Landscape Ecology

Managing visitor-wildlife interactions in high-use protected areas requires identifying not only where visitors concentrate but also where visitor use overlaps with wildlife space use. We estimated relative human- macaque encounter potential in Kamikochi, Chubu Sangaku National Park, Japan, where Japanese macaques (Macaca fuscata) and visitors use the same frontcountry landscape. We integrated Mobile Spatial Statistics derived from mobile phone network data, trail-camera counts, and direct-follow location data from macaques along a frontcountry-backcountry gradient. Broad-scale 500-m grid estimates of daytime visitor presence for the 2023 and 2024 open seasons were allocated to 50-m trail sections and adjusted using counts from 10 trail cameras. Automated video counts based on YOLO11 and ByteTrack showed high agreement with manual counts of 100 validation videos (MAE = 0.10 persons/video, MAPE = 1.26%, exact agreement rate = 92.0%). Visitor-use and macaque-use data were converted into kernel density surfaces, and encounter potential was calculated as their normalized product. High encounter potential was concentrated in the central frontcountry: 87.1% of areas with index values [≥] 0.9 fell within the frontcountry and 12.9% within the backcountry. Areas with high visitor use or high macaque use alone did not necessarily show high encounter potential when the two distributions were spatially offset. These results show that spatial overlap provides management information not captured by visitor counts alone. Relative encounter potential can help prioritize patrols, visitor messaging, viewing management, and other interventions in protected areas where wildlife does not consistently avoid heavily used visitor spaces.

Habitat destruction leads to a severe decline in biodiversity. Ascertaining effects of habitat loss afterwards is regularly done, whereas predicting the effects of different habitat loss scenarios on biodiversity beforehand is much preferred but as of yet rarely possible. Such predictions would help inform management decisions to prevent biodiversity loss in the first place, or at least mitigate effects if necessary, by providing handles to set priorities in making trade-offs. To this end, we developed and applied an adapted version of the information-theoretical framework of Maximum Entropy Theory in Ecology (METE) to predict species-area relationships in tropical forest landscapes undergoing habitat loss and fragmentation. Species-area relationships provide information about biodiversity and can, in undisturbed systems, be predicted using METE, based on state variables such as the total number of individuals, the total number of species, and the total area size. In disturbed systems, however, it is established that METEs predictions are off. Using an individual-based model, we explore how METEs state variables should be adjusted, based on the landscape characteristics habitat cover and degree of habitat fragmentation, to restore its accuracy of species-area relationship predictions in disturbed environments. We validate our adjusted METE predictions with empirical data. Our adjusted METE predictions demonstrate the severity of fragmentations impact on biodiversity, relative to what could be expected in a pristine, continuous environment. With this new method, we can estimate the immediate effects of habitat loss on biodiversity, as well as its long-term effects.

Ground-nesting wild bees require suitable soil conditions for their below-ground development and overwintering life stages. Understanding how soil translocation and disturbance affect individuals and the following generations of bees is important for identifying conservation measures for ground-nesting wild bees. We investigated the effects of soil translocation and disturbance on the ground-nesting solitary spring mining bee Colletes cunicularius. In a replicated controlled design, we compared the effects of soil translocation with different degrees of disturbance on the number of surviving bees and the emergence time. We found that soil translocation and disturbance reduced bee survival but did not result in complete mortality, with some individuals surviving even under severe soil disturbance. There was no evidence that soil disturbance affected the timing of spring emergence, suggesting that disturbance is unlikely to alter the timing of nest construction and, consequently, the production of the next generation. Our results indicate that translocating soil containing overwintering bees could be a promising conservation measure to compensate for habitat loss caused by land conversion.

