Landscape Ecology

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Example of a J. virginiana encroachment around a tree planting and b an encroaching J. virginiana in the Sandhills, NE USA (satellite imagery is from Google Earth)
Map of studies sites a, b and transects used to examine spatial patterns of woody encroachment in the Sandhills ecoregion of the central Great Plains
Recruitment curve (black) characterizing the scatter of encroaching J. virginiana around a seed source. The inset plot shows the cumulative probability of recruitment as a function of distance from a seed source. Dashed lines mark the 90th and 95th percentile distances and denote a break between average vs. rare, long-distance recruitment distances
Percentile distances from tree plantings calculated from the distribution of encroaching J. virginiana around tree plantings over five subsequent time steps (represented by height classes: t1 ≥ 5 m; t2 ≥ 4 m; t3 ≥ 3 m; t4 ≥ 2 m; t5 ≥ 1 m). 98th and 99th percentile distances from tree plantings show changes in the encroachment distribution’s tail compared to the 50th, 75th, and 90th percentile distances representing high density encroachment
  • Dillon T. FogartyDillon T. Fogarty
  • Robert B. PetersonRobert B. Peterson
  • Dirac TwidwellDirac Twidwell
Context Woody encroachment is the process whereby grasslands transition to a woody-dominated state. This process is a global driver of grassland decline and is ultimately the outcome of increased woody plant recruitment in grasslands. Yet, little is known about how recruitment distances structure spatial patterns of encroachment. Objectives Here, we develop a recruitment curve to describe the scatter of woody plant recruitment around seed sources and examine how this structures spatial patterns of encroachment. Methods We developed a recruitment curve for Juniperus virginiana using an encroachment dataset that captures spread from tree plantings into treeless grassland sites in the Nebraska Sandhills (USA). In addition, we used height classes of encroaching J. virginiana as subsequent time steps of an encroachment process to examine how the leading edge of encroachment expanded over time. Results The recruitment curve was characterized by a fat-tailed distribution. Most recruitment occurred locally, within 157 m of seed sources (95th percentile distance), while, sparse long-distance recruitment characterized the curve’s tail. Expansion of the leading edge of encroachment was characterized by two features: (1) a slow moving, high density area near tree plantings and (2) rapid expansion of the distribution’s tail, driven by long-distance recruitment in treeless areas. Conclusion Our results show a high capacity for woody plant invasion of grasslands. Local recruitment drives transitions to woody dominance, while long-distance recruitment generates a rapidly advancing leading edge. Plans to conserve and restore grasslands will require spatially informed strategies that account for local and long-distance recruitment of woody plants.
 
Example of avoidance areas mapped (in red) as all locations on the landscape with a 3% or greater density of oil and gas pads and access roads (in black)
Study area and 14 × 14 km sample sites for mule deer energy expenditure model in the Book Cliffs region of Utah. Samples are symbolized by colored squares and were chosen to include areas in the first, second, and third quantiles of terrain ruggedness (TRI) and top and bottom 50% in the rate of oil and gas development (fast, slow)
Example of estimated mule deer energy expenditure (EE) to reach any point on the landscape starting from one of the grid points. Values range from 0 at the starting point to 1740 kilocalories in the northwest. As shown by the arrows, EE on the north side of avoidance areas has a greater cumulative cost than places unobstructed by oil and gas development. In this example, the unobstructed portion would take a deer 800 kcal to walk the 6.5 km distance, but a place at the same Euclidean distance made less accessible by development (I.e., forcing the deer to travel further) would require the deer use 1250 kcal, a 64% increase in EE
Violin and jitter plots of calculated median mule deer energy expenditure (EE) in kilocalories for each of 12 14 × 14 km study sites beginning in an undeveloped landscape in the Book Cliffs region of northeastern Utah and after the oil and gas development in the years 2001, 2006, 2011, and 2016. The width and height of the violins represent the density and range of predicted values respectively. Asterisks represent median value of each year’s values
Three examples a, b, c of change in energy expenditure (EE) from an undeveloped landscape to varying levels of oil and gas development in the years 2001, 2006, 2011, and 2016. Values range from 0 or no change to approximate 800 kilocalories of additional energy needed for a deer to reach a location
Context Wildlife avoid human disturbances, including roads and development. Avoidance and displacement of wildlife into less suitable habitat due to human development can affect their energy expenditures and fitness. The heart rate and oxygen uptake of large mammals varies with both natural aspects of their habitat (terrain, climate, predators, etc.) and anthropogenic influence (noise, light, fragmentation, etc.). Although incorporating physiological analyses of energetics can inform the impacts of both development and conservation, management decisions rarely incorporate individuals’ energetic requirements when deciding on locations for potential development. Objectives We aimed to estimate the change in expected energy expenditure, numerically and spatially, for mule deer to traverse a landscape with varying levels of oil and gas development through time. Methods Using calculations of energy expenditure of mule deer (Odocoileus hemionus) by weight, in relation to physical terrain components, plus avoidance factors for anthropogenic disturbance, we developed a spatiotemporal model of the minimum energy required for mule deer to traverse a landscape. We compared expected energy expenditure across 12 study sites with increasing levels of oil and gas development and over time in our study area, on the northern Colorado Plateau of Utah. Results We found that energy expenditure can be increased by development, regardless of terrain, through increased travel distance associated with avoidance behavior. Maximum median energy expenditure to traverse a 1400 ha sample area rose from 1135 to 1935 kilocalories, a 70% increase in energy required of a mule deer. There was a significant relationship between energy expenditure and the size of oil and gas development (p < 0.001), its compactness (p < 0.05), and its ‘thinness’ (p < 0.001), but not terrain ruggedness (p = 0.25). Conclusion As the energy costs of movement correlate across multiple species of large mammals, our analysis of the energetic cost, for mule deer, associated with development can serve as a quantitative representative of the impacts of oil and gas development for multiple mammals—including threatened or endangered species. Our bioenergetic cost-distance model provides a means of delineating impediments to efficient movement and can be used to quantify the expected energetic costs of proposed future developments. As wildlife are exposed to increasing anthropogenic stressors which reduce fitness, it is important to make strategic siting decisions to reduce energetic costs imposed by human activities.
 
Context Global biodiversity decreases rapidly, driven by various factors ranging from climate change to anthropogenic activities. Identifying driving forces of population decline is critical for biological conservation. Time-series data are especially valuable for this goal, but unfortunately, high-quality time-series data are generally lacking, hampering evidences-based conservation policy making. Objectives In this study, we examined how population growth rates of wintering waterbird species changed across 34 years (1986–2019) in response to changes in landscape context, climatic, ecological and anthropogenic factors in the Yangtze River Floodplain. Specifically: we aimed to (1) understand the factors that are correlated with the population trend of each waterbird species, and (2) identify the spatial scale at which each waterbird species responds to surrounding landscape changes. Methods We systemically collected wintering survey data from 1986 to 2019 in Shengjin Lake National Nature Reserve for six waterbird species including Oriental stork (Ciconia boyciana), Eurasian spoonbill (Platalea leucorodia), Tundra swan (Cygnus columbianus), Swan goose (Anser cygnoid), Hooded crane (Grus monacha) and White-naped crane (Grus vipio), coupled with climatic and anthropogenic data. Satellite images were analyzed to characterize ecological variables and landscape context (both in landscape and class levels). Results Our results suggested that anthropogenic landscape changes surrounding wetland habitats (i.e., landscape context) acted as the primary factors driving the waterbird population changes and were responsible for the observed population declines. In particular, increasing built-up areas and decreasing cropland areas associated with urbanization and human settlement expansion largely explained the declining population size. Our results also showed that different variables operated at a different scale of the landscape context, highlighting the importance of the surrounding landscape configuration at both small and larger scales, as built-up area was most important at around 8 km for most of the studied species, but cropland area expansion benefitted the two crane species at a larger spatial extent. The fishing ban policy implemented in 2017 provides an opportunity for reversing such declines, as positive effects of reduced human activities were observed in a portion of waterbird species. Conclusions The demonstrated strong effects of landscape context on wetland biodiversity illustrated that practical mitigating measures can increase conservation success if they not only target the wetland habitats per se but also include the surrounding non-wetland areas at larger spatial scales.
 
