Towards more predictable and consistent landscape metrics across spatial scales

Habitat change and fragmentation are considered key drivers of environmental change and biodiversity loss. To understand and mitigate the effects of such spatial disturbances on biological systems, it is critical to quantify changes in landscape pattern. However, the characterization of spatial patterns remains complicated in part because most widely used landscape metrics vary with the amount of usable habitat available in the landscape, and vary with the scale of the spatial data used to calculate them. In this study, we investigate the nature of the relationship between intrinsic characteristics of spatial pattern and extrinsic scale-dependent factors that affect the characterization of landscape patterns. To do so, we used techniques from modern multivariate statistics to disentangle widely used landscape metrics with respect to four landscape components: extent (E), resolution (R), percentage of suitable habitat cover (P), and spatial autocorrelation level (H). Our results highlight those metrics that are less sensitive to change in spatial scale and those that are less correlated. We found, however, significant and complex interactions between intrinsic and extrinsic characteristics of landscape patterns that will always complicate researcher's ability to isolate purely landscape pattern driven effects from the effects of changing spatial scale. As such, our study illustrates the need for a more systematic investigation of the relationship between intrinsic characteristics and extrinsic properties to accurately characterize observed landscape patterns.     

Identifying the spatial effects and driving factors of urban PM2.5 pollution in China

Exploring exactly where air pollution comes from, and identifying the key factors that influence it, can provide a scientific basis for the rational formulation and effective implementation of air pollution policies in China. Based on the data from 2001 to 2012 covering PM_2.5 concentrations in 285 Chinese cities, we use dynamic spatial panel models to empirically analyze the key driving factors of this air pollution. Results show that China’s urban smog demonstrates both obvious global spatial autocorrelation and local spatial agglomeration. There is a significant inverted “U-shaped” curve between economic development level and air pollution, and most cities are in the phase in which pollution is increasing in conjunction with improvements to the economy. Due to a rapid increase in population in built up areas, a high-proportion of secondary industry, a coal-dominated energy structure and increasing traffic intensity, China’s smog problem is becoming more and more serious. FDI probably will not play a future role in mitigating the air pollution. Central heating in winter in northern China further aggravates local smog to a certain extent. Because China’s haze pollution presents path-dependent characteristics and spatial spillover effects in the time dimension and in the space dimension respectively, so smog alleviation policies should be implemented based both on the strategies of maximizing effort and regional joint prevention and control.     

Climate and landscape influence on indicators of lake carbon cycling through spatial patterns in dissolved organic carbon

Freshwater ecosystems are strongly influenced by both climate and the surrounding landscape, yet the specific pathways connecting climatic and landscape drivers to the functioning of lake ecosystems are poorly understood. Here, we hypothesize that the links that exist between spatial patterns in climate and landscape properties and the spatial variation in lake carbon (C) cycling at regional scales are at least partly mediated by the movement of terrestrial dissolved organic carbon (DOC) in the aquatic component of the landscape. We assembled a set of indicators of lake C cycling (bacterial respiration and production, chlorophyll a, production to respiration ratio, and partial pressure of CO₂), DOC concentration and composition, and landscape and climate characteristics for 239 temperate and boreal lakes spanning large environmental and geographic gradients across seven regions. There were various degrees of spatial structure in climate and landscape features that were coherent with the regionally structured patterns observed in lake DOC and indicators of C cycling. These different regions aligned well, albeit nonlinearly along a mean annual temperature gradient; whereas there was a considerable statistical effect of climate and landscape properties on lake C cycling, the direct effect was small and the overall effect was almost entirely overlapping with that of DOC concentration and composition. Our results suggest that key climatic and landscape signals are conveyed to lakes in part via the movement of terrestrial DOC to lakes and that DOC acts both as a driver of lake C cycling and as a proxy for other external signals.     

