Fine Spatial Scales Research Articles

Simulation of background neuronal activity and noise in human intracranial microwire recordings

BackgroundHuman intracranial microwire recordings allow measurement of neuronal activity in human subjects at a fine temporal and spatial scale. The recorded extracellular potentials represent a mixture of action potentials from nearby neurons, local field potentials, and other noise sources. Signal processing of these recordings is used to separate the activity of putative single neurons from other background and noise. To better understand the separation of single neuron activity, one approach is to simulate the signals produced by neurons firing action potentials combined with background activity and noise. New methodThis paper characterizes the background activity and noise in human intracranial microwire recordings and presents an accurate and efficient method of simulation using an infinite impulse response filter to color white noise. Results and comparisonThis method reproduces the power spectral density of the background activity and noise over a frequency range of 1–5000 Hz and is over 200 times faster than previously used methods. It thus facilitates large scale studies of variation of noise sources, field potentials, and processing parameters. It performs equivalently in terms of spike sorting to simulation using white noise. Another advantage is that the simulated signals are known to arise from a pseudorandom number generator and cannot be the result of detecting simulated background spiking activity. ConclusionsThis approach provides a rapid and accurate method of simulating background noise and neural activity in human intracranial microwire recordings. It is suitable for use in large scale simulations to study spike sorting in this type of recording.

Fine-scale spatial variability of marine acoustic environment corresponds with habitat utilization of Indo-Pacific humpback dolphins in Hong Kong waters

Acoustic properties of the underwater environment are important in maintaining biological processes of various marine organisms. However, with the increasing level of underwater noise in the global ocean, there is a growing need to better understand how marine animals use soundscape cues in their habitat selection. Indo-Pacific humpback dolphins (Sousa chinensis) inhabiting the Pearl River Estuary, southeast China, live in one of world’s most developed and noisiest coastal environment and are subjected to many sources of anthropogenic noise. To investigate whether spatial variability of underwater soundscape corresponds with their habitat utilization, we collected daytime underwater recordings in western Hong Kong waters from mid-2016 to mid-2018, and quantified the spatial pattern of marine acoustic environment and its differing characteristics in a fine spatial scale. We developed a framework of soundscape information retrieval to investigate spectral features that may facilitate identification of dolphins’ core habitats. Our findings reveal that a spectral feature, which peaks at 2 kHz, is a reliable predictor of humpback dolphin core habitat. Further modelling of spatial and seasonal variations of underwater soundscape demonstrates that the relative strength of this spectral feature is positively correlated with the sighting rates of humpback dolphins throughout the year. Although the source of the 2 kHz feature remains unknown, it is likely associated with humpback dolphins’ prey. We suggest that underwater acoustic environment represents an important component in evaluating the quality and suitability of coastal habitats for the daily needs of this threatened dolphin species. Local and regional conservation authorities should include habitat-specific baseline soundscape data when developing conservation management strategies.

Coyotes in the Great Basin desert do not exhibit a spatial response following the removal of anthropogenic water sources

Coyote (Canis latrans) range expansion into desert ecosystems has highlighted the role of anthropogenic water sources in arid ecosystems. Despite hypotheses that additional water facilitated this expansion, previous studies reported that coyotes did not exhibit a spatial or dietary response to removal of anthropogenic water. We used GPS data to examine if coyotes responded to water removal at a finer spatial scale than previously investigated. Our integrated step selection analysis did not find evidence that coyotes adjusted their distance to water following water removal. Vegetation was an important factor in habitat selection of coyotes, with riparian and agricultural areas being the most selected among vegetation types. Coyotes selected for locations where hunting and trapping was prohibited. Possibly the cause of increased coyote abundance in our study area was not due to the introduction of anthropogenic water sources but rather due to the cessation of regional lethal predator control programs. These two management decisions both occurred in the 1970s, therefore, their influences on the subsequent increase of coyote abundance may have been conflated. Our results, in combination with previous studies, provide evidence that coyotes are desert-adapted carnivores that do not rely on anthropogenic water sources.