Old-growth forest loss drives the global biodiversity crisis. Nevertheless, this impact could be buffered by the increasing expansion of secondary (regenerating) forests, which can provide supplementary habitat for wildlife. We tested this hypothesis assessing the effect of old-growth and secondary forest cover on the abundance and immature-to-female ratio (proxy of reproductive success) of two endangered primates: Geoffroy' spider monkeys and black howler monkeys. We measured the response and predictor variables across 18 whole landscapes (landscape-scale approach) in the Lacandona rainforest, Mexico. As there could be tipping points of forest loss beyond which species extinction is accelerated (extinction thresholds), we separately tested the linear and non-linear effect of forest cover on each response, independently for three spatial scales. We found stronger and larger-scale negative responses to forest loss in spider monkeys than in howler monkeys. However, the data were better predicted by linear models, giving no support to the extinction threshold hypothesis. In both species, forest loss had stronger negative impacts on monkey abundance when considering old-growth forest, than when considering secondary forest cover, or total (old-growth + secondary) forest cover. Yet, the immature-to-female ratio was weakly related to forest cover in both species. Secondary forests seem to have a weak buffering effect in both species, possibly because they are relatively young (<30 years old) and do not have large trees. This implies that old-growth forests are irreplaceable for preventing primate extirpation, especially for species with specialized diet and large spatial requirements, such as spider monkeys.
 
Context Obtaining accurate and precise maps of landscape features often requires intensive spatial sampling and interpolation. The data required to generate reliable interpolated maps varies with sampling density and landscape heterogeneity. However, there has been no rigorous examination of sampling density relative to landscape characteristics and interpolation methods. Objectives Our objective was to characterize the 3-way relationship among sampling density, interpolation method, and landscape heterogeneity on interpolation accuracy and precision in simulated and in situ landscapes. Methods We simulated landscapes of variable heterogeneity and sampled at increasing densities using gridded and random strategies. We applied three local interpolation methods (i.e., Inverse Distance Weighting, Universal Kriging, and Nearest Neighbor) to the sampled data and estimated accuracy (slope and intercept) and precision (R²) between interpolated surfaces and the original surface. Finally, we applied these analyses to in situ data using a normalized difference vegetation index raster collected from pasture with various resolutions. Results In our simulations, all interpolation methods and sampling strategies yielded similar accuracy and precision except in the case of Universal Kriging with random sampling. Additionally, low heterogeneity and increasing sample density improved both accuracy and precision, with cross-validation slopes and R² values approaching optimal values. In situ analysis demonstrated that heterogeneity decreased with resolution. Nearest Neighbor under both sampling strategies and Universal Kriging using the gridded sampling strategy had the highest accuracy and precision. Decreased heterogeneity and increased sampling density improved accuracy and precision for all combinations of interpolation method and sampling strategies. Conclusions Heterogeneity of the landscape is a major influence on the accuracy and precision of interpolated maps. There is a need to create structured tools to aid in determining sampling design most appropriate for interpolation methods across landscapes of various heterogeneity.
 
Map of priority areas and links resulting from the consensual mapping exercises. During our workshop, the study region was divided into multiple subregions, for each a singular table was set up. For each table, different sets of priority areas (shaded areas) and links (dotted lines) were identified and mapped
Map of Montérégie at the beginning of the BAU (Business as Usual) scenario in 2010, and at the end of the simulation in 2100 (under baseline climate scenario)
Heatmap of mean change (N = 10 iterations) in mean flow (averaged over the landscape and in % of the baseline flow) between 2010 (observed) and 2100 (predicted), faceted by species (BLBR: short-tailed shrew, MAAM: American marten, PLCI: red-backed salamander, RASY: wood frog, URAM: black bear) and land use change scenarios
Histograms of flow values (log), faceted by species (BLBR: short-tailed shrew, MAAM: American marten, PLCI: red-backed salamander, RASY: wood frog, URAM: black bear) and scenario (only including combinations with the climate scenario RCP 8.5), comparing the observed values for 2010 (in blue) with the values from the model output in 2100 (in red)
Mean percent change for all network metrics (except for mean cost; on the x axis), for all species (on the y axis), and scenarios (on the alternate x axis). Scenarios: BAU—business as usual; BAUR—Business as usual with reforestation; CP—protection of corridors identified in the workshop; CPR—protection of corridors identified in the workshop with reforestation—CPTR—protection of corridors identified in the workshop with targeted reforestation in corridors (BLBR: short-tailed shrew, MAAM: American marten, PLCI: red-backed salamander, RASY: wood frog, URAM: black bear)
Context An important output of connectivity science is the identification of priority areas for the conservation of landscape connectivity. However, current connectivity conservation planning methods rarely take into account risks associated with future land use and climate change, and seldom incorporate stakeholder perceptions of connectivity priorities. Objectives We modeled future connectivity change for five umbrella vertebrate species in a fragmented landscape, the Montérégie region in Québec, Canada. We aimed to show how connectivity, land use change, and climate change models can be integrated with stakeholder information to derive simple connectivity assessments and conservation scenarios. Methods We projected change in ecological connectivity along with land use and climate change, for five vertebrate species whose needs are representative of the habitat and movement needs of many other vertebrates in our study region. We organized a participatory workshop with local stakeholders, utilizing methods of consensus building to identify priority areas for connectivity. We used the results to generate simple connectivity conservation scenarios. Results Land use change strongly impacted connectivity negatively for all species. The effects were worsened by climate change the more our species relied on climate-sensitive forest habitats, suggesting that interactions between climate and land use change can matter even at sub-regional scales. Integrating stakeholders’ priorities into connectivity modeling allowed for the definition of useful scenarios. Conclusions Our results highlight the relevance of an iterative, multi-stakeholder approach to the definition of scenarios for connectivity conservation priorities. Integrated models can support the scenario-making process for fragmented landscapes, where deriving realistic and relevant alternative scenarios is challenging.
 