Aridity indices predict organic matter decomposition and comminution processes at landscape scale

Microbial decomposition and invertebrate comminution of a particular organic substrate are largely regulated by temperature and water availability. Numerous metrics have been used to model decay processes at large regional-to-global scales. However, their use at smaller landscape scales might not be practical or feasible. Aridity, generally defined as the balance between long term annual precipitation (P) and potential evapotranspiration (PET), is a metric that synthesizes the major climatic drivers regulating ecosystem processes including the activity of microbes and invertebrates on the forest floor. Thus, aridity indices (AIs) can theoretically represent suitable predictors of decomposition and comminution processes at landscape scale.We investigated our hypothesis in a sclerophyll forest in south-east Australia, where decomposition and comminution rates of Eucalyptus globulus leaf litter were measured in eight sites positioned along an aridity gradient caused by variable exposure to solar radiation. Four sites were also instrumented to continuously monitor air, litter and soil microclimatic variables.We found that AIs were strongly related to above- and below-ground microbial decomposition rates, as well as above-ground comminution rates. Some microclimatic variables, such as shortwave radiation, air relative humidity and litter temperature were also significantly related to above-ground processes, but not below-ground decomposition. Among the AIs tested, the index calculated using the Priestley-Taylor equation for PET had consistently higher coefficients of determination with decomposition and comminution rates. Our case study suggests that AIs can represent robust predictors of both decomposition and comminution processes at landscape scale and useful surrogates for more expensive microclimatic predictors collected at site level. AIs could also be used across spatial scales (from local to continental) to improve biogeochemical and hydrological models by incorporating a spatially-explicit representation of decay processes.     

An approach to analyzing environmental drivers to shape spatial variations in body-size structure of biofilm-dwelling protozoa during an annual cycle in marine ecosystems

As an inherent trait, body-size structure has been used to summarize functional features of a community instead of taxonomic resolutions due to the high redundancy for bioassessment. In this study, the multivariate approaches were used to determine the environmental drivers to the spatial variation in body-size structure based on an annual dataset of biofilm-dwelling protozoa. Samples were monthly collected at four stations within a gradient of pollution in coastal waters of the Yellow Sea, northern China during a 1-year cycle. The second-stage (2STAGE) clustering and ordination analyses demonstrated that the annual patterns were significantly different among four sampling stations. Mantel analysis showed the spatial variations in body-size structures of the protozoa were significantly correlated with the water quality status along the pollution gradient. Best matching analysis revealed that the potential environmental drivers to shape the spatial difference in body-size structure may be pH, chemical oxygen demand (COD) and nutrients (e.g., soluble phosphates, ammonia and nitrates). It is suggested that the multivariate approaches used may determine the environmental drivers to shape the spatial variations in body-size structure of biofilm-dwelling protozoa in marine ecosystems.     

扎龙自然保护区不同空间尺度景观格局时空变化及其生态风险

景观格局变化下的区域生态风险研究可以为保护区域景观生态系统健康,实现区域资源的合理和可持续利用提供重要科学依据。以扎龙自然保护区为研究区,利用遥感手段提取景观类型信息,定量分析不同空间尺度下研究区景观格局时空变化特征和演变规律。根据研究区景观格局变化特点构建景观生态风险指数,对研究区不同时期空间尺度变化过程中景观生态风险时空变化特征进行分析。研究表明:1995—2010年研究区不同空间尺度下景观总体格局和各景观类型格局变化显著,呈现不同变化特征;景观生态风险随空间粒度的增大而减小。对比分析两期数据,研究区景观生态风险加剧,其空间分布均呈环形扩散特征。根据本研究区区域面积大小和景观格局复杂程度,确定了景观格局和景观生态风险研究的适宜尺度域。     