Attribution of individual methane and carbon dioxide emission sources using EMIT observations from space.

Carbon dioxide and methane emissions are the two primary anthropogenic climate-forcing agents and an important source of uncertainty in the global carbon budget. Uncertainties are further magnified when emissions occur at fine spatial scales (<1 km), making attribution challenging. We present the first observations from NASA's Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer showing quantification and attribution of fine-scale methane (0.3 to 73 tonnes CH4 hour-1) and carbon dioxide sources (1571 to 3511 tonnes CO2 hour-1) spanning the oil and gas, waste, and energy sectors. For selected countries observed during the first 30 days of EMIT operations, methane emissions varied at a regional scale, with the largest total emissions observed for Turkmenistan (731 ± 148 tonnes CH4 hour-1). These results highlight the contributions of current and planned point source imagers in closing global carbon budgets.

An artificial neural network emulator of the rangeland hydrology and erosion model

Machine learning (ML) is becoming an ever more important tool in hydrologic modeling. Previous studies have shown the higher prediction accuracy of those ML models over traditional process-based ones. However, there is another advantage of ML which is its lower computational demand. This is important for the applications such as hydraulic soil erosion estimation over a large area and at a finer spatial scale. Using traditional models like Rangeland Hydrology and Erosion Model (RHEM) requires too much computation time and resources. In this study, we designed an Artificial Neural Network that is able to recreate the RHEM outputs (annual average runoff, soil loss, and sediment yield and not the daily storm event-based values) with high accuracy (Nash-Sutcliffe Efficiency ≈ 1.0) and a very low computational time (13 billion times faster on average using a GPU). We ran the RHEM for more than a million synthetic scenarios and train the Emulator with them. We also, fine-tuned the trained Emulator with the RHEM runs of the real-world scenarios (more than 32,000) so the Emulator remains comprehensive while it works specifically accurately for the real-world cases. We also showed that the sensitivity of the Emulator to the input variables is similar to the RHEM and it can effectively capture the changes in the RHEM outputs when an input variable varies. Finally, the dynamic prediction behavior of the Emulator is statistically similar to the RHEM.

Characterizing the rod pathway in cone-dominated thirteen-lined ground squirrels.

AII-amacrine cells (AIIs) are widely accepted as a critical element of scotopic pathways mediating night vision in the mammalian retina and have been well-characterized in rod-dominant mice, rabbits, and non-human primates. The rod pathway is characteristic of all mammalian eyes, however, the anatomic and physiologic role of AIIs and the rod pathways in cone dominant thirteen-lined ground squirrels (TLGS) is limited. Here, we employed both immunohistochemistry and electrophysiological approaches to investigate the morphology of AIIs and functional aspects of the rod pathway in TLGS. In all TLGS retinas examined, putative AIIs were calretinin-positive and exhibited connections to rod bipolar cells with decreased cell density and expanded arborization. Notably, AIIs retained connections with each other via gap junctions labeled with Connexin36. Comparisons between single photoreceptor recordings and full-field electroretinograms revealed scotopic ERG responses were mediated by both rods and cones. Thus, the components of the rod pathway are conserved in TLGS and rod signals traverse the retina in these cone-dominant animals. AIIs are sparsely populated, matching the diminished rod and rod bipolar cell populations compared to rod-dominant species. The infrequent distribution and lateral spacing of AII's indicate that they probably do not play a significant role in cone signaling pathways that encode information at a finer spatial scale. This contrasts with the mouse retina, where they significantly contribute to cone signaling pathways. Therefore, the AII's original function is likely that of a 'rod' amacrine cell, and its role in cone pathways in the mouse retina might be an adaptive feature stemming from its rod dominance.