Illustration of a different spatial networks. Panel a presents a weighted 4-node network denoted G\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{G}$$\end{document} and its corresponding adjacency matrix (AG\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{A}}_{\mathrm{G}}$$\end{document}). The weights, denoted numerically and by edge width, act as facilitators/inhibitors of movement along some dispersal corridor between nodes. Panel b depicts two networks with equal spectral radius (λG=80km\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\uplambda }_{\mathrm{G}}=80\mathrm{ km}$$\end{document}), but different eigenvector centrality (EC) variance. Solid nodes represent a network with a zero EC variance (varv→G=0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{var}\left({\overrightarrow{\mathrm{v}}}_{\mathrm{G}}\right)=0$$\end{document}); hollow nodes represent a network with a nonzero EC variance (varv→G=0.026\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{var}\left({\overrightarrow{\mathrm{v}}}_{\mathrm{G}}\right)=0.026$$\end{document}). Panel c depicts two networks with equal spectral radius and EC variance (λG=65km\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\uplambda }_{\mathrm{G}}= 65\mathrm{ km}$$\end{document} and varv→G=0.0086\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{var}\left({\overrightarrow{\mathrm{v}}}_{\mathrm{G}}\right)=0.0086$$\end{document}), but different EC skewness. Solid nodes indicate a network with a negative EC skewness (skewv→G=-1.79\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{skew}\left({\overrightarrow{\mathrm{v}}}_{\mathrm{G}}\right)=-1.79$$\end{document}); hollow nodes indicate a network with a positive EC skewness (skewv→G=1.086\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{skew}\left({\overrightarrow{\mathrm{v}}}_{\mathrm{G}}\right)=1.086$$\end{document})
Relating eigenmetrics to other popular network metrics. Note that many of the above relationships arise from the fact that adjacency matrices considered in this study are fully connected, zero-diagonal, nonnegative and symmetric. For details of their derivation, see Supplemental Material B. Correlations between metrics are presented in Table 1. Network metric abbreviations are given by: w¯\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\varvec{\overline{w}}}$$\end{document} = mean strength, Eglob\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \varvec{E}_{{\varvec{glob}}} $$\end{document} = global efficiency, lG\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{l}\left( \varvec{G} \right)$$\end{document} = mean shortest path length, Eloc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \varvec{E}_{{\varvec{loc}}}$$\end{document} = mean local efficiency, c¯\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\varvec{\overline{c}}} $$\end{document} = mean clustering coefficient
Relating single indicators to ecological outcome. Each row pertains to the spread (a–c) and survival (d–f) scenarios. Data are presented as median measures of each model outcome. The solid red line in each plot represents a fitted linear trend in the data
Grouping spread outcomes. In a, color denotes the median time to full network occupation, averaged across all values of EC skewness. Panels c–e present median spread times for select values of EC skewness. (Individual plots for all values of EC skewness can be found in Supplemental Material D.) Dark blue shades correspond to regions of fast spread; yellow colors indicate regions of slow spread. In b, color denotes the standard deviation of spread time taken across all values of EC skewness. Dark blue shades indicate regions with lower variation across EC skewness; yellow colors correspond to regions of high variation. The dot-dashed line highlights the boundary between fast and more variable spread regions
Grouping survival outcomes. In a, color denotes the median time to full network extinction, averaged across all values of EC skewness. Panels c–e present median survival times for select values of EC skewness. (Individual plots for all values of EC skewness can be found in Supplemental Material D.) Dark blue shades correspond to regions of quick extinction; yellow colors indicate regions of longer persistence. In b, color denotes the standard deviation of survival time taken across all values of EC skewness. Dark blue shades indicate regions with lower variation across EC skewness; yellow colors correspond to regions of high variation. The dot-dashed line highlights the boundary between quick extinction and more variable survival regions
ContextNetwork-theoretic tools contribute to understanding real-world system dynamics, such as species survival or spread. Network visualization helps illustrate structural heterogeneity, but details about heterogeneity are lost when summarizing networks with a single mean-style measure. Researchers have indicated that a system composed of multiple metrics may be a more useful determinant of structure, but a formal method for grouping metrics is still lacking. Objectives Our objective is to present a tool that can account for multiple properties of network structure, which can be related to model outcomes.Methods We develop an approach using the statistical concept of moments and systematically test the hypothesis that this system of metrics is sufficient to explain variation in processes that take place on networks, using an ecological system as an example.ResultsOur results indicate that the moments approach outperforms single summary metrics by adjusted-R2 and AIC model fit criteria, and accounts for a majority of the variation in process outcomes.Conclusions Our scheme is helpful for indicating when additional structural information is needed to describe system process outcomes such as survival or spread.
 
Contexts To quantify ecosystem service (ES) changes caused by the dynamic of green vegetation (GV) during rapid urbanization, it is necessary to fully understand the ‘real’ conversion pattern of GV, yet key and ‘real’ conversion patterns within specific periods and contributions to GV quality remain poorly understood. Objectives We use normalized difference vegetation index (NDVI) as a mediator to represent GV quality. Land cover transfer matrix (LCTM) and urban greenspace dynamic index (UGDI) were employed to fully understand GV dynamic from quantity-quality and gain-loss perspectives. The Pearl River Delta Metropolitan Region (PRDMR), one of the fastest urbanizing regions in the world, was selected as a case. Results From 1990 to 2015, built-up land, forests and grasslands has the most dynamic conversion, and also has the most significant impact on NDVI. The NDVI value of the newly-built forest (0.29) was much lower than that of the lost forest (0.5), which demonstrate the value and importance of existing natural forest ecosystem, as newly-built forest does not provide the same ESs (although newly-built forest generally has stronger carbon sequestration ability). Hence, we reconfirm and suggest that in regional ecological planning and management, in addition to creating new, higher quality GV, it is essential to protect existing natural forest ecosystems. Conclusion The study proposed new and full perspectives, including quantity-quality and gain-loss angle of view, enhance the understanding of GV dynamic and can be used in other related analyses. Results also provide important theoretical bases for regional ecological planning and natural forest ecosystem protection.
 
Map of the study area at the BDFFP, Central Amazon, Brazil and schematic representation of the BDFFP landscape during data collection (2011–2013), illustrating the low structural contrast between the continuous forest, late-stage secondary regrowth forest matrix (approximately 30 years of regeneration) and forest fragments
Comparison of α-diversity metric q = 2 across the Interior-Edge-Matrix and size gradients at the Biological Dynamics of Forest Fragments Project (forest fragment interiors, forest fragment edges and adjoining secondary forest/matrix). The predicted differences between each habitat and continuous forest interior, modelled using MCMCGLMM are plotted with their corresponding 95% credible interval. Those which do not touch or overlap the vertical dashed line (0) are considered significant (*pMCMC < 0.05, **pMCMC < 0.01 ***pMCMC < 0.001). See Fig. S3 in Online Supplementary Material for q = 0 and q = 1
Taxonomic, functional and phylogenetic diversity metrics q = 2 modelled as a function of local and landscape predictor variables (vegetation structure, forest cover, edge density and patch density) based on surveys of aerial insectivorous bats at the Biological Dynamics of Forest Fragments Project, Brazil. Shown are posterior mean estimates ± 95% credible intervals. Credible intervals which do not touch or overlap the zero line are considered significant (* pMCMC < 0.05). See Fig. S4 in Online Supplementary Material for q = 0; Fig. S5 in Online Supplementary Material for q = 1
Context Human-modified landscapes are globally ubiquitous. It is critical to understand how habitat loss and fragmentation impact biodiversity from both a local habitat context and landscape-scale perspective to inform land management and conservation strategies. Objectives We used an experimentally fragmented landscape in the Brazilian Amazon to investigate variation in aerial insectivorous bat diversity in response to local habitat and wider landscape characteristics, applying a multiscale approach. Methods We conducted bat acoustic surveys at 33 sites, comprising old secondary forests and fragments of primary forest. Taxonomic, functional and phylogenetic diversity facets were calculated within a Hill numbers framework. We analysed responses to fragment size, interior-edge-matrix gradients, as well as local vegetation structure, continuous forest cover, edge density and patch density across five spatial scales (0.5−3 km) surrounding detector locations. Results Compared with continuous forest, secondary forest matrix around the smallest fragments harboured lower diversity. The overall negative effect of the matrix became less pronounced with increasing fragment size. In contrast, forest edges generally contained higher taxonomic, functional and phylogenetic diversity. We found subtle scale-sensitive associations for functional diversity, responding positively to forest cover (at the 1 km scale) and negatively to edge (1 km scale) and patch density (2.5 km scale). Conclusions Despite a low-contrast matrix of tall secondary forest surrounding fragments after ~ 30 years of forest recovery, aerial insectivorous bat diversity is not comparable to continuous primary forest. Assemblage functional diversity responds to compositional and configurational landscape characteristics at scales deserving further evaluation at guild and species level.
 
