sharks, possibly owing to the study sites’ proximity to deep channels. Mark-recapture and active acoustic telemetry are complementary approaches worthy of consideration where passive telemetry is impractical.

Statisticians frequently voice concern that their interactions with applied researchers start only after data have been collected. The same can be said for our experience with home-range studies. Too often, conversations about home range begin with questions concerning estimation methods, smoothing parameters, or the nature of autocorrelation. More productive efforts start by asking good (and interesting) research questions; once these questions are defined, it becomes possible to ask how various design and analysis strategies influence one's ability to answer these questions. With this process in mind, we address key sample-design and data-analysis issues related to the topic of home range. The impact of choosing a particular home-range estimator (e.g., minimum convex polygon, kernel density estimator, or local convex hull) will be question dependent, and for some problems other movement or use-based metrics (e.g., mean step lengths or time spent in particular areas) may be worthy of consideration. Thus, we argue the need for more question-driven and focused research and for clearly distinguishing the biological concept of an animal's home range from the statistical quantities one uses to investigate this concept. For comparative studies, it is important to standardize sampling regimes and estimation methods as much as possible, and to pay close attention to missing data issues. More attention should also be given to temporally changing space-use patterns, with biologically meaningful time periods (e.g., life-history stages) used to define sampling periods. Last, we argue the need for closer connections between theoretical and empirical researchers. Advances in ecological theory, and its application to natural resources management, will require carefully designed research studies to test theoretical predictions from more mechanistic modeling approaches.

Recent issues that have emerged in regard to raccoon dog (Nyctereutes procyonoides) include interaction with humans and disease transmission. Therefore, understanding their habitat characteristics and preferences is crucial in the effort to limit conflicts with humans. A total of thirteen raccoon dogs were captured from three regions in South Korea, each with distinct habitat characteristics. GPS trackers were attached for tracking the raccoon dogs’ movements. Utilizing GPS tracking data, Kernel Density Estimation (KDE), Minimum Convex Polygon (MCP), and Jacobs Index were applied to learn more about the habitat preferences of the raccoon dogs. According to the results, the habitat composition ratios for KDE and MCP showed that forests had the largest proportion. However, a habitat composition ratio similar to the land proportion of the area that they inhabit indicated that raccoon dogs had the ability to adapt to various habitats. Jacobs Index analysis revealed different habitat selection patterns compared to KDE and MCP, with forests showing neutral to negative selection despite comprising large proportions of home ranges. Our results highlight important methodological considerations when inferring habitat preferences from spatial data, suggesting that multiple analytical approaches provide complementary insights into animal space use.

ABSTRACT The African savannah elephant (Loxodonta africana) migrate in landscapes with patchily distributed food resources in semi-arid environments. GPS collar data in combination with the Minimum Convex Polygon approach (100% MCP) can be utilised to investigate elephant home ranges and spatial ecology. Mapping of suitable habitats in landscapes with isolated and patchy resources housing threatened and endangered species like the African savannah elephant is critical for conservation of their natural habitat. This study aimed to: (i) investigate the seasonal ranging patterns of the African savannah elephants and (ii) model the preferred habitat of the African savannah elephants in Mana Pools National Park (MNP) in Zimbabwe. Minimum Convex Polygon method was employed to delineate elephant home ranges and the MaxEnt algorithm was used to model their habitat preferences. There were significant differences (p < 0.05) in the size of the home ranges across all the three demarcated seasons (wet, transitional and dry). Elephant habitat preference is mainly driven by the presence, quantity and quality of palatable vegetation close to the Zambezi River in the Mana Pools National Park. GPS telemetry provides smart data for understanding elephant behaviour and movement patterns in semi-arid environments across seasons.