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Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home‐range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied ‘bottleneck’ positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super‐spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in understanding transmission dynamics, even for environmentally transmitted microbes like E. coli. This study is the first to use microbial genetics to construct and analyse transmission networks in a wildlife population and highlights the potential utility of an approach integrating microbial genetics with network analysis.

Territoriality and stable home ranges are a common space‐use pattern among animals. These ranges provide its inhabitants with important resources and thus favourable territories are associated with an increased fitness. While the role of territory quality and changes of territory ownership have often been investigated, the changes of territorial boundaries have been less studied. Here, we investigated space‐use changes in a social mammal species, applying a novel analytical approach, calculating long‐term dissimilarity in space use using distancematrices based on periodic utilization distributions. This approach makes it possible to identify different space‐use patterns, which cannot be distinguished by only considering changes between consecutive time periods. We analysed meerkat (Suricata suricatta) movements of a total of 24 different groups over a 16‐year period, resulting in 134 group years. We then correlated the identified home‐range changes to life‐history events and possible environmental drivers. Groups had stable territories for several years before they abandoned their home range mostly to move quickly to new areas where they again remained for several years. Of 26 identified sudden shifts, 22 occurred in the summer months and often involved distances larger than the original home‐range size. Home‐range movements that were close together in time were often also spatially clustered and moved in a similar direction. These shifts were often preceded by more frequent interactions between groups, but did not seem to be a product of direct displacements by other groups. The normalized difference vegetation index as a measure of food production and social factors such as dominance changes did not correlate to changes. Against our expectation space‐use changes were not accumulations of small changes, but more often involved long‐distance moves into unknown ranges. This means that the groups enter areas where they cannot profit from local knowledge. The methods used identify episodes of long stability alternated by sudden changes in meerkats and in general provides insight into long‐term space use. Our methods can be used to analyse long‐term space use, either within or across species. Sixteen years of meerkat movement data shows that after periods of stable territories, sudden range shifts occur. Groups move over greater distances into ranges that they did not use before. These territory shifts followed after an increase in inter‐group encounters.

: Core areas are important descriptors of animal space‐use patterns, but current estimation methods rely on arbitrary rules and potentially lead to imprecise or erroneous area estimates. We proposed a Bayesian statistical model that incorporates an individual‐based method for estimating core area boundaries. The model accounts for boundary uncertainty and multiple scales of clustering by partitioning a home range into ≥2 completely spatially random point patterns defined by a kernel density isopleth. We used data from coyotes (Canis latrans), bobcats (Lynx rufus), and red‐shouldered hawks (Buteo lineatus) to estimate core areas for individual animals. We also estimated core areas from simulated point patterns with known boundaries, varying numbers of points, and relative densities of points inside core areas, and compared estimates to those obtained using the 50% isopleth. Optimal isopleths for the empirical data ranged between 18.7% and 71.5%. We found no species‐specific range of core area isopleths. Across all simulated scenarios, our method outperformed the 50% isopleth‐based estimate, which consistently overestimated core areas. Minta overlap values were 20–40% higher across all scenarios for our method compared to the 50% isopleth. Minta overlap values were >75% in 90% of scenarios using our method. Objectively estimating core areas using our individual‐based method may lead to improved inference about which behavioral and ecological processes underlie observed space‐use patterns because of greater estimate precision.

We studied the impact of proximity to human concentrations, hikers, and field vehicles on mountain gazelles (Gazella gazella gazella) space-use patterns, flight distance, and visibility in the southern coastal plain of Israel. We collected data on gazelle behavior and human disturbance from fixed observation sites, drive counts, and pellet counts. The density of pellets was positively correlated with the distance to human concentrations, and the flight distance was positively correlated with human disturbance level, suggesting mountain gazelle space use and flight distance were affected by human disturbance. Gazelles were less visible in the more disturbed areas. Our findings provide a framework for conservation measures such as determining the size of buffer zones and where and when enforcement efforts should take place to keep mountain gazelle populations viable in spite of the ecological impacts of human encroachment on mountain gazelle habitat.

The space use strategies animals use to acquire resources needed for survival and reproduction reflect life history traits and individual behaviors. For large solitary carnivores, such as cougars (Puma concolor), prey, mates, and safe habitat in which to raise offspring, are resources that influence space use. Most animal home range studies investigate differences between sexes but fail to explore the space use patterns among individuals. We first used 95% minimum convex polygon (MCP), kernel density estimate (KDE), and Brownian bridge estimator (BB), to estimate the home range of 43 cougars satellite-collared in west-central Alberta, Canada, in 2016–2018. We found that adult males (MCP = 498 km2; KDE = 623 km2; BB = 547 km2) had home ranges that were more than twice the size of those of adult females (MCP = 181 km2; KDE = 273 km2; BB = 217 km2). We then used net squared displacement, path segmentation analysis, and multi-response permutation procedure, to examine the space use patterns of 27 female and 16 male cougars. We constructed a decision tree and found that 23% of cougars were dispersers (12% of females and 44% of males), 47% were residents (58% of females and 31% of males), 9% were seasonal home range shifters (12% of females and 6% of males), and 19% shifted to a new area during the study period (19% of females and 19% of males). We learned that dispersers all were subadults, whereas all residents, seasonal shifters, and shifters, were adults, except for one subadult male. Our study provides insights on animal home ranges with methods to categorize different space use strategies which could be used to help assess the dynamics of a population.