Climate alters the movement ecology of a non‐migratory bird

Global climate change is causing increased climate extremes threatening biodiversity and altering ecosystems. Climate is comprised of many variables including air temperature, barometric pressure, solar radiation, wind, relative humidity, and precipitation that interact with each other. As movement connects various aspects of an animal''s life, understanding how climate influences movement at a fine‐temporal scale will be critical to the long‐term conservation of species impacted by climate change. The sedentary nature of non‐migratory species could increase some species risk of extirpation caused by climate change. We used Northern Bobwhite (Colinus virginianus; hereafter bobwhite) as a model to better understand the relationship between climate and the movement ecology of a non‐migratory species at a fine‐temporal scale. We collected movement data on bobwhite from across western Oklahoma during 2019–2020 and paired these data with meteorological data. We analyzed movement in three different ways (probability of movement, hourly distance moved, and sinuosity) using two calculated movement metrics: hourly movement (displacement between two consecutive fixes an hour apart) and sinuosity (a form of tortuosity that determines the amount of curvature of a random search path). We used generalized linear‐mixed models to analyze probability of movement and hourly distance moved, and used linear‐mixed models to analyze sinuosity. The interaction between air temperature and solar radiation affected probability of movement and hourly distance moved. Bobwhite movement increased as air temperature increased beyond 10°C during low solar radiation. During medium and high solar radiation, bobwhite moved farther as air temperature increased until 25–30°C when hourly distance moved plateaued. Bobwhite sinuosity increased as solar radiation increased. Our results show that specific climate variables alter the fine‐scale movement of a non‐migratory species. Understanding the link between climate and movement is important to determining how climate change may impact a species’ space use and fitness now and in the future.     

Metrics for macroscale invasion and dispersal patterns

Aim The ability to quantitatively measure the continuum of macroscale patterns of species invasion is a first step toward deeper understanding of their causal factors. We took advantage of two centuries worth of herbarium data, to evaluate a set of metrics to measure macroscale patterns, allowing cross-species comparisons of invasive expansion across large geographic areas.Methods We used herbarium specimens to reconstruct county-level invasion histories for two non-native plants (Alliaria petiolata and Lonicera japonica), with distinct spatiotemporal distribution patterns over the past two centuries. Using county centroids from species' initial occurrences, we quantified point pattern metrics from multiple disciplines (e.g. urban crime analysis, landscape ecology etc.) that are historically used at smaller spatial scales, to evaluate their ability to detect macroscale spatial diffusion and amount of directional expansion. Metrics were further assessed for their ease of use, data requirements, independence from other metrics and intuitiveness of interpretation.Important findings We identified four suitable metrics for distinguishing differences in spatial patterns: (i) standard distance, (ii) number of patches, (iii) Euclidean nearest neighbor summary class statistic coefficient of variation and (iv) mean center that when applied to county-level presence data allowed us to determine the directions by which distributions expanded and if distributions increased via outward expansion, infilling and/or jump dispersal events. These metrics when compared during the same invasion phase are capable of quantifying macroscale variability among species in their distributional and dispersal patterns. Being able to quantify differences among species in these patterns is important in understanding the drivers of species dispersal patterns. These metrics therefore represent a simple yet thorough toolset for achieving this goal.     

动物移动网络研究对景观生态学的贡献

景观生态学在其发展之初的20世纪80年代, 提出了关于景观网络研究(包括景观网络概念、网络结构指数和景观连接度)的基本构想, 这些构想需要在景观过程的研究中逐渐被落实和发展。动物移动过程因动物在斑块或廊道上有着独特丰富的属性特征、与周围资源环境之间存在复杂反馈而区别于无机物运移的景观过程, 则动物移动网络研究在实现关于景观网络研究的基本构想、推动景观生态学发展中贡献独特。因此, 总结动物移动网络研究的来源脉络及其对景观生态学的理论贡献对于景观网络领域和景观生态学学科的发展都具有重要意义。本文抓住景观生态学发展之初提出的关于景观网络研究的基本构想, 寻找和剖析其中所蕴含的景观生态学思想, 追踪这些思想如何被落实、发展、并形成目前的三个热点方向: 动物移动网络模拟、重要值评价和景观连接度分析; 总结这三个方向的研究进展, 指出整合动物的空间行为特征是必然发展趋势; 揭示出动物移动网络研究始终都以发掘斑块或廊道的动物有机体的属性特征(如种群数量)、以及描述这种属性在不同斑块或廊道之间的差异和联系为方向, 正是这种属性的发掘有效地落实、发展和丰富了关于景观网络研究的最初构想, 对景观生态学的贡献比其他过程更为独特。文章还总结了我国动物移动网络研究与国际研究相比较为滞后的现状, 指出其暂时尚未显示出对我国景观生态学的独特贡献; 强调发展源于跟踪定位数据的动物空间行为生态学研究是减小差距的重要、必要前提。期望本文能引发关于景观网络乃至景观生态学理论发展的方向性思考, 为研究者提供参考。     