Sixteen chessboard-like landscapes a–p with dimensionality of 3 × 3, 4 × 4, 8 × 8 and 16 × 16 with two classes –class white, and class green. The existing 3 × 3 landscapes a–d were fine-grained to 4 × 4, 8 × 8, and 16 × 16 to depict how entropy changes when changing spatial resolution while keeping the configurations similar to the original a–d landscapes
A conceptual depiction of the framework that shows how to calculate average Kullback–Leibler divergence for a sample, and how to proceed to a Kullback Information Index (KII) that captures both the compositional and configurational organization of the landscape with a single number. In the top leaf (Sampling unit 1) the two types of cells appear in separate patches (yellow and green) in panel a. In b the Kullback–Leibler divergence DKL\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{KL}$$\end{document} is calculated for each sample and its average found for each location (A, B, C). In c each average is further divided by the edge length to yield the Kullback Information Index (KII), which thus is a property of the entire leaf. In the lower leaf (Sampling unit 2) the yellow and green components are thoroughly mixed in each sampling cell, yielding more or less the same averaged Kullback values e as in the upper leaf. However, the edge length is now much larger and the KII therefore noticeably lower f, clearly distinguishing the lower leaf from the upper. The KII can then be used to compare samples across scales by comparing to the maximum possible value of KII, which scales directly with the number of cells and inversely to the spatial normalization factor [i.e., Total Edge length (TE) in this example]
A stepwise sketch of the proposal to study multi-scale entropy patterns of a coral reef (top panels) and forest ecosystem (bottom panels) using Kullback Information Index. The sequence is the following: a Calculate the probability distributions of the available components (green, yellow, black etc.) at different locations (A, B, C). b Calculate the Kullback–Leibler divergences and average them for each of these locations. c Normalize each of the averages with a spatial component (e.g., distance between samples or edge length) where the different components meet to get the Kullback Information Index (KII). d Compare these KII for different sampling locations. e Coarse grain further to compare different individuals and f still further to different landscapes
Context The way organisms are patterned in space dictates the outcome of many ecological processes such as growth, survival, colonization, and migration. The field of landscape ecology has developed quantitative metrics to describe spatial patterning using the concept of entropy. However, a general theory of how these patterns relate to one another within and between different organizational levels and over different spatial scales has remained incomplete. Objectives Review how statistical versions of entropy have been applied to detect spatial organization and propose a theoretical framework to use Kullback–Leibler relative entropy for cross-scale analyses on a landscape of any size. Methods Examine previous efforts using entropy in landscape ecology and introduce a Kullback Information Index as a next step in the science of scaling. Results Entropic indices can provide compositional and configurational information about a system and can be used to detect landscape patterns. Yet, most entropy-based metrics are scale-dependent, highlighting the need to find a common currency for comparative analysis across scales. The non-symmetric unitless property of the Kullback–Leibler relative entropy may remedy that since it is theoretically capable of comparing variables and scales. The proposed framework can be extended to describe any system that contains scalable modules of interest, which will advance scaling in landscape ecology and other disciplines. Conclusions The Kullback Information Index describes landscapes’ compositional and configurational patterns across scales. Since relative entropy is connected to information theory and thermodynamics, the framework’s results can be interpreted within a broader ecological context.
 
Location of the two townships that comprised our primary study area (black filled polygons) and the test landscapes, in relationship to the footprint of the Landsat scene (WRS2 Path 12 Row 28) associated with the time series of forest harvest history used to derive habitat data (black outline). Test landscapes included an ecological reserve adjacent to our study area (gray filled polygons), and a working forest in northwestern Maine (hatched polygons)
Effective trapping areas (black outlines) overlaid and habitat conditions A) ca. 1997 on our primary study area and B) ca. 2007 on our northwestern Maine test landscape. Habitat is classified as unsuitable (white), suitable without partial harvesting (dark green), or suitable with partial harvesting (light green). Also clearly visible is the forest preserve directly East of the primary study area, which had no history of forestry for at least the last 50 years
Empirical cumulative distribution functions comparing landscape metrics in real, occupied home ranges (solid line) with simulated, unoccupied home ranges (dotted line) for male and female marten combined. Metrics include percent of home range in suitable habitat (PHR), patch density (PD), landscape shape index (LSI), and area-weighted average patch area (AREA)
Probability of occurrence for female and male American marten circa 1997 and 2007 across central and western Maine, USA (Landsat WRS2 Path 12 Row 28) estimated based on top sex-specific models. Locations of the primary study area where models were developed and second test landscape are outlined in white
ContextFor wildlife dependent on mature forest living in managed landscapes, habitat loss from land use can outpace habitat recovery from forest regrowth, challenging persistence of habitat specialists. For some species, the effects of habitat loss or fragmentation may also differ between sexes when physiological or behavioral differences influence space use.Objectives We evaluated differences in landscape-scale occurrence, as a function of habitat amount and configuration, between male and female American martens in the heterogeneous, commercial forestlands of northern Maine, USA.Methods Using location data from a long-term (1994–1997) radio-telemetry study, we modeled boundaries of home ranges occupied by resident adults (> 1 year) and simulated potential home ranges in unoccupied areas. Landscape metrics of habitat amount and configuration within home ranges, calculated from time-specific maps of suitable habitat derived from Landsat satellite imagery, were used to develop binary logistic regression models that were compared within an information theoretic framework. We extrapolated and tested top performing models in two novel landscapes.ResultsHabitat amount was most important when predicting female occurrence. For males, which maintained larger home ranges, occurrence was influenced by habitat amount and patchiness (density and shape). Top sex-specific models reliably predicted marten occurrence (1991–1996) in an adjacent forest preserve, but overestimated occurrence, particularly for females, in a more distant (~ 70 km Northeast) commercial forest in 2007.Conclusions Marten in our study exhibited nonlinear declines in occurrence as suitable habitat declined. Maintaining sufficient habitat to support adult females in particular will be important to the future conservation of this late breeding, area sensitive, habitat specialized species.
 
Map created in Q-GIS 3.10 showing the layout of scent-marking posts, waterpoints, buffers and protected area, with the study area location in Namibia shown on inset map. Buffers were selected for each species as those with the lowest AICc during AICc ranking of linear models in R; final buffers (3.065 km radius) for cheetah are based on 12 h digestion time and 6.13 km daily movement rate (Houser et al. 2009); final buffers for leopard (2.07 km radius) are based on 36 h digestion time and 1.38 km daily movement rate (Marker and Dickman 2005). Raster FWC layer provided by Wessels et al. (2019). Data for layers provided by CCF
An example camera-trap image of a cheetah showing morphometric points used to derive belly score measurements (in pixels) and angles (in degrees). FL front leg length, BCL belly chord length, BD belly drop length, HC horizontal chord angle, VC vertical chord angle, Θ leg angle calculated from HC and VC. Measurements in this image were used as the standard for cheetah as the image displayed near-perfect posture. Image provided by CCF. Belly score method adapted from Cloutier (2020). Image created in GIMP 2.10.24
Graphs created in RStudio showing predicted probabilities of leopard (a, b) and cheetah (c, d) belly score against covariates in the two top models (Table 4). Plots for covariates in the top leopard dry season model are: a quadratic mFWC; b distWP. Plots for covariates in the top cheetah wet season model are: c quadratic mFWC; d pPA. No models were supported for leopard in the wet season or cheetah in the dry season. Covariates for which confidence intervals did not overlap zero are indicated by an asterisk
Context Habitat loss and alteration affect wildlife populations worldwide. Bush encroachment alters landscapes and threatens arid and semi-arid grasslands, but its effects on predator–prey relationships and carnivore community ecology are not well understood. Predation strategies of large predators, for example high-speed pursuits versus ambush from short distances, are likely to be affected differently by bush encroachment. Objectives We assessed how bush encroachment affects overall predation success of cheetahs (Acinonyx jubatus) and leopards (Panthera pardus) in a savanna landscape under variable fractional woody cover (FWC). We tested if predation success remained relatively unchanged for leopards across a gradient of FWC, and whether cheetah predation was most successful at low to intermediate cover and varied seasonally. Methods Belly scores of predators were measured from camera-trap images collected over 7 years in north-central Namibia and used to index predation success. We derived predicted belly score probabilities as a function of FWC, which was measured using a Synthetic Aperture Radar (SAR)-derived satellite data layer. Results Predicted leopard predation success was highest at 0.27–0.34 FWC in the dry season, potentially due to lower prey density in highly covered areas and decreased prey catchability in low cover. Predicted cheetah predation success was highest at 0.24–0.28 FWC in the wet season, potentially due to increased landscape openness, high availability of habitat margins for visualising and stalking prey, and decreased kleptoparasitism by leopards. These results highlight optimal habitat cover thresholds that favour lower FWC for cheetah than for leopard. Conclusions The findings indicate that landscape heterogeneity is important for predation success of cheetahs and leopards, suggesting that habitat management should focus on bush control efforts to maintain intermediate levels of bush cover.
 