Contemporary integrated land-use transport models (ILUTMs) explicitly consider interactions between floorspace demand/supply and rent at fine spatial scales, which requires a good understanding between floorspace use pattern and competition of locations among socioeconomic activities. Floorspace use patterns are usually represented by space use coefficients (SUCs) by activity type by zone, which are then used to develop theoretical space-use-rent curves (SURCs), in order to reflect the elasticity between rent and floorspace consumption rates. Literature review indicates that existing studies mostly use borrowed SUCs or subjective judgement methods for synthesising base-year floorspace and developing SURCs. In general, their accuracy is largely unknown and synthesised floorspace could be highly inaccurate. In this study, a linear programming method is proposed to estimate localised SUCs by assuming that zonal population, employment and floorspace total data are available. Study results show that the method can provide localised SUCs and better SURCs than traditional methods. It is found that, as the size of the homogeneous optimisation areas (HOAs) decreases, the accuracy of zonal space totals estimated increases considerably. For example, the estimation error between the observed and estimated zonal space totals reduces from 76.2% under the most aggregate case to 24.7% under the most disaggregate case. The sum of square errors (SSEs) between the optimised SUCs and the SURCs also reduces to about one-quarter of their original values. The method proposed contributes to a procedural process to estimate localised SUCs with known accuracy, which is proved to be a better alternative to traditional synthesis methods. 当代的土地利用-交通整体规划模型（ILUTM）明确地在精细空间尺度上考虑楼面空间需求/供应与租金之间的相互作用，这需要很好地理解楼面空间的利用模式和社会经济活动对地点的竞争。为了反映租金与楼面空间消费率之间的弹性，空间利用模式通常用不同活动类型和不同区域的空间利用系数（SUC）表示，然后用 SUC 来建立空间利用-租金曲线（SURC）。文献回顾表明，现有的研究大多使用借来的 SUC 或主观判断方法来合成基准年度的楼面空间并建立 SURC。一般来说，它们的准确度在很大程度上是未知的，合成的楼面空间可能极不准确。本研究在假设区域人口、就业和楼面空间总数据可获取的前提下，提出用一种线性编排方法来估计局部 SUC。研究结果表明，该方法可以得出局部 SUC 和比传统方法更好的 SURC 。研究发现，随着匀质优化区域（HOA）的面积减小，区域空间总面积估计值的准确度显著增加。例如，区域空间总面积观测值和估计值之间的误差从最集中化例子中的 76.2% 降至最分散化例子中的 24.7%。优化的 SUC 和 SURC 之间的平方误差（SSE）总和也减少到其原始值的约四分之一。我们提出的方法有助于以确切的准确度估计局部 SUC，这被证明是对传统合成方法的更好替代。

Studying how animals interact with their environment is fundamental to informing conservation and management efforts, especially when examining large, wide-ranging carnivores in human-dominated landscapes. We hypothesized that the home ranges of bears are configured to exploit supplemental food (corn) and avoid people. In 2004–2016, we tracked 10 brown bears from the Dinaric-Pindos population using GPS telemetry, then used Brownian bridge movement models to estimate their home ranges. We related seasonal home range size to circadian period and density of supplemental feeding sites using generalized linear mixed-effect models. We also used ecological-niche factor analysis to study habitat composition within home range core areas in study areas characterized by different levels of human encroachment. We found that home range size was inversely related to density of supplemental feeding sites, and bears had larger home ranges at night (x̄ = 103.3 ± 72.8 km2) than during the day (x̄ = 62.3 ± 16.6 km2). Our results also revealed that bears living in more human-influenced areas concentrated their use far from human settlements and agricultural lands but stayed close to supplemental feeding sites. Our data suggest that bears alter their space-use patterns at the home range level in response to anthropogenic land use and food availability.