Identifying reliable indicators of dengue vector habitat is critical for proactive surveillance and spatially targeted vector control. Bird communities are sensitive to habitat quality and landscape condition, and their functional diversity may track ecological conditions that support Aedes mosquito populations. We tested whether bird functional diversity predicts dengue vector presence across two working landscapes in Costa Rica, comparing bird-based predictors against landscape composition, vegetation structure, and plant diversity. Bird functional dispersion (FDis), the spread of species in multidimensional trait space, was the top-ranked predictor for Ae. aegypti and Ae. albopictus larval presence and for adult Ae. aegypti, outperforming all other predictor classes. Probability of presence declined with increasing FDis, where functionally homogeneous bird communities, characteristic of disturbed landscapes, co-occur with higher vector presence, consistent with shared habitat filtering. Functional diversity estimations may offer a practical signal for identifying habitats at elevated dengue vector risk.

Understanding the ecological determinants of species ranges is a central goal of ecology. Novel tools like global datasets and machine learning models allow us to describe species ranges with increasing scope and accuracy, and to develop and test ecological hypotheses about their determinants. Here, I focus on the widespread nuisance mosquito and dog heartworm vector Aedes sierrensis, a tree hole-breeding mosquito that is widespread and abundance within its native range in western North America, and develop species distribution models (SDMs) to characterize the species range and its environmental determinants. I find that the species range is highly predictable from long-term average bioclimatic variables. Temperature and precipitation were the primary determinants: suitability was highest at wet-season average temperatures of 0 - 10{degrees}C, minimum temperatures of -5 - 5{degrees}C, and summer temperatures of 8 - 22{degrees}C in environments with adequate seasonal rainfall concentrated in the winter. After accounting for climate, land cover variables showed minimal importance for prediction, but suitability was higher in forests and outside of urban areas. The results are consistent with ecological knowledge of the Ae. sierrensis life cycle from field observations and previous laboratory experiments, suggesting that individual physiological constraints scale up to determine species distributional limits.

West Nile virus (WNv) in eastern North America is transmitted primarily by Culex pipiens and Culex restuans, two cryptic vector species whose relative abundance shifts seasonally and across urbanization gradients - gradients that are themselves now shifting under continuing climate change and urban expansion. Because the two species are difficult to separate morphologically and are typically pooled in routine surveillance, this turnover is seldom tracked directly, and agencies lack species-resolved tools to anticipate when, where, and how vector communities will reorganize. Yet the timing of this turnover has been linked to the seasonal timing and intensity of human WNv cases, making it a potentially forecastable correlate of risk. We molecularly identified 9,789 Culex collected over six years, of which 5,808 came from a designed multi-year study in the Baltimore-Washington metropolitan region, and the remainder from Philadelphia and Chicago for cross-regional comparison. We combined negative-binomial generalized linear mixed models of species-specific abundance in Baltimore-Washington with Gradient Forest models of compositional turnover across all three regions. The two species diverged along both the urbanization and thermal gradients: Cx. pipiens abundance increased with impervious surface and with temperature, whereas Cx. restuans declined along both. Consequently, highly urbanized areas remained Cx. pipiens-rich across the season, whereas suburban, low-to-moderate-development landscapes exhibited the largest seasonal shifts in community composition. Accumulated degree-days (ADD) and weekly mean temperature were the most consistent drivers of turnover across regions, with the shift from Cx. restuans to Cx. pipiens fastest between 594 and 610 ADD. These patterns translate into three operational tools for WNv management: a climate-based degree-day window that lets agencies forecast the community shift from temperature data before it is detectable in trap composition, identification of suburban landscapes as priority targets for intensified surveillance and early intervention, and species-resolved environmental responses that help anticipate how community composition may shift within these regions as local climate and land-use conditions change. Together they offer a path from reactive surveillance to anticipatory, spatially targeted, and forward-looking management of WNv risk under environmental change. Open Research StatementAll datasets and R scripts used to generate the results and figures in this study will be made publicly available on DRYAD (DOI pending) upon acceptance of the manuscript. Climate data are publicly available from the PRISM Climate Group (PRISM Group, Oregon State University). Landscape predictor datasets are publicly available from the sources cited in Methods (Dewitz 2023; WorldPop 2018; U.S. Census Bureau 2020; Zell & Sanford 2020; Gesch et al., 2018; Didan 2015).