Matrix composition and distribution of the habitat patches in the study area
Age at first emigration in relation to body size (chord length from bill to tail), body condition (Scaled Mass Index, SMI), habitat quality of natal patch (density of large oaks) and natal patch size. Solid lines show mean values and grey shaded areas 95% CrI of model predictions for males (light grey) and females (dark grey) with all other model predictors set to their mean values. Dots depict raw data points for males (white dots) and females (black dots); n = 41 individuals
Age at emigration in relation to local population density in patches with hard edges (10th quantile, corresponding to a deciduous forest proportion of 0.292; solid line), intermediate edge softness (50th quantile, corresponding to 0.558; dashed line) and soft edges (90th quantile, corresponding to 1.000; pointed line). Lines show the mean values and grey shaded areas 95% CrI of model predictions for females with all other model predictors set to their mean values. Dots depict raw data points; n = 41 individuals
Duration of transfer in relation to body condition (Scaled Mass Index, SMI) and local population density. Lines show mean values and shaded areas 95% CrI of model predictions for males (light grey) and females (dark grey) with all other model predictors set to their mean values. Dots are raw data points for males (white dots) and females (black dots); n = 35 individuals
Net distance from the natal nest at the end of transfer in relation to local population density and patch connectivity. Lines show mean values and shaded areas 95% CrI of model predictions for males (light grey) and females (dark grey) with all other model predictors set to their mean values. Dots are raw data points for males (white dots) and females (black dots); n = 35 individuals
Context Natal dispersal critically influences eco-evolutionary dynamics and the persistence of spatially structured populations. As both short- and long-distance movements contribute to population persistence in fragmented landscapes, understanding dispersal requires assessing phenotypic and environmental effects on a wide range of distances. Objectives To assess phenotypic and environmental correlates of dispersal movements in fragmented landscapes. Methods We radio-tracked juvenile middle spotted woodpeckers in fragmented landscapes to assess phenotypic and environmental effects on emigration age, transfer duration (in days), and transfer distances. Results Large fledglings and those in good condition emigrated earlier than smaller individuals and those in worse condition. Birds in better condition also reduced transfer duration. Overall, females dispersed earlier, remained shorter at transfer and moved further than males. However, while females increased transfer distances with increasing connectivity, males increased distances with decreasing connectivity. Emigration age increased with decreasing patch size and increasing patch quality, and with decreasing population density in patches with soft edges. Both transfer duration and distance increased with decreasing population density. Conclusions The correlations between phenotypic traits of fledglings and their posterior movements suggest that early-life conditions influenced dispersal through carry-over effects. Early emigration from low-quality and high-populated patches can be a behavioural mechanism to quickly escape adverse natal conditions, but population density effects were modulated by edge hardness. Finally, because reductions in connectivity led to similar transfer distances between sexes through a reduction in female distances, a lack of sex-biased dispersal can be a previously overlooked effect of habitat isolation that may alter eco-evolutionary dynamics.
 
Context Connectivity between habitat patches is vital for ecological processes at multiple scales. Traditional metrics do not measure the scales at which individual habitat patches contribute to the overall ecological connectivity of the landscape. Connectivity has previously been evaluated at several different scales based on the dispersal capabilities of particular organisms, but these approaches are data-heavy and conditioned on just a few species. Objectives Our objective was to improve cross-scale measurement of connectivity by developing and testing a new landscape metric, cross-scale centrality. Methods Cross-scale centrality (CSC) integrates over measurements of patch centrality at different scales (hypothetical dispersal distances) to quantify the cross-scale contribution of each individual habitat patch to overall landscape or seascape connectivity. We tested CSC against an independent metapopulation simulation model and demonstrated its potential application in conservation planning by comparison to an alternative approach that used individual dispersal data. Results CSC correlated significantly with total patch occupancy across the entire landscape in our metapopulation simulation, while being much faster and easier to calculate. Standard conservation planning software (Marxan) using dispersal data was weaker than CSC at capturing locations with high cross-scale connectivity. Conclusions Metrics that measure pattern across multiple scales are much faster and more efficient than full simulation models and more rigorous and interpretable than ad hoc incorporation of connectivity into conservation plans. In reality, connectivity matters for many different organisms across many different scales. Metrics like CSC that quantify landscape pattern across multiple different scales can make a valuable contribution to multi-scale landscape measurement, planning, and management.
 
Road network of Friuli Venezia Giulia Region. Background colours represent elevation: lighter colours correspond to higher elevations. The road network lies mainly within the lowlands.
Habitat suitability map for golden jackal. Highly suitable areas are shown in red and are represented mainly by the Karst (south-eastern FVG), river valleys, hilly areas bordering the lowlands and the natural areas within the lowlands (the southern part of FVG). Lowlands and uplands, as well as steep areas are depicted as poor habitats for golden jackals.
Habitat connectivity map for golden jackal. Areas with high connectivity values are represented mostly by the Karst, river valleys and hilly areas. Most of the road-killed golden jackals were found within the Karst, since it is a permeable area, as depicted by its high connectivity values
Effect of a distance from the nearest Slovenian source population (m), b presence of guardrails and fences (0 = no, 1 = yes), and c type of road (1 = highway, 2 = state roads, 3 = regional roads, 4 = local road) in terms of roadkill risk for jackals
Effect of a percentage of suitable habitats calculated within each buffer, b habitat fragmentation, expressed through the landscape division index (0 = poorly fragmented habitat, 1 = highly fragmented habitat), and c habitat connectivity in terms of roadkill risk for golden jackals
Context Anthropogenic structures have considerable effects on ecosystems, disrupting natural population processes and representing a serious risk in terms of vehicle collisions. The golden jackal (Canis aureus) is a mesocarnivore species whose range is expanding in Europe. Roadkills are one of the main human-induced mortalities in Italy to the species. Objectives Identify road-related characteristics and ecological factors related to golden jackal roadkill risk in Italy. Methods We used habitat suitability (Maxent) and connectivity (Circuit theory) models to derive 15 metrics potentially affecting roadkill risk. We tested their influence using Bayesian generalized linear models and generalized linear models comparing golden jackal roadkill locations to random locations. Furthermore, we tested if there were significant sex, age-related and seasonal differences among road-killed individuals. Results We found that roadkill risk was higher in areas characterized by higher values of habitat suitability and connectivity, habitat fragmentation and along highways. It was lower with increasing distance to the source population and in the presence of guardrails. No significant differences were detected in terms of roadkill risk between sexes, age classes and season Conclusions The identified factors affecting road mortality of golden jackals in Italy provide insights on how to mitigate wildlife-vehicle collisions. Crossing areas, and visual and acoustic warnings for wildlife, as well as the importance of managing fences along high traffic volume roads could help mitigate further damage. Finally, there is a need to further investigate the effectiveness of mitigation measures in the light of the golden jackal’s ongoing expansion in a human-modified landscape.
 