Urban expansion and associated habitat fragmentation are expected to be detrimental to global biodiversity. Natural habitat that is extensively modified often poses challenges to native fauna that must adapt to new conditions to survive. While some species decline in numbers or become locally extinct, the Eurasian red squirrel (Sciurus vulgaris) successfully inhabits cities. Because squirrels are sensitive to habitat fragmentation and their spaceuse patterns are influenced by the distribution and abundance of major food resources, their movement patterns are likely to be modified in response to changes in environmental conditions brought about by urbanization. Therefore, to investigate whether supplementary food resources are key to its success, the home ranges of squirrels inhabiting a large city park were related to both natural food sources (NFS) and anthropogenically-provided food sources (PFS) in 3 seasons. The combination of home ranges that were relatively small, year-round food availability and the lack of a seasonal body mass change indicates that the semi-urban environment can be highquality habitat. The squirrels' home ranges encompassed areas with a year-round NFS supply, but they shifted their home range core areas closer to PFS in seasons where they were more reliable, even though NFS were also abundant at the time. Additionally, heavier individuals' core areas were located closer to PFS. Consequently, our results show that human activity (i.e., via PFS) had a direct, measureable effect on squirrels' feeding habits and movement patterns even though NFS were available. However, the consequences of urbanization are not always detrimental for native animal species and an improved knowledge of energy resources and their effect on habitat use is important for understanding and minimizing the long-term impacts of humans on urban wildlife.

Background Models of utilization distribution in the form of partial differential equations have long contributed to our understanding of organismal space use patterns. In studies of infectious diseases, they are also being increasingly adopted in support of epidemic forecasting and scenario planning. However, as movement research shifts its focus towards large data collection and statistical modeling of movement trajectories, the development of such models has notably slowed. Methods Here, we demonstrate the continued importance of modeling utilization distribution to predict variation in space-use patterns over time. We highlight the considerable, yet largely untapped, potential of such models, which have historically been limited by the steady-state assumption due to longstanding technical constraints. Now, by adapting existing computational tools primarily developed for material science and engineering, we can probe beyond the steady states and unlock from them a broad spectrum of complex, transient space-use dynamics. Our approach requires little experience in numerical analysis and is readily accessible to model practitioners in ecology and epidemiology across diverse systems where movement is a critical feature. Results We illustrated our approach using a mix of canonical and novel case studies, covering topics from wildlife translocation to vaccine deployment. First, we revisited a classical model of canid territorial formation driven by scent-mediated conspecific avoidance. Transient space-use analysis uncovered previously hidden spatial dynamics that are ecologically informative. Next, we applied our approach to long-distance movement on realistic landscapes. Habitat and land-use heterogeneities markedly affected the transient space-use dynamics and short-term forecasts, even when the steady state remained unchanged, with direct implications for conservation management. Finally, we modeled transient space-use dynamics as both a response to and a driver of transient population dynamics. The importance of this interdependence was shown in the context of epidemiology, in a scenario where the movement of healthcare personnel is influenced by local outbreak conditions that are stochastically evolving. Conclusions By facilitating transient space-use analysis, our approach could lead to reevaluations of foundational ecological concepts such as home range and territory, replacing static with dynamic definitions that more accurately reflect biological realities. Furthermore, we contend that a growing interest in transient space-use dynamics, spurred by this work, could have transformative effects, stimulating new research avenues in ecology and epidemiology.

Ecologists have used Global Positioning Systems (GPS) to track animals for 30 years. Issues today include logging frequency and precision in estimating space use and travel distances, as well as battery life and cost. We developed a low‐cost (~US$125), open‐source GPS datalogger based on Arduino. To test the system, we collected positions at 20‐s intervals for several 1‐week durations from cattle and sheep on rangeland in North Dakota. We tested two questions of broad interest to ecologists who use GPS collars to track animal movements: (1) How closely do collared animals cluster in their herd? (2) How well do different logging patterns estimate patch occupancy and total daily distance traveled? Tested logging patterns included regular logging (one position every 5 or 10 min), and burst logging (positions recorded at 20‐s intervals for 5 or 10 min per hour followed by a sleep period). Collared sheep within the same pasture spent 75% of daytime periods within 51 m of each other (mean = 42 m); collared cattle were within 111 m (mean = 76 m). In our comparison of how well different logging patterns estimate space use versus constant logging, the proportion of positions recorded in 1‐ and 16‐ha patches differed by 2%–3% for burst logging and 1% for regular logging. Although all logging patterns underestimated total daily distance traveled, underestimations were corrected by multiplying estimations by regression coefficients estimated by maximum likelihood. Burst logging can extend battery life by a factor of 7. We conclude that a minimum of two collars programmed with burst logging robustly estimate patch use and spatial distribution of grazing livestock herds. Research questions that require accurately estimating travel of individual animals, however, are probably best addressed with regular logging intervals and will thus have greater battery demands than spatial occupancy questions across all GPS datalogger systems. GPS technology is increasingly important to animal ecology and environmental management, but cost and battery life remain limiting factors. We built a low‐cost GPS datalogger system for livestock and demonstrate how different logging frequencies optimize battery life and information‐gathering for different questions relating to space use and travel.