Evaluating the effectiveness of landscape metrics in quantifying spatial patterns

The effectiveness of landscape metrics in quantifying spatial patterns is fundamental to metrics assessment. Setting 36 simulated landscapes as sample space and focusing on 23 widely used landscape metrics, their effectiveness in quantifying the complexity of such spatial pattern components as number of patch types, area ratio of patch types and patch aggregation level, were analyzed with the application of the multivariate linear regression analysis method. The results showed that all the metrics were effective in quantifying a certain component of spatial patterns, and proved that what the metrics quantified were not a single component but the complexity of several components of spatial patterns. The study also showed a distinct inconsistency between the performances of landscape metrics in simulated landscapes and the real urban landscape of Shenzhen, China. It was suggested that the inconsistency resulted from the difference of the correlation among spatial pattern components between simulated and real landscapes. After considering the very difference, the changes of all 23 landscape metrics against changing of number of patch types in simulated landscapes were consistent with those in the real landscape. The phenomenon was deduced as the sign effect of spatial pattern components on landscape metrics, which was of great significance to the proper use of landscape metrics.     

Fine‐scale spatiotemporal dynamics of fungal fruiting: prevalence,amplitude, range and continuity

Despite the critical importance of fungi as symbionts with plants, resources for animals, and drivers of ecosystem function, the spatiotemporal distributions of fungi remain poorly understood. The belowground life cycle of fungi makes it difficult to assess spatial patterns and dynamic processes even with recent molecular techniques. Here we offer an explicit spatiotemporal Bayesian inference of the drivers behind spatial distributions from investigation of a Swiss inventory of fungal fruit bodies. The unique inventory includes three temperate forest sites in which a total of 73 952 fungal fruit bodies were recorded systematically in a spatially explicit design between 1992 and 2006. Our motivation is to understand how broad‐scale climate factors may influence spatiotemporal dynamics of fungal fruiting within forests, and if any such effects vary between two functional groups, ectomycorrhizal (ECM) and saprotrophic fungi. For both groups we asked: 1) how consistent are the locations of fruiting patches, the sizes of patches, the quantities of fruit bodies, and of prevalence (occupancy)? 2) Do the annual spatial characteristics of fungal fruiting change systematically over time? 3) Are spatial characteristics of fungal fruiting driven by climatic variation? We found high inter‐annual continuity in fruiting for both functional groups. The saprotrophic species were characterised by small patches with variable fruit body counts. In contrast, ECM species were present in larger, but more distinctly delimited patches. The spatial characteristics of the fungal community were only indirectly influenced by climate. However, climate variability influenced overall yields and prevalence, which again links to spatial structure of fruit bodies. Both yield and prevalence were correlated with the amplitudes of occurrence and of fruit body counts, but only prevalence influenced the spatial range. Summarizing, climatic variability affects forest‐stand fungal distributions via its influence on yield (amount) and prevalence (occupancy), whereas fungal life‐history strategies dictate fine‐scale spatial characteristics.     

Dynamic graph convolution neural network based on spatial-temporal correlation for air quality prediction

Air pollution is a serious threat to both the ecological environment and the physical health of individuals. Therefore, accurate air quality prediction is urgent and necessary for pollution mitigation and residents’ travel. However, few existing models are established based on the dynamic spatiotemporal correlation of air pollutants to predict air quality. In this paper, a novel deep learning model combining the dynamic graph convolutional network and the multi-channel temporal convolutional network (DGC-MTCN) is proposed for air quality prediction. To efficiently represent the time-varying spatial dependencies, a new spatiotemporal dynamic correlation calculation method based on gray relation analysis is proposed to construct dynamic adjacency matrices. Then, the spatiotemporal features are sufficiently extracted by the graph convolutional network and the multi-channel temporal convolutional network. Two real-world air quality datasets collected from Beijing and Fushun are applied to verify the performance of our proposed model. The experimental results show that compared with other baselines, the DGC-MTCN model has excellent prediction accuracy. Especially for the prediction of multi-step and different stations, our model performs better temporal stability and generalization ability.     