Achieving global conservation and restoration targets requires knowing not only how much land to conserve and restore, but also where such actions will have the greatest ecological effect. Conservation and, in particular, restoration priorities are often inferred from habitat condition, opportunity, or broad ecological value, yet these criteria do not necessarily identify where local changes generate the largest landscape-scale consequences. Building on sensitivity-based conservation prioritization, we extend the approach to derive ecological loss and gain potential by differentiating a persistence-relevant connectivity metric with respect to local habitat condition. Sensitivity identifies areas where local degradation or improvement would most strongly affect connected habitat at the landscape scale. Combining sensitivity with current condition distinguishes high loss potential, where degradation would have large ecological consequences, from high gain potential, where ecological improvement could generate large benefits. Applying the framework to national forest-naturalness data in Norway, we show that loss and gain potential are spatially related but far from redundant, and that gain potential is not simply concentrated in the most degraded forests. Both high-loss and high-gain areas were poorly represented within current protected areas, although loss potential was consistently better represented than gain potential. Extending sensitivity-based prioritization in this way provides a general and scalable means of linking local habitat-condition change to persistence-relevant landscape outcomes, while distinguishing the ecological potentials that can inform conservation and restoration decisions.

Climate change will alter habitat suitability for plant communities in mountain ecosystems of the Western Himalaya. There will be contrasting magnitudes and spatial configurations of these changes for different plant species, and it is an important question how to translate these contrasts into effective climate-proof multi-species conservation planning. This study assessed current habitat associations and projected changes in suitable habitat for two contrasting species, Meconopsis aculeata and Rhododendron campanulatum, in Himachal Pradesh, India. Field surveys and spatially filtered occurrence records were combined with MaxEnt modelling using 10 non-collinear environmental predictors. Habitat suitability was projected to 2050 and 2070 under Shared Socioeconomic Pathways; SSP1-2.6, SSP2-4.5 and SSP5-8.5 using HadGEM3-GC31-LL. Field observations showed contrasting habitat associations, with M. aculeata occurring mainly in open alpine herbaceous communities and R. campanulatum in subalpine woody communities. Elevation was an important predictor for both species while climatic predictors contributed differently to their relative importance and response patterns. Future projections indicated reductions in suitable habitat of approximately 25% for M. aculeata and 27-28% for R. campanulatum. Habitat-change maps showed heterogeneous patterns of loss, gain and persistence rather than uniform contraction, and centroid displacement remained small (7.7-8.5 km). We performed proximity analyses to show which currently suitable areas are most close to areas of future gain, in order to support the design of climate-proof protected areas. We discuss how interspecific differences in response are relevant to multi-species conservation planning that considers the spatial relationship between current high-suitability areas and the gain, loss and redistribution of future suitable habitat.

Land use change is widely recognized as a major driver of emerging zoonotic diseases. Yet, while forest plantations are expanding worldwide, their consequences on the circulation of infectious agents in wildlife remain poorly understood. We investigated how native pine plantations and broadleaved habitats shape rodent-associated infectious hazard in the generalist wood mouse (Apodemus sylvaticus), in southwestern France. Using a multi-season survey, we screened 424 wood mice for a broad range of infectious taxa and compared infection probability and infected-host abundance among pine plantation edges, broadleaved hedgerows embedded within pine plantations and broadleaved forests. Using a combination of linear mixed model and joint species distribution modelling, we tested whether infectious hazard differed between pine plantations and broadleaved forests, whether broadleaved hedgerows shifted these patterns towards those observed in broadleaved forests, and how these habitat-related patterns varied across seasons. We detected 22 potentially infectious taxa, including nine recognized zoonotic bacteria. Season strongly influenced infectious patterns, while habitat effects were taxa and season dependent. In spring, pine plantation edges increased infection probability for Bartonella grahamii and Neoerhlichia mikurensis compared to broadleaved forest, but their strong negative effect on wood mouse abundance resulted in consistently lower infected-host abundance for most infectious taxa. Broadleaved hedgerows showed distinct and season-dependent patterns with infectious profiles resembling those of pine edges in spring, but with higher infectious hazard than both other habitats for several taxa in autumn. Our results highlight that habitat effects on infectious hazard cannot be inferred from infection probability alone. Pine plantations may reduce rodent-associated zoonotic hazard through their strong negative effect on host abundance, despite increasing infection probability for some agents. Forest diversification and restoration initiatives, such as broadleaved hedgerows here, may have unforeseen epidemiological consequences, underscoring the need to integrate disease ecology into forest management and restoration to anticipate variation in zoonotic risks, while maximizing ecological benefits.