Schematic representation of a landscape graph. Light green patches with trees represent habitat patches, i.e. the nodes of a landscape graph. Brown paths represent favourable dispersal paths between habitat patches, i.e. the links of a landscape graph
Joint use of landscape graphs (LG) and genetic graphs (GG) to address landscape genetic questions. Populations and habitat patches (A, B, C, D) are the same in both types of graph
Simulated relationships between graph-theoretic landscape and genetic variables. (A) Land cover map used for creating the landscape graph. The forest patches of an existing French landscape (225km2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$225\,\hbox {km}^{2}$$\end{document}) were identified and used for creating the nodes. Cost values were assigned to the five land cover types (between brackets) for computing least-cost paths and corresponding cost-distances. The dispersal paths followed by the individuals pertaining to the 89 populations during the gene flow simulations were associated with cost-distances lower than the so-called “percolation” threshold in order to keep the graph connected. Their width is proportional to dispersal probability, which decreases exponentially with cost-distance. Node size is proportional to the simulated allelic richness at generation 500. (B) Biplot showing the relationships between the genetic response variables (allelic richness, MIW, in black), the landscape predictor variables (capacity, Flux (F), Betweenness Centrality (BC), in blue) and the two main factor variables deriving from the PLS-R2 regression (t1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t_{1}$$\end{document} and t2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t_{2}$$\end{document}). Q2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Q}^{2}$$\end{document} and R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {R}^{2}$$\end{document} indicate the share of variance of the response variables explained by each factor variable, with and without cross-validation respectively. Q2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Q}^{2}$$\end{document} values above 0.0975 indicate that the corresponding factor variable has a significant effect on the response variables
Simulated relationships between landscape graph and genetic graph modularity patterns in response to barrier effects. (A) Land cover map used for creating the landscape graph in a similar way as described in Fig. 3. The dispersal paths followed by the individuals of the 60 randomly located populations during the simulations had a planar topology. (B) The links of the genetic graph built at generation 500 were weighted with DPS\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {D}_{{\mathrm{PS}}}$$\end{document} values and pruned using a “percolation” threshold applied to DPS\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {D}_{{\mathrm{PS}}}$$\end{document} values in order to keep the graph connected. The population colours and symbols indicate the four modules to which they pertain according to the optimal partition. Black bold lines correspond to the spatial limits of the four habitat patch modules deriving from the landscape graph. (C) Adjusted Rand Index (ARI) values according to the number of modules in the landscape and genetic graphs whose module partitions are compared. This relationship is depicted for every alternative cost value assigned to highways (from 1 to 10000)
Complementarity between genetic graphs and landscape graphs. Habitat patches in which genetic sampling occurs are displayed by a white dot on the left panel
Context All the components of landscape and genetic structures can be associated with the nodes and links of landscape graphs and genetic graphs. Yet, these graphs have long been used separately despite the potential for their combined use in landscape genetics. Objectives First, comparing these graphs could be an effective way to disentangle the influence of intra-patch features from that of inter-patch connectivity on genetic structure or to assess whether intra-population genetic diversity and inter-population genetic differentiation are sensitive to the same landscape influences. Methods Moreover, because graph pruning determines which connections between nodes are considered in calculating neighbourhood-based metrics or graph-based distances, comparing the metrics or distances derived from differently pruned graphs can be an effective way to identify the scale of landscape effects or the scale at which both gene flow and drift determine genetic differentiation. Similarly, comparing node partitions in both types of graphs could strengthen the validity of barrier identifications. Results Second, beyond mere comparisons, integration of landscape and genetic graphs through gravity models can further enhance their joint use for theoretical and applied objectives alike. Conclusion We thus believe that future research could illustrate and enhance the relevance of these methods for a wider range of applications in landscape genetics.
 
Structure of the network of the study of ecosystem service trade-offs in landscapes (1990–2019). The size of each node is associated with the frequency of co-citation. Nodes with a purple outline denote articles with a high centrality value
Thematic clusters make up the main network with their limits represented by polygons. Seven clusters complete the main network of the analysis of ES trade-offs (A). Eleven clusters make up the counterfactual analysis network (B). The black arrows highlights the most relevant clusters of research in A and relevant clusters in B
Analysis of the temporal evolution of the scientific literature with respect to ES trade-offs in landscapes. Each horizontal line corresponds to a timeline for each thematic cluster, labeled on the right with black letters
Context Trade-offs between ecosystem services (ES) occur by premeditated decision or as an involuntary consequence of landscape change. This has been highlighted as a challenge to human well-being and landscape sustainability. Little is known about the main research topics on ES trade-offs and the evolution of this subject over time. Objectives To identify the main areas of research in the study of ES trade-offs in landscapes and analyze the temporal evolution of the scientific field. Methods We conducted a scientometric analysis to visualize the structural configuration of the ES trade-offs field of study; and identify its thematic trends and temporal evolution employing CiteSpace co-citation analysis. Results We identified seven articles that are pivotal to the discipline and four main areas of research: (1) landscape functioning and ES, (2) interactions between multiple ES, (3) landscape management, (4) social-ecological perspective. The field of study is evolving through a transition to a qualitative perspective with approaches that includes social perceptions of ES trade-offs across landscapes. This transition is driven by the emerging trend in the discipline: social-ecological perspective in ES trade-off assessments. Conclusions The scientific field is contributing to the construction of a body of knowledge about the relationship between ES and human well-being which is a substantial support to for advancing in landscape sustainability science. Future works could focus on the development of methodologies that strongly incorporate the temporal scale in ES trade-offs, including social variables and participatory approaches in ES trade-offs assessments.
 
Context The process of forest fragmentation determines landscapes with isolated forest patches immersed in a distinct matrix. This process may hinder pollinator movement throughout the landscape, which may negatively impact on pollen flow among native plant populations. Objectives We evaluated the effect of the loss of forest connectivity on pollen dispersal by specialized native bees in the oil-producing and self-incompatible Nierembergia linariifolia. Methods We estimated pollen flow between plants of N. linariifolia at an agroecosystem with remnant forest of central Argentina. Six plant populations (source populations) were treated with fluorescent dyes as pollen analogues, and stigmata of recipient plants were collected to seek for dye particles. Dye deposition rate was assessed for plants that were connected through remnant forest to a source population or unconnected by a crop matrix, and at increasing distances to a source population. Results Deposition rate per plant was higher in connected than in unconnected plants, and decreased with increasing distances to a source population in an exponential fashion. Most of the dispersal events between connected plants occurred at the vicinity of a source population. Long dispersal events (up to 1259 m) were recorded between plants located at neighbouring forest patches separated by an agricultural matrix. Conclusions Landscape connectivity through forest remnants is key to enhance pollen flow between self-incompatible plants such as N. linariifolia. Besides, the evidence of pollen dispersal through the agricultural matrix pinpoints the essential role of native pollinators in maintaining pollen flow among unconnected plant populations in fragmented landscapes.
 
Context Ecological structure in ecotones, defined by how species from adjacent systems co-occur, affects ecosystem functions and climate change responses. Ecotone structure can vary spatially, yet variability in broader-scale ecotones is poorly understood. In Wisconsin (USA) the Tension Zone is an ecoregional ecotone, separating northern and southern ecosystems. Objectives Characterize ecotone structure in the Tension Zone, examine how structure varied spatially, and identify how environmental drivers affected structure. Methods Using historical (1800s) tree occurrence data, we examined co-occurrence of northern and southern species at multiple scales (1.0 km to 7.5 km) at different locations in the Tension Zone, identifying the finest scale at which co-occurrence was detected. We assessed relationships between co-occurrence and environmental variables. Results Co-occurrence emerged at different scales, related to interacting climate and soil variables and location within the ecotone. Northern and southern trees co-occurred at broader scales near ecotone center and at locations with higher climatic water availability and sandier soils; they co-occurred at finer scales in locations with higher climatic water availability and richer soils. Sites with xeric tree species were associated with broader-scale co-occurrence. Conclusions We detected spatially variable structure within the Tension Zone, resulting from multi-scale processes among underlying environmental drivers. Finer-scale co-occurrence may have resulted from competition in high-resource environments, while broader scale co-occurrence may have been driven by fire and associated feedbacks. Characterizing structure in an ecoregional ecotone adds to a growing body of evidence that finer-scale factors play a role in defining the characteristics, functions, and responses of broader-scale ecotones.
 
Map of study area within Switzerland. a Spatial distribution of the 98 survey plots in the Swiss Plateau. Land use types (100-m resolution) were represented in the estimated spatial extents (i.e., 1 × 1, 2 × 2, 4 × 4, 6 × 6 km). b Landscape predictors were measured at multiple spatial extents. The vascular plants were surveyed along transects of 2500 m length and 5 m width within each focal square (i.e., the smallest spatial extent)
Amount of explained deviance (D²) in the models including landscape structure and/or climate for predicting species richness. Besides the single D² value of the climate-only model, the mean value (the vertical green and blue bars) and range (the vertical black lines) of D² are shown for the models with landscape predictors and models with both landscape and climate predictors
Explained deviance D.² of models with fixed spatial scales in comparison to all models of all remaining spatiotemporal scale combinations (36,864 models in total). Box plots show the median, 25% and 75% quartiles and 1.5 interquartile range; outliers are indicated by circles. The best model (red dot) and its corresponding temporal scales (18 = 2018, 09 = 2009, 97 = 1997, 85 = 1985) and spatial scales (1 km, 2 km, 4 km, 6 km) of each predictor are shown above the box plots (W = wooded area, U = urban area, M = effective mesh size). Climatic covariates are not shown in the formula. The results of the global Kruskal–Wallis test and the pairwise Wilcoxon tests among fixed spatial scales are shown in each plot of the nine species groups (a-i). Asterisks (ns = p > 0.1; (*) = 0.05 < p < 0.1; * = p < 0.05; ** = p < 0.01; *** = p < 0.001) refer to comparisons among different fixed spatial scales
Explained deviance D² of models with fixed temporal scales in comparison to all models of all remaining spatiotemporal scale combinations (36,864 models in total). Box plots show the median, 25% and 75% quartiles and 1.5 interquartile range; outliers are indicated by circles. The best model (red dot) and its corresponding temporal scales (18 = 2018, 09 = 2009, 97 = 1997, 85 = 1985) and spatial scales (1 km, 2 km, 4 km, 6 km) of each predictor are shown above the box plots (W = wooded area, U = urban area, M = effective mesh size). Climatic covariates were not shown in the formula. The results of the global Kruskal–Wallis test and the pairwise Wilcoxon tests among fixed temporal scales are shown in each plot of the nine species groups (a-i). Asterisks (ns = p > 0.1; (*) = 0.05 < p < 0.1; * = p < 0.05; ** = p < 0.01; *** = p < 0.001) refer to comparisons among different fixed temporal scales
Context The effects of landscape structure on biodiversity may change with the spatial and temporal scale at which landscape structure is measured. Identifying the spatial extent and temporal scale at which the biodiversity-landscape relationship is strongest (i.e., the scale of effect) is important to better understand the effect of landscape structure. Objectives The spatial and temporal scale of effect is analyzed to identify whether it differs in ecologically distinct species groups. How species richness-landscape relationship changes with spatial and temporal scales is tested. Methods Based on 98 survey plots (1 km²) of vascular plants on the Swiss Plateau, we analyzed the relationships between species richness of different species groups and landscape predictors at different spatial extents (1 km², 4 km², 16 km², 36 km²) and time periods (past landscapes—1985, 1997, 2009 and the current landscape 2018). Results The spatial scale of effect was 1 km for most species groups, while the temporal scale of effect differed among species groups. The strength of the species richness-landscape relationship generally decreased with increased spatial extents, while it changed little across temporal scales. Conclusions Although our study only considered changes in landscape structure over the last c. 30 years, ecologically distinct species groups revealed differences in the temporal scale of effect including a rapid response of neophytes linked to ongoing biological invasions. However, the variation in the species richness-landscape relationship was greater when changing spatial extent than time. We highlight that studying the relationship between landscape structure and biodiversity should consider not only space but also time, and different responses of ecologically distinct species groups.
 
Two examples of the procedure for converting raster maps of landscape probability (habitat suitability, occupancy, habitat selection) of a territorial species for maximum clique analysis using a vertex cover algorithm. Landscape templates are raster files with cells containing a probability value from 0 to 1. The landscape map is converted to a map of home-range capacity by buffering each pixel in the landscape map by a circular moving window the size of the home range of the target organism (a and e). Values are averaged within the circular window and the resulting value is assigned to the central pixel, representing the capacity of the area surrounding the central pixel to support a territory (b and f). Next, threshold values for suitability are chosen among home-range capacity values (0.7 in b and f) as potential territories (‘pseudo home ranges’) across the landscape. Pixels with home-range capacity values ≥ the threshold are classified as pseudo home ranges and buffered by circles the size of a home range (c, g). Pseudo home ranges are then converted from a spatial location to a point in a mathematical graph and numbered (d, h). Overlapping pseudo home ranges (points) are connected by edges (lines, d, h). The maximum clique size is then calculated by subtracting the smallest set of pseudo home ranges that overlap with the rest of the graph from the total number of vertices (two in d and four in h)
Map of the United States, highlighting the state of Indiana in grey (a) where landscape carrying capacity (Nk) was estimated for bobcats (Lynx rufus). Map of major habitat types for bobcats in Indiana (b). Predictive map for habitat suitability of bobcats in Indiana (c). Portion of Indiana exhibiting overlapping pseudo home ranges for the median threshold of habitat quality for maximum clique analysis (d)
Relationship between maximum clique values computed by the vertex cover and the number of pseudo home ranges per cluster (adj r² = 0.999, P < 0.001) used to estimate landscape carrying capacity (Nk) for bobcats (Lynx rufus) in Indiana, U.S.A. (a). Clusters too large for computation (≤ 3158 pseudo home ranges) were predicted by this linear relationship and are represented by filled in circles. Relationship between maximum cliques computed by the vertex cover and greedy algorithms (b, adj r² = 0.999, P < 0.001). Relationship between the difference in maximum clique values computed by the vertex cover and greedy algorithms and the number of pseudo home ranges per cluster (c, adj r² = 0.831, P < 0.001)
Maximum clique values (landscape carrying capacity, Nk) at five thresholds of habitat quality for bobcats (Lynx rufus) in the state of Indiana, U.S.A. Means and confidence intervals include 15 large clusters (> 3000 pseudo home ranges) for the 25.0% and 37.5% thresholds that were either predicted from a linear regression equation (Predicted) or computed using an algorithm that tended to underestimate true values for maximum cliques (Greedy). Means for the remaining thresholds (50%, 62.5%, 75%) did not require prediction of large clusters (Computed). Quality threshold represents quantiles for modeled values of habitat suitability extracted from 236 statewide presence locations. Results for the greedy algorithm are offset to differentiate from predicted results
Context Maximum clique analysis (MCA) can approximate landscape carrying capacity (Nk) for populations of territorial wildlife. However, MCA has not been widely adopted for wildlife applications, mainly due to computational constraints and software wildlife biologists may find difficult to use. Moreover, MCA does not incorporate uncertainty into estimates of Nk. Objectives We extended MCA by applying a vertex cover algorithm to compute Nk over a large (92,789 km²), continuous spatial scale for female bobcats (Lynx rufus) in Indiana, USA. We incorporated uncertainty by calculating confidence intervals for Nk across five thresholds of habitat suitability using 10 replicate suitability maps from bootstrapped datasets. For portions of the landscape too large to be solved with the vertex cover algorithm, we compared predictions from a linear model and a “greedy” algorithm. Results Mean estimates of Nk for female bobcats in Indiana across habitat suitability thresholds ranged from 539 (0.75 threshold) to 1200 territories (0.25 threshold). On average, each 12.5 percentile reduction in the suitability threshold increased estimates for Nk by 1.2-fold. Both the predictive and greedy algorithm produced reasonable estimates of maximum cliques for areas that were too large to compute with the vertex cover algorithm. The greedy algorithm produced smaller confidence intervals compared to the predictive approach but underestimated maximum cliques by 1.2%. Conclusions Our research demonstrates effective application of MCA to species occupying large landscapes while accounting for uncertainty. We believe our methods, coupled with availability of annotated scripts developed in R, will make MCA more broadly accessible to wildlife biologists.
 
Context Habitat loss and fragmentation can interact with other threats, including altered fire regimes, and responses to these effects can be mediated by functional traits. Objectives To determine how richness and abundance of reptile trait groups respond to habitat fragmentation, patch isolation and fire. Methods We surveyed reptiles in 30 sites over 3 years. Sites in remnant patches in farmland were adjacent to a conservation park with either recently burnt or long-unburnt habitat. The remnant patches were stratified by distance from the reserve. Sites were spatially paired, and we experimentally burnt one of each pair in farmland. Trait groups included size, reproduction, habitat position, diet, and activity period. Results None of the trait groups benefited from experimental burns, while the burns reduced abundance of viviparous, small, and above-ground species. Species richness was lower in isolated sites than in sites close to the conservation park, while generalist trait groups appeared unaffected by patch isolation. Large-sized reptiles had higher abundance in remnants. There was not more rapid colonisation of burnt sites near recently burnt conservation park. Instead, low initial abundance may have been caused by fire in combination with drought, with high rainfall during the study allowing recovery and spill-over into adjacent remnants. Conclusions Landscape structure appears to interact with natural fires, restoration burns and longer-term climatic trends to influence the abundance and distribution of reptiles. Traits mediate responses, enabling us to formulate a set of testable mechanistic hypotheses, which illustrates a pathway to generalisation and prediction.
 
Four possible models of causal relationships between species and genetic diversity with coincident spatial structures. In cases A and B, coincident spatial structure between SD and GD results from processes leaving similar spatial footprints on both of them. Contrary to case B, the species–genetic diversity relationship in case A is exclusively driven by processes with similar spatial footprints. In cases C and D, coincident spatial structure between SD and GD results from a direct effect of one diversity component
Access to a view-only version of this paper by using the following SharedIt link: https://rdcu.be/cR0WM Context Understanding species–genetic diversity relationships is key to foster holistic conservation approaches aimed at preserving biodiversity across multiple dimensions. Despite the facts that genetic and species diversity are likely to be spatially structured and that species–genetic diversity correlations (SGDCs) reveal coincident spatial patterns between the two diversity levels, spatial autocorrelation has been largely overlooked. Objectives We assessed the benefits of investigating species–genetic diversity relationships through a spatial framework using high Andean wetlands from Chile as a case study system. Methods Genetic diversity was estimated using amplified fragment length polymorphism markers for five abundant species and species diversity was assessed as taxonomic richness for two communities (plants and benthic macroinvertebrates). We tested SGDCs using Moran Spectral Randomizations (MSR), and used a causal modelling procedure to elucidate relationships between species and genetic diversity and their coincident spatial structures. Results. While traditional correlation tests detected significant SGDCs in most cases (i.e. nine), only three species-genetic relationships reached significance or borderline significance with the MSR approach. In all these cases, genetic and species diversity displayed similar spatial autocorrelation patterns. The causal modelling analyses suggested direct effects of genetic diversity on plant richness for species involved in nutrient cycling. Conclusions. Our study provides new perspectives on species-genetic diversity relationships in high Andean wetlands. In addition, it demonstrates the usefulness of causal modelling approaches and the importance of incorporating spatial information to advance understanding of the processes driving both species and genetic diversity, as well as their interactions.
 
Map of study locations included in Table 1. Points have been differentiated by color based on the mechanisms investigated within the literature. Spruce–fir forests have been identified at a landscape scale, thus the specific species composing these forests varies across latitude
Our proposed theoretical framework for future investigations of spruce beetle and fire interactions. Panel a illustrates that both spruce beetle outbreaks and fire have the potential to alter the substrate (fuel loading and stand basal area) needed for a second disturbance. The outcomes of this modification are not resolved within the literature for spruce beetle outbreaks that are followed by fire but are in clear consensus for fires that are followed by spruce beetle outbreaks. Panel b illustrates that both spruce outbreaks and fires are capable of modifying the environmental conditions [irradiance, evapotranspiration (ET), water balance, and sublimation] that are necessary for a second disturbance. However, little research has been conducted on this topic
Context Disturbance interactions can create compound, novel effects across landscapes compared to individual disturbance events. However, little consensus exists regarding which mechanisms are important for controlling the interaction of two disturbances with similar climatic forcings in subalpine spruce–fir forests. Objectives To investigate the importance of controls on disturbance interactions, we first outline potential mechanistic links between spruce beetle outbreaks and fires based on existing research. Second, we update the theoretical framework used to understand interactions between spruce beetles and fire in subalpine spruce–fir forests. Third, we provide expectations for potential interactions and suggest avenues for further research. Methods We synthesized existing primary literature to categorize the potential mechanisms controlling the interactions between spruce beetles and fire. Results We categorized mechanisms as either substrate mediated or environmentally mediated. Most research investigating the interaction between spruce beetle outbreaks and fire focuses on substrate mediation. There is a need to expand investigations of environmental mediating mechanisms due to the importance of climate and the ability for either disturbance to alter microclimatic conditions. Conclusions Environmentally mediated mechanisms may better elucidate the interactions between spruce beetles and fire than substrate mediated mechanisms because both disturbances require specific environmental conditions, and both can alter environmental conditions that favor a second disturbance. Our understanding of how these mechanisms promote or constrain interactions is limited and warrants future study. Investigating these topics and expanding the scope of research both spatially and temporally may identify additional patterns that increase the predictability of this important disturbance interaction.
 
(a) The experimental design was set up in the LTSER Zone Atelier Plaine & Val de Sèvre, a study area situated in central western France. Oilseed rape plant phytometers were placed in OSR fields (yellow), cereal fields (grey) and grasslands (blue) from 2015 to 2019. (b) In each farmer’s field, two OSR phytometers (circles) were placed, one at the edge and one in the core of the field. Four branches were tested, one with a control treatment, one with a large mesh treatment, one with a small mesh treatment and one with an Osmolux treatment. In OSR, the flowering sequence goes from the base to the apex. Flowers opened during the experiment were delaminated from flowers opened before or after field experiment. Pollination processes involved in each experimental treatment include in the control treatment self-pollination, wind-pollination, small insect-pollination, and large insect-pollination. (c) Plant phytometers