Stochastic Modelling of Air Pollution Impacts on Respiratory Infection Risk

The impact of air pollution on people’s health and daily activities in China has recently aroused much attention. By using stochastic differential equations, variation in a 6 year long time series of air quality index (AQI) data, gathered from air quality monitoring sites in Xi’an from 15 November 2010 to 14 November 2016 was studied. Every year the extent of air pollution shifts from being serious to not so serious due to alterations in heat production systems. The distribution of such changes can be predicted by a Bayesian approach and the Gibbs sampler algorithm. The intervals between changes in a sequence indicate when the air pollution becomes increasingly serious. Also, the inflow rate of pollutants during the main pollution periods each year has an increasing trend. This study used a stochastic SEIS model associated with the AQI to explore the impact of air pollution on respiratory infections. Good fits to both the AQI data and the numbers of influenza-like illness cases were obtained by stochastic numerical simulation of the model. Based on the model’s dynamics, the AQI time series and the daily number of respiratory infection cases under various government intervention measures and human protection strategies were forecasted. The AQI data in the last 15 months verified that government interventions on vehicles are effective in controlling air pollution, thus providing numerical support for policy formulation to address the haze crisis.     

Landscape heterogeneity metrics as indicators of bird diversity: Determining the optimal spatial scales in different landscapes

Species distribution models are often used to study the biodiversity of ecosystems. The modelling process uses a number of parameters to predict others, such as the occurrence of determinate species, population size, habitat suitability or biodiversity. It is well known that the heterogeneity of landscapes can lead to changes in species’ abundance and biodiversity. However, landscape metrics depend on maps and spatial scales when it comes to undertaking a GIS analysis.We explored the goodness of fit of several models using the metrics of landscape heterogeneity and altitude as predictors of bird diversity in different landscapes and spatial scales. Two variables were used to describe biodiversity: bird richness and trophic level diversity, both of which were obtained from a breeding bird survey by means of point counts. The relationships between biodiversity and landscape metrics were compared using multiple linear regressions. All of the analyses were repeated for 14 different spatial scales and for cultivated, forest and grassland environments to determine the optimal spatial scale for each landscape typology.Our results revealed that the relationships between species’ richness and landscape heterogeneity using 1:10,000 land cover maps were strongest when working on a spatial scale up to a radius of 125–250 m around the sampled point (circa 4.9–19.6 ha). Furthermore, the correlation between measures of landscape heterogeneity and bird diversity was greater in grasslands than in cultivated or forested areas. The multi-spatial scale approach is useful for (a) assessing the accuracy of surrogates of bird diversity in different landscapes and (b) optimizing spatial model procedures for biodiversity mapping, mainly over extensive areas.     

景观生态学在大气污染研究中的应用

快速城市化和工业化进程造成了一系列大气污染问题,亟需在宏观尺度上解析大气污染时空分布规律。景观生态学关注格局与过程耦合,景观"源汇"理论可对应解析大气污染物的源与汇效应,将景观生态学的理论与方法引入大气污染研究中已成为解决当前区域发展与大气污染权衡的有效途径。从景观生态学视角辨识了景观与大气污染物的源汇关系,系统梳理了景观格局与大气污染的定量关系,指出当前景观格局指标仍需进一步完善以表征大气污染时空分布特征,而高质量大气污染物时空数据的缺乏是限制景观格局与大气污染过程耦合分析的重要因素,拓展应用景观"源汇"理论,定量解析景观格局对大气污染的源汇效应,同时进一步研发遥感反演技术,实现大气污染物分布格局的精细刻画,将为区域景观规划提供重要支撑。强化大气污染研究中的景观生态学分析途径,将有助于深化景观生态学格局与过程耦合研究体系,也将为景观可持续管理提供有力的科学支撑。     