Rapid detection is critical for successful management of transboundary animal disease incursions in wild host populations. However, decisions about how best to allocate wildlife disease surveillance effort must be made under high uncertainty. Risk-based surveillance can improve efficiency but approaches that focus surveillance too narrowly on expected high risk areas could have low power to detect unexpected events. We developed and field-tested an adaptive, search-theoretic surveillance framework for detecting transboundary animal disease incursions in wild ungulates in New South Wales, Australia. Key principles that guided the frameworks development included accommodating uncertainty, regularly updating search priorities based on expected risk and spatial coverage, and a flexible structure that allows the system to respond to changing information or conditions over time. We created a coarse state-wide risk map that served as a weakly informative prior describing expected variability in disease incursion risk, loosely focused on foot and mouth disease virus (FMDv). Risk and search values were updated every three months based on realised surveillance effort and estimated detection probabilities over the preceding 12 months, meaning that areas of persistently high risk could nonetheless have low search value if they had recently been intensively searched. Surveillance activities collected blood and swab samples from 1,964 wild pigs (Sus scrofa) during 110 sampling occasions over a two-year evaluation and refinement period. Activities sought to simulate FMDv surveillance operations, but FMDv serological tests were not available at the time. Effort was consistently concentrated in areas of high search value, with at least 74% of sampled cells in the highest risk class. Estimated surveillance system sensitivity ranged from 0.86 to 0.93 over five successive updating cycles and increased as operational procedures were refined. Although the surveillance program was based on FMDv incursion risk, it also fulfilled its secondary objective of detecting unexpected events, including detecting Japanese encephalitis virus in wild pigs before detections in humans and domestic animals. By combining risk-based surveillance with adaptive updating of search priorities in a modular structure, the framework provided a flexible and generalisable approach for early detection of transboundary and emerging animal disease incursions in wildlife populations under high uncertainty.

Abstract Habitat configuration influences population connectivity and, consequently, genetic structure. River networks provide heterogeneous, hierarchically arranged environments that shift drastically from upstream to downstream. We compared congeneric species across Ephemeroptera, Plecoptera, and Trichoptera, emphasizing longitudinal replacement (upstream to downstream) and the rarely studied wet rock (hygropetric) habitats. We surveyed six rivers on the Muroto Peninsula, Japan, qualitatively sampling aquatic insects at 63 sites. Cluster analysis based on the environmental data classified surveyed sites into four clusters. We analyzed a total of 10 species from four genera that exhibit longitudinal replacement patterns within genera. Genetic analyses based on the mitochondrial cytochrome c oxidase subunit I region revealed that upstream species showed higher genetic diversity than downstream species. In contrast, species adapted to hygropetric exhibited the lowest genetic differentiation among all habitat types. A novel contribution of this study is the inclusion of hygropetric species. The surprisingly low differentiation in hygropetric species suggests high connectivity, similar to lentic species. By comparing congeneric taxa across orders within environmentally similar rivers, we reduce phylogenetic and environmental confounds, strengthening inference that habitat configuration and dispersal traits jointly shape genetic structure. These findings provide a new perspective on riverine spatial ecology and underscore the importance of microhabitat-aware comparisons for evolutionary inference.