Recharge Estimation Approach in a Data-Scarce Semi-Arid Region, Northern Ethiopian Rift Valley

Sustainable management of groundwater resources highly relies on the accurate estimation of recharge. However, accurate recharge estimation is a challenge, especially in data-scarce regions, as the existing models are data-intensive and require extensive parameterization. This study developed a process-based hydrologic model combining local and remotely sensed data for characterizing recharge in data-limited regions using a Basin Characterization Model (BCM). This study was conducted in Raya and Kobo Valleys, a semi-arid region in Northern Ethiopia, considering both the structural basin and the surrounding mountainous recharge areas. Climatic Research Unit monthly datasets for 1991 to 2020 and WaPOR actual evapotranspiration data were used. The model results show that the average annual recharge and surface runoff from 1991 to 2020 were 73 mm and 167 mm, respectively, with a substantial portion contributed along the front of the mountainous parts of the study area. The mountainous recharge occurred along and above the valleys as mountain-block and mountain-front recharge. The long-term estimates of the monthly recharge time series indicated that the water balance components follow the temporal pattern of rainfall amount. However, the relation of recharge to precipitation was nonlinearly related, showing the episodic nature of recharge in semi-arid regions. This study informed the spatial and temporal distribution of recharge and runoff hydrologic variables at fine spatial scales for each grid cell, allowing results to be summarized for various planning units, including farmlands. One third of the precipitation in the drainage basin becomes recharge and runoff, while the remaining is lost through evapotranspiration. The current study’s findings are vital for developing plans for sustainable management of water resources in semi-arid regions. Also, monthly groundwater withdrawals for agriculture should be regulated in relation to spatial and temporal recharge patterns. We conclude that combining scarce local data with global datasets and tools is a useful approach for estimating recharge to manage groundwater resources in data-scarce regions.

Using individual‐based habitat selection analyses to understand the nuances of habitat use in an anthropogenic landscape: a case study using greater sage‐grouse trying to raise young in an oil and gas field

Habitat selection analyses conducted at an individual level may reveal patterns in selection not apparent when individuals are pooled in population‐level approaches. Using GPS transmitters that gather high‐resolution location data, we explored fine‐scale habitat selection and space use within home ranges of female greater sage‐grouse Centrocercus urophasianus that raised young (brood‐rearing sage‐grouse) in an oil and gas development area. To evaluate fine‐scale habitat selection of brood‐rearing sage‐grouse we used a two‐stage approach. First, we developed models for each individual (i.e. individual‐level modeling) and evaluated individual‐level responses to modified habitats and infrastructure. Second, we averaged individual‐level estimates using a bootstrap approach to make population‐level inference. The average home range size during brood‐rearing in our study, from nest hatch to six weeks, was 0.85 ± 0.21 km2. Individual and population‐level results indicated that brood‐rearing females consistently selected for natural vegetation and avoided disturbed surfaces at a fine spatial scale. Our study area included substantial areas of recent (≤ 10 years) habitat reclamation which females also avoided. Visible power lines consistently led to avoidance behavior. In addition to consistent patterns of habitat selection, our individual models demonstrated variability and contrasting behaviors in how brood‐rearing females responded to specific infrastructure features and anthropogenic water bodies. At the population‐level anthropogenic water bodies were avoided but at the individual‐level the intensity of avoidance was variable among individuals. Individual variability was often explained by the age of the brood‐rearing female (first year or adult). First year females were more likely than adults to use habitats close to infrastructure and consistently established home ranges in areas with more surface disturbance and infrastructure when compared to adults. Our results provide new insights into fine‐scale habitat‐selection strategies used by female sage‐grouse with broods in an area where oil and gas infrastructure is widespread and cannot be avoided.

Achievable load shifting potentials for the European residential sector from 2022–2050

This paper develops quantitative predictions for residential electricity demand and the potential for load shifting for Europe at a fine spatial scale for years 2022–2050. To date only theoretical and spatially coarse measurements of this potential exist for a subset of technologies used in the residential sector. As the EU moves towards an ‘Energy Union’, it will be increasingly necessary to consider the whole integrated energy system across the EU and thereby databases with large spatial coverage are needed. To fill this need, we generate hourly load profiles for the residential sector for key electricity consuming devices that take into account the most recent empirical studies of usage patterns, and of the cost and willingness of households to participate in load shifting programs. Additionally, we extend the list of investigated technologies by the important classes of electric mobility and heat pumps for electric space heating. Predictions and insights provided in our article serve multiple use cases, i. they can be used in energy system models to assess the value of load shifting for the future energy system including system costs, ii. they enable infrastructure planners and grid operators to calibrate their expectations of the future role of load shifting in the electricity systems and iii. enable policy makers and to include load shifting in their considerations of future market designs.

Shedding light on dark taxa in sky‐island Appalachian leaf litter: Assessing patterns of endemicity using large‐scale, voucher‐based barcoding

Abstract Developing systematic conservation plans depends on a wealth of information on a region's biodiversity. For ‘dark taxa' such as arthropods, such data are usually very incomplete and in most cases left out from assessments. Sky islands are important and often fragile biodiversity hotspots. Southern Appalachian high‐elevation spruce–fir forests represent a particularly threatened sky‐island ecosystem, hosting numerous endemic and threatened species, but their arthropods remain understudied. Here we use voucher‐based megabarcoding to explore genetic differentiation among leaf‐litter arthropod communities of these highlands, and to examine the extent to which they represent dispersed communities of more or less coherent species, manageable as a distributed unit. We assembled a dataset comprising more than 6000 COI sequences representing diverse arthropod groups to assess species richness and sharing across peaks and ranges. Comparisons were standardised across taxa using automated species delimitation, measuring endemism levels by putative species. Species richness was high, with sites hosting from 86 to 199 litter arthropod species (not including mites or myriapods). Community profiles suggest that around one fourth of these species are unique to single sky islands and more than one third of all species are limited to a particular range. Across major taxa, endemicity was lowest in Araneae, and highest in neglected groups like Isopoda, Pseudoscorpionida, Protura and Diplura. Southern Appalachian sky islands of spruce–fir habitat host significantly distinct leaf‐litter arthropod communities, with high levels of local endemicity. This is the first work to provide such a clear picture of peak and range uniqueness for a taxonomically broad sample. Ensuring the protection of a sizeable fraction of high‐elevation litter species richness will therefore require attention at a relatively fine spatial scale.

Proportion of charcoal carbon in the forest floor carbon pool in relation to fire history in a boreal forest landscape

AbstractFire in the boreal forests emits substantial amounts of organically bound carbon (C) to the atmosphere and converts a fraction of the burnt organic matter into charcoal, which in turn is highly refractory and functions as a long‐term stable C pool. It is well established that the boreal forest charcoal pool is sufficiently large to play a significant role in the global C cycle. However, there is a need for spatially representative estimates of how large proportions of the forest floor C pool are made up of charcoal across different plant communities in the boreal forest ecosystem. Thus, we have quantified the amounts of C separately in charcoal and the organic layers of the forest floor across fine spatial scales in a boreal forest landscape with a well‐documented fire history. We found that the proportion of charcoal C made up an average of 1.2% of the total forest floor C, and the charcoal proportions showed a high small‐scale spatial variability and were concentrated in the organic–mineral soil interface. Proportions of charcoal C decreased with increasing time since last fire. Deeper soils, denser soils, and local concave areas had the highest proportions of charcoal C, whereas historical fire frequencies and current differences in vegetation did not relate to the proportions of charcoal C.

Working and residential segregation of migrants in Longgang City, China: A mobile phone data-based analysis

Despite being a long-established research topic, most urban segregation studies have neither explored the socio-spatial disparities beyond the context of housing, nor systematically examined both residential and non-residential segregation patterns at a very fine spatial scale. Recently, mobile phone data has been increasingly used as a data source to investigate experiences of segregation. While being celebrated for its high spatiotemporal resolution, mobile phone data analysis is often limited by the lack of users' socioeconomic information. To fill these gaps and by taking a small Chinese county city, Longgang, as the case of study, we measure migrants' residential and working segregation experiences through mobile phone data at a 100-metre scale. Results showed that both working and residential populations show a high level of segregation using the population diversity and location quotient indicators, but the segregation experienced by working population is less than that by residential population. Migrants are highly segregated with local citizens, but less segregated in some areas like the industrial zones surrounding the traditional downtown area. This study helps us better understand migrants' working and residential segregation of migrants in subordinate Chinese cities, and inform the planning and policy-making process to promote urban integration.

Temperature variation generates interspecific synchrony but spatial asynchrony in survival for freshwater fish communities.

Identifying environmental drivers of demographic variation is key to predicting community-level impacts in response to global change. Climate conditions can synchronize population trends and can occur both spatially for populations of the same species, and across multiple species within the same local community. The aim of this study was to investigate patterns of temporal variation in survival for freshwater fish communities in two geographically close but isolated sites and to understand the amount of variation accounted for by abiotic covariates including metrics of water temperature and stream flow. Using mark-recapture data, we estimated bi-monthly apparent survival in a Bayesian Cormack-Jolly-Seber framework. The model included random effects to quantify temporal variance to understand species synchrony with the rest of the fish community and between sites. Study species included bluehead chub (Nocomis leptocephalus), creek chub (Semotilus atromaculatus), and striped jumprock (Moxostoma rupiscartes) in the southeastern USA. Results showed that survival varied over time and periods of low survival were associated with higher mean water temperature. However, temporal patterns of survival differed among species and between sites, where survival was synchronous among species within a site but asynchronous between sites for the same species despite their spatial proximity. Study streams differed in summer thermal regimes, which resulted in contrasting summer survival patterns, suggesting sensitivity of these fishes to warming. We found that interspecific synchrony was greater than spatial synchrony, where regional drivers such as temperature may interact with local habitat leading to differences in survival patterns at fine spatial scales. Finally, these findings show that changes in the timing and magnitude of environmental conditions can be critical in limiting vital rates and that some populations may be more resilient to climate variation than others.

Multi‐Year Tracing of Spatial and Temporal Dynamics of Post‐Fire Aeolian Sediment Transport Using Rare Earth Elements Provide Insights Into Grassland Management

AbstractAeolian sediment transport occurs as a function of, and with feedback to ecosystem changes and disturbances. Many desert grasslands are undergoing rapid changes in vegetation, including the encroachment of woody plants, which alters fire regimes and in turn can change the spatial and temporal patterns of aeolian sediment transport. We investigated aeolian sediment transport and spatial distribution of sediment in the surface soil for 7 years following a prescribed fire using a multiple rare earth element (REE) tracer‐based approach in a shrub‐encroached desert grassland in the northern Chihuahuan desert. Results indicate that even though the aeolian horizontal sediment mass flux increased approximately three‐fold in the first windy season in the burned areas compared to control areas, there were no significant differences after three windy seasons. The soil surface of bare microsites was the major contributor of aeolian sediments in unburned areas (87%), while the shrub microsites contributed the least (&lt;2%) during the observation period. However, after the prescribed fire, the contribution of aeolian sediments from shrub microsites increased considerably (∼40%), indicating post‐fire microsite‐scale sediment redistribution. The findings of this study, which is the first to use multiple REE tracers for multi‐year analysis of the spatial and temporal dynamics of aeolian sediment transport, illustrate how disturbance by prescribed fire can influence aeolian processes and alters dryland soil geomorphology in which distinct soils develop over time at very fine spatial scales of individual plants.

Projecting high resolution population distribution using Local Climate Zones and multi-source big data

High precision and easily updatable data on population estimates are critical for urban planning, disaster risk assessment, and public health campaigns. However, traditional administrative census data often faces limitations in spatial resolution, making it difficult for further applications. In this study, we construct a fast and efficient population estimation model using Local Climate Zones (LCZ) products at a fine spatial scale. The population totals are estimated by LCZ units, based on a robust linkage between built LCZ types and population density. Validated by city- and county-level census data in 21 cities in China, the new model exhibits a very good fit with R2 values of 0.77 and 0.73 respectively, which confirms the effectiveness of LCZ-based population estimates. To our knowledge, this is the first study that directly estimates the population via LCZ maps. In addition, it was proven that population density can be used as a new property for LCZ type definition. As LCZ products are convenient to obtain, this work provides a simple, economical, and reliable population estimation method that can complement the traditional census.

Spatiotemporal variation in the size structure of the chub mackerel, Scomber japonicus, over half a century in the East Asian Marginal Seas

The size and age structure of a population are crucial aspects for understanding population dynamics. Many marine fish species have experienced long-term reductions in size and age owing to overfishing and/or global warming; however, these aspects remain unexplored. The present study elucidated the spatiotemporal variations in chub mackerel size and age structure based on the operational reports of large- and medium-sized Japanese purse seine fisheries for nearly half a century. Our results revealed size- and age-dependent segregation at a fine spatial scale, which may indicate ontogenetic niche shifts in response to thermal and feeding conditions or intercohort competition. On a long-term temporal scale, a significant reduction in size and age was observed in the East China Sea, whereas a contrasting increase in size and age was observed in the northern Sea of Japan. This may indicate that large fish have greater sensitivity to global warming and shift their distribution more rapidly northward than do small fish. In addition, the reduction in size and age during the spawning season in the East China Sea may result in a bias towards younger spawners, potentially rendering population dynamics more unstable. This study highlights the importance of investigating the spatiotemporal variation in the size structure of exploited fish stocks.

Evapotranspiration and groundwater inputs control the timing of diel cycling of stream drying during low-flow periods

Geologic, geomorphic, and climatic factors have been hypothesized to influence where streams dry, but hydrologists struggle to explain the temporal drivers of drying. Few hydrologists have isolated the role that vegetation plays in controlling the timing and location of stream drying in headwater streams. We present a distributed, fine-scale water balance through the seasonal recession and onset of stream drying by combining spatiotemporal observations and modeling of flow presence/absence, evapotranspiration, and groundwater inputs. Surface flow presence/absence was collected at fine spatial (~80 m) and temporal (15-min) scales at 25 locations in a headwater stream in southwestern Idaho, USA. Evapotranspiration losses were modeled at the same locations using the Simultaneous Heat and Water (SHAW) model. Groundwater inputs were estimated at four of the locations using a mixing model approach. In addition, we compared high-frequency, fine-resolution riparian normalized vegetation difference index (NDVI) with stream flow status. We found that the stream wetted and dried on a daily basis before seasonally drying, and daily drying occurred when evapotranspiration outputs exceeded groundwater inputs, typically during the hours of peak evapotranspiration. Riparian NDVI decreased when the stream dried, with a ~2-week lag between stream drying and response. Stream diel drying cycles reflect the groundwater and evapotranspiration balance, and riparian NDVI may improve stream drying predictions for groundwater-supported headwater streams.

Single field-of-view sounder atmospheric product retrieval algorithm: establishing radiometric consistency for hyper-spectral sounder retrievals

Abstract. The single field-of-view (SFOV) sounder atmospheric product (SiFSAP) retrieval algorithm has been developed to address the need to retrieve high-spatial-resolution atmospheric data products from hyper-spectral sounders and ensure the radiometric consistency between the retrieved properties and measured spectral radiances. It is based on an integrated optimal-estimation inversion scheme that processes data from the satellite-based synergistic microwave (MW) and infrared (IR) spectral measurements from advanced sounders. The retrieval system utilizes the principal component radiative transfer model (PCRTM), which performs radiative transfer calculations monochromatically and includes accurate cloud-scattering simulations. SiFSAP includes temperature, water vapor, surface skin temperature and emissivity, cloud height and microphysical properties, and concentrations of essential trace gases for each SFOV at a native instrument spatial resolution. Error estimations are provided based on a rigorous analysis for uncertainty propagation from the top-of-atmosphere (TOA) spectral radiances to the retrieved geophysical properties. As a comparison, the spatial resolution for the traditional hyper-spectral sounder retrieval products is much coarser than the native resolution of the instruments due to the common use of the “cloud-clearing” technique to compensate for the lack of cloud-scattering simulation in the forward model. The degraded spatial resolution in traditional cloud-clearing sounder retrieval products limits their applications for capturing meteorological or climate signals at finer spatial scales. Moreover, a rigorous uncertainty propagation estimation needed for long-term climate trend studies cannot be given due to the lack of direct radiative transfer relationships between the observed TOA radiances and the retrieved geophysical properties. With the advantages of the higher spatial resolution; the simultaneous retrieval of atmospheric, cloud, and surface properties using all available spectral information; and the establishment of “radiance closure” in the sounder spectral measurements, the SiFSAP provides additional information needed for various weather and climate studies and applications using sounding observations. This paper gives an overview of the SiFSAP retrieval algorithm and assessment of SiFSAP atmospheric temperature, water vapor, clouds, and surface products derived from the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) data.

Habitat visibility affects the behavioral response of a large herbivore to human disturbance in forest landscapes

Wildlife can perceive humans as predators and human disturbance, whether lethal (e.g., hunting) or non-lethal (e.g., hiking, biking, and skiing), triggers antipredator behavior among prey. Visibility is the property that relates habitat structure to accessibility of visual information that allows animals to detect predators and evaluate predation risk. Thus, the visibility of a habitat (hereafter referred to as habitat visibility) for prey species alters the perceived risk of predation and therefore has a strong influence on their antipredator behavior. Yet, knowledge of how habitat visibility affects the response of animals to different types of human disturbance is limited, partly, because it is challenging to measure habitat visibility for animals at a fine spatial scale over a landscape, particularly in highly heterogeneous landscapes (e.g., forests). In this study, we employed a newly described approach that combines terrestrial and airborne LiDAR to contiguously measure fine-scale habitat visibility in a forested landscape. We applied the LiDAR-derived habitat visibility to examine how habitat visibility in forests affects the summer space use of 20 GPS-collared female red deer (Cervus elaphus) modeled with integrated step-selection functions in the Bavarian Forest National Park, Germany when exposed to various types of human disturbance including recreational activities, forest roads, hiking trails, and hunting. We found that red deer in our study area avoided areas with higher all types of human disturbance, especially during daylight hours. Furthermore, habitat visibility significantly modified the use of space by red deer in response to human recreational activities, forest roads, and hiking trails, but not to the hunting area. Red deer tended to tolerate a higher intensity of human recreational activities and to use areas closer to forest roads or hiking trails when they have lower habitat visibility (i.e., more cover). Our findings highlight the importance of considering visual perception when studying the response of wild animals to human disturbance. We emphasize the potential to mitigate negative consequences of human disturbance on wildlife, through measures such as maintaining vegetative buffers around recreational infrastructure (e.g., roads and skiing tracks) in order to reduce habitat visibility around areas frequented by humans.

Multi-scale habitat selection throughout the annual cycle of a long-distance avian migrant

Long-distance migrants are constrained by widely separated hospitable habitats in geographically isolated locations, making them vulnerable to environmental change, both through natural and anthropogenic causes. Knowledge about their resource selection decisions is imperative to understand the drivers of their declines. The distinct periods within an annual cycle, when individuals experience different environmental circumstances, are inextricably linked through carry-over effects which can have important consequences for the individual, and consequently the population. In this study, we employ precise archival GPS-tracking data of European Nightjars (Caprimulgus europaeus) and high-resolution global land cover data to examine habitat selection during the sedentary wintering and breeding periods, as well as during autumn and spring migration, using a correlational approach. We demonstrate how nightjars use general habitat characteristics, such as landscape diversity, for high-order habitat selection, while resource selection at a finer spatial scale is reliant on fine-scale variables related to a habitat’s suitability, such as surface area of grassland and shrubland. We show that nightjars favour spatially diverse landscapes, which allows them to minimize time spent searching for optimal habitats. The considerable variation in the drivers of habitat selection between and within seasons shows how anthropogenic land-use change can have an array of different impacts on migrants by influencing large- and fine-scale habitat selection. This study shows the advantages of an individual based GPS-tracking approach, combined with high spatial resolution remote sensing data, and highlights the need for full annual-cycle research on scale dependent habitat selection of long-distance avian migrants.