Spatiotemporal remote sensing of ecosystem change and causation across Alaska

Contemporary climate change in Alaska has resulted in amplified rates of press and pulse disturbances that drive ecosystem change with significant consequences for socio‐environmental systems. Despite the vulnerability of Arctic and boreal landscapes to change, little has been done to characterize landscape change and associated drivers across northern high‐latitude ecosystems. Here we characterize the historical sensitivity of Alaska's ecosystems to environmental change and anthropogenic disturbances using expert knowledge, remote sensing data, and spatiotemporal analyses and modeling. Time‐series analysis of moderate—and high‐resolution imagery was used to characterize land‐ and water‐surface dynamics across Alaska. Some 430,000 interpretations of ecological and geomorphological change were made using historical air photos and satellite imagery, and corroborate land‐surface greening, browning, and wetness/moisture trend parameters derived from peak‐growing season Landsat imagery acquired from 1984 to 2015. The time series of change metrics, together with climatic data and maps of landscape characteristics, were incorporated into a modeling framework for mapping and understanding of drivers of change throughout Alaska. According to our analysis, approximately 13% (~174,000 ± 8700 km²) of Alaska has experienced directional change in the last 32 years (±95% confidence intervals). At the ecoregions level, substantial increases in remotely sensed vegetation productivity were most pronounced in western and northern foothills of Alaska, which is explained by vegetation growth associated with increasing air temperatures. Significant browning trends were largely the result of recent wildfires in interior Alaska, but browning trends are also driven by increases in evaporative demand and surface‐water gains that have predominately occurred over warming permafrost landscapes. Increased rates of photosynthetic activity are associated with stabilization and recovery processes following wildfire, timber harvesting, insect damage, thermokarst, glacial retreat, and lake infilling and drainage events. Our results fill a critical gap in the understanding of historical and potential future trajectories of change in northern high‐latitude regions.     

Natural disturbances are spatially diverse but temporally synchronized across temperate forest landscapes in Europe

Natural disturbance regimes are changing substantially in forests around the globe. However, large‐scale disturbance change is modulated by a considerable spatiotemporal variation within biomes. This variation remains incompletely understood particularly in the temperate forests of Europe, for which consistent large‐scale disturbance information is lacking. Here, our aim was to quantify the spatiotemporal patterns of forest disturbances across temperate forest landscapes in Europe using remote sensing data and determine their underlying drivers. Specifically, we tested two hypotheses: (1) Topography determines the spatial patterns of disturbance, and (2) climatic extremes synchronize natural disturbances across the biome. We used novel Landsat‐based maps of forest disturbances 1986–2016 in combination with landscape analysis to compare spatial disturbance patterns across five unmanaged forest landscapes with varying topographic complexity. Furthermore, we analyzed annual estimates of disturbances for synchronies and tested the influence of climatic extremes on temporal disturbance patterns. Spatial variation in disturbance patterns was substantial across temperate forest landscapes. With increasing topographic complexity, natural disturbance patches were smaller, more complex in shape, more dispersed, and affected a smaller portion of the landscape. Temporal disturbance patterns, however, were strongly synchronized across all landscapes, with three distinct waves of high disturbance activity between 1986 and 2016. All three waves followed years of pronounced drought and high peak wind speeds. Natural disturbances in temperate forest landscapes of Europe are thus spatially diverse but temporally synchronized. We conclude that the ecological effect of natural disturbances (i.e., whether they are homogenizing a landscape or increasing its heterogeneity) is strongly determined by the topographic template. Furthermore, as the strong biome‐wide synchronization of disturbances was closely linked to climatic extremes, large‐scale disturbance episodes are likely in Europe's temperate forests under climate changes.     