1. As city populations expand globally, understanding how urbanization shapes ecosystem services is increasingly important. Urban agriculture plays a key role in food access, particularly in low-income areas with limited access to fresh produce. Arthropods play important roles in these systems through pollination, herbivory, pest control, and nutrient cycling. Urban development of natural areas often reduces biodiversity, including that of arthropods. Additionally, urban biodiversity frequently correlates positively with socioeconomic conditions, a pattern known as the luxury effect. 2. In this study, we examined how gradients in urbanization and socioeconomic context relate to local and landscape habitat quality, arthropod abundance, and pollination services across 21 community gardens in the Denver, CO Metro Area. To quantify ecosystem services, we employed a sentinel plant, Cucumis sativus (cucumber), and collected arthropods (insects, myriapods, arachnids, isopods), assigning them to functional groups based on their known feeding behavior. We evaluated direct and indirect pathways linking urban matrix characteristics to arthropod-mediated ecosystem services. 3. The abundance of all arthropod groups (including pollinators, herbivores, and natural enemies) declined steeply with increasing impervious surface. Neighborhood wealth was associated only with bee species richness and Shannon diversity and displayed a hump-shaped pattern that peaked at wealth levels 35-46% above the national average, then declined. Multivariate GLMs revealed that Western honeybees (Apis mellifera) were the only species driving differences in bee community composition, with their abundance positively related to neighborhood wealth. 4. We also observed pollen limitation in our cucumber plants, and as bee Shannon diversity increased, cucumber production increased. Environmental covariates alone showed no direct relationship with cucumber production, indicating that insect pollination mediated this effect. This work identifies the biophysical and social-ecological drivers of urban arthropod biodiversity and ecosystem services, revealing where crop production may be most vulnerable to decline in cities.

Protected areas are vital to the recovery of endangered species. Of the 24 remaining endemic passerines in Hawaii, 16 species are endangered or critically endangered. Yet protected areas in the archipelago are changing as a result of climate change and biological invasions. For example, the non-native southern house mosquito (Culex quinquefasciatus), the primary vector of avian malaria (Plasmodium relictum), has been encroaching upward as higher elevations warm. How the ranges of endemic birds have also shifted their range upward in the Ka[u] Rainforest, the largest native forest on the Island of Hawaii, is not well understood. We used hierarchical distance sampling to characterize how population density has changed from 2002 to 2024 for eight endemic bird species in the Ka[u] Rainforest. For five species, the most parsimonious model included an interaction between year and a quadratic elevation effect. Species that were most common at lower elevations in 2002, e.g., Apapane (Himatione sanguinea), tended to be most common at mid-elevations by 2024. Species that were already most common at higher elevations, such as Iiwi (Drepanis coccinea) and Hawaii [A]kepa (Loxops coccineus), tended to decline in density. For example, Iiwi density at 1,530 m declined from 3.31 birds per ha (95% confidence interval [CI]: 2.67-4.11) to 1.34 birds per ha (95% CI: 1.05-1.71), and Hawaii [A]kepa were completely extirpated from elevations below 1,530 m by 2024. Our results demonstrate how the elevational ranges of endemic species have shifted in response to climate change, biological invasions, and habitat degradation. Translating these results into conservation measures may require a more thorough investigation of the causal mechanisms of these range shifts with finer scale habitat data, under the same hierarchical modeling framework.

Introduction and Aim: Future climate instability creates obstacles for organisms across taxa, especially in areas that experience higher stochasticity. Wetland wading bird populations are a prime example of this scenario, as the hydrologic cycles that drive population success in these habitats depend heavily on the predictability of wet and dry periods. When fluctuations in wet and dry periods shift excessively, populations may be forced to relocate or shift migration routes. Our goal is to predict if worsening environmental conditions could drive increases in facultative migration and subsequent residency shifts Location: State of Florida, USA. Methods: We developed an individual-based movement model motivated by wading birds in southern Florida to determine if this concept of climate-driven range or residency shift is likely to occur in the future. To further interpret the outputs of this individual-based model, we compared habitat suitability for the American wood stork across Southeast of the United States between 2010 and 2025, 2055 and 2085. Results: Our IBM model predicted that a northward residency shift is predicted to occur given the environmental scenario of harsher fluctuation via climate instability in the Everglades. Moreover, our findings suggest that wood storks with partial and/or facultative migration may be more likely to adapt to these fluctuations. Our suitability maps showed spatially heterogeneous responses, with notable increases in northern Georgia, and the Carolinas, and consistent declines in northern Florida, indicating a gradual northward shift in suitable breeding habitat. Main Conclusions: Conserving networks of suitable wetlands across broader spatial scales, may be critical for facilitating movement and persistence under hydrologic instability. Mechanistic models such as the one presented in this study can help anticipate these shifts and inform proactive conservation planning. This model can also be adapted to fit similar wetland systems, or other landscapes with various population and movement phenotypes present.

The tidal wetlands of Louisiana comprise about 25% of those found throughout the conterminous United States yet estimates of wetland loss rates in the region between 1932 and 2016 have exceeded 60 km2 yr-1. To mitigate further degradation and wetland loss in the region, a globally unprecedented $50B, 50-year plan for coastal Louisiana is driving restoration efforts, and demand exists from multiple stakeholders for regularly updated, regional-scale, accurate land cover information. We used machine learning (random forests; RF) and cloud computing to develop a new Landsat-based, marsh vegetation community geospatial dataset. The dataset depicts wetland vegetation community types defined in a previous study at annual (1985-2025) time steps at 30-m resolution. An RF algorithm was used to integrate training samples with feature variables derived from Landsat imagery, and the resulting geospatial data product achieved an overall correct classification rate of 78%. The approach for development of the land cover dataset presented here has potential for application in other coastal wetland habitats throughout the world.

Plant phenology influences biotic interactions and the cycling of water, carbon, and nutrients, ultimately affecting global climate. Accurately quantifying phenological dynamics is therefore essential for ecological forecasting, yet estimates vary widely depending on data sources, smoothing approaches, and date-extraction techniques. For this reason, the selection of an appropriate processing pipeline requires benchmarking under controlled conditions. In this study, we use a Land Surface Model (LSM) to simulate ecosystem dynamics at four temperate forest sites under five lesser studied climate perturbations. The resulting synthetic dataset allows us to control for environmental conditions while comparing commonly used phenological detection methods (relative thresholds at 10%, 20% and 50%, highest rate of change, detection of inflection points in the curvature angle, detection of recovery and senescence points). Amongst tested methods, we find general agreement in responses to climate alterations, where inflection and low thresholds (10%) methods provide the closest match to the model predicted start of season, with a maximum discrepancy of 4 days. Higher thresholds (20%, 50%) are closer to the model predicted end of season, though discrepancies reach up to 10 days. High thresholds and rate-of-change methods also reliably detect the peak of season. We further show that some methods detect phenological responses missed by temperature-only type growing season, supporting the need for more realistic phenological representations in LSMs. Beyond evaluating extraction methods within a common framework, our study expands on the potential of using LSMs as sandboxes for ecological testing rather than solely predictive tools.

Urban and rural woodlands have become important habitats for wildlife, providing opportunities for ecological and ethological adaptation to changing environmental conditions. Due to limited tree resources in anthropogenic woodlands, different species are often forced to interact more closely, and one possible outcome of such interactions is hybridisation. Recent studies have shown that hybrids between the uncommon Syrian Woodpecker (Dendrocopos major) and the often common Great Spotted Woodpecker (Dendrocopos syriacus) are relatively widespread, yet rare, in sympatric populations (Bakai et al. 2026). To understand the factors determining their occurrence in rural and urban landscapes, we conducted an intensive survey across Europe. We examined the effects of environmental factors, particularly woodland characteristics, as well as the co-occurrence of hybrids with the parental species and European Starlings (Sturnus vulgaris) (hereafter Starling). Spatial analyses revealed that the parental species generally have allotopic distributions, with Syrian Woodpeckers (hereafter SW) being more common in scattered woodlands in both rural and urban landscapes, while Great Spotted Woodpeckers (hereafter GW) are more commonly associated with denser woodlands and forests. Although in some areas the two species can co-occur, which provides opportunities for interspecific hybridisation, hybrid offspring showed no clear preference for any particular woodland type. However, they tended to occur in proximity to one of the parental species, usually the species that was locally rarer. Starlings were not identified as a factor influencing hybrid distribution. These results suggest that hybrids have a relatively broad ecological tolerance, potentially facilitated by heterosis. These findings contribute to our understanding of the factors influencing interspecific hybridisation and may have implications for the management of urban and rural woodlands to preserve habitats for the SW, an uncommon and strictly protected species in the west of the European Union.

Landscape management facilitating animal movement is essential for sustaining urban wildlife populations and ecosystem services. Although linear vegetation corridors effectively conserve terrestrial animal movements, it remains unclear which landscape elements serve as pathways for aquatic organisms. Urban waterbirds frequently utilize rivers, but the specific characteristics that render certain river segments crucial as movement corridors have yet to be elucidated. This study aims to identify the characteristics of river segments functioning as critical corridors based on waterbird movement strategies. We tracked the movement behaviors of the Black-headed Gull (Chroicocephalus ridibundus), a river-dependent species in daily movements, and identified key movement corridors in highly urbanized Tokyo, Japan. Using GPS tracking data from gulls, we evaluated how river sinuosity and surrounding feature heights influenced tendencies to follow rivers across various temporal scales and perceptual ranges. Furthermore, we predicted and mapped these tendencies across individual river segments in central Tokyo. Gulls tended to fly along river segments with tall features during instantaneous to mid-term decision-making. In contrast, they utilized straight channels for long-term decisions. Although results varied slightly depending on the spatial scale they can perceive, this overarching trend remained consistent. Furthermore, our model predicted that the lower reaches of the Sumida River serve as critical movement corridors. This predictive tendency was also highly robust across all time scales. While straightened river segments with high feature heights may provide unsuitable habitats for diverse taxa, we emphasize their ecological value and argue that they should be conserved as essential movement corridors for waterbirds.

Vegetation plays a critical role in shaping fine-scale tick density by structuring local microclimates, habitat resources, and host interactions that largely determine tick survival and activity, with implications for tick-borne disease transmission. Tick burden in the United States continues to increase, with annual reported tick-borne disease cases more than doubling from 2004 through 2019. These trends call for a broader scope of tick-mitigation strategies that go beyond traditional prevention measures that focus on personal protection; and there is an opportunity to inform habitat-level interventions. Here, we quantify fine-scale vegetation structure across 48 subplots at tick surveillance sites in Western Massachusetts to assess local habitat features associated with Ixodes scapularis density and potential for human-tick contact risk. In assessing understory and overstory vegetation attributes, this study identified life stage-specific differences in predicted tick density, with higher adult densities in areas with deeper leaf litter and presence of invasive plant species and nymphs associated with high presence of ferns. Both adult and nymphal Ix. scapularis were predicted at higher densities in relatively low basal area forests (18.9 to 26.9 m2/hectare). This study design provides a simple, low-cost framework for organizations looking to incorporate tick-informed forest measurement ecology into their public health tick surveillance protocols with minimal training.

Ecologists remotely track movement steps of animals (e.g., via global positioning systems) and use step selection functions to study the effect of environmental factors upon their movement decisions. Constructing such functions requires pairing each observed step with some number of unobserved but feasible comparison steps. Larger numbers of comparison steps generally yield better estimates but also incur potentially challenging computational demands. Thus, it is important to determine an appropriate number of comparison steps. No established guidance exists for this decision. Here, we use simulated tracks to assess how many comparison steps are needed, fitting each set of steps to a conditional logistic regression model. We monitor errors in estimated effects for several classes of tracks, identifying the number of comparison steps for which mean relative absolute error in estimated effects is consistently low. By this criterion, 32 comparison steps per observed step are needed for our primary class of simulated tracks. Tracks in more homogeneous landscapes, tracks with shorter mean step lengths, or shorter tracks generally require more comparison steps (ranging from 64 to 128 per observed step) to achieve the same level of accuracy. Longer tracks generally require fewer comparison steps (16 per observed step). These results clearly demonstrate that the number of comparison steps influences how well step selection functions estimate covariate effects and provides initial direction in a research area that currently lacks quantitative guidance. Movement ecologists should take care when selecting the number of comparison steps paired with each observed step because those decisions matter.