Models of Eucalypt phenology predict bat population flux

Fruit bats (Pteropodidae) have received increased attention after the recent emergence of notable viral pathogens of bat origin. Their vagility hinders data collection on abundance and distribution, which constrains modeling efforts and our understanding of bat ecology, viral dynamics, and spillover. We addressed this knowledge gap with models and data on the occurrence and abundance of nectarivorous fruit bat populations at 3 day roosts in southeast Queensland. We used environmental drivers of nectar production as predictors and explored relationships between bat abundance and virus spillover. Specifically, we developed several novel modeling tools motivated by complexities of fruit bat foraging ecology, including: (1) a dataset of spatial variables comprising Eucalypt‐focused vegetation indices, cumulative precipitation, and temperature anomaly; (2) an algorithm that associated bat population response with spatial covariates in a spatially and temporally relevant way given our current understanding of bat foraging behavior; and (3) a thorough statistical learning approach to finding optimal covariate combinations. We identified covariates that classify fruit bat occupancy at each of our three study roosts with 86–93% accuracy. Negative binomial models explained 43–53% of the variation in observed abundance across roosts. Our models suggest that spatiotemporal heterogeneity in Eucalypt‐based food resources could drive at least 50% of bat population behavior at the landscape scale. We found that 13 spillover events were observed within the foraging range of our study roosts, and they occurred during times when models predicted low population abundance. Our results suggest that, in southeast Queensland, spillover may not be driven by large aggregations of fruit bats attracted by nectar‐based resources, but rather by behavior of smaller resident subpopulations. Our models and data integrated remote sensing and statistical learning to make inferences on bat ecology and disease dynamics. This work provides a foundation for further studies on landscape‐scale population movement and spatiotemporal disease dynamics.     

Towards a unified framework for connectivity that disentangles movement and mortality in space and time

Predicting connectivity, or how landscapes alter movement, is essential for understanding the scope for species persistence with environmental change. Although it is well known that movement is risky, connectivity modelling often conflates behavioural responses to the matrix through which animals disperse with mortality risk. We derive new connectivity models using random walk theory, based on the concept of spatial absorbing Markov chains. These models decompose the role of matrix on movement behaviour and mortality risk, can incorporate species distribution to predict the amount of flow, and provide both short‐ and long‐term analytical solutions for multiple connectivity metrics. We validate the framework using data on movement of an insect herbivore in 15 experimental landscapes. Our results demonstrate that disentangling the roles of movement behaviour and mortality risk is fundamental to accurately interpreting landscape connectivity, and that spatial absorbing Markov chains provide a generalisable and powerful framework with which to do so.     

Bearding the scorpion in his den: desert isopods take risks to validate their ‘landscape of fear’ assessment

Animals balance the risk of predation against other vital needs by adjusting their spatial behavior to match spatiotemporal variation in predation risk. To map this ‘landscape of fear’, prey use evolutionary rules of thumbs that are associated with the activity and hunting efficiency of predators. In addition, prey acquire perceptual information about the presence, identity and state of potential predators and use these cues to focus their acute anti‐predatory responses. Our goal was to explore if and how prey also use such perceptual information that decays with time to update their spatiotemporal risk assessment. We placed scorpions in freshly dug burrows and recorded the spatial activity and defense behavior of their isopod prey upon encountering the burrows straight after settling the scorpions and seven days later. To corroborate our understanding, we also examined the isopods’ detailed reactions towards deserted scorpion burrows. The isopods reacted defensively to scorpion burrows during their first encounter. After seven days, proportionally more isopods approached the scorpion burrows on their way out for foraging and fewer isopods encountered it on their way back. No changes in the spatial activity were observed towards deserted burrows. In addition, on the eighth day, more isopods entered the risky area near the scorpion burrows when leaving their own burrow than on the first encounter. The results suggest that isopods used predator cues to readjust the ‘landscape of fear’. Yet, rather than avoiding the dangerous areas altogether, the isopods implemented risky inspection behavior, validating whether the danger is actual. Our findings imply that inspection behavior toward predators can be used for future planning of prey spatial activity, offsetting possible ‘information decay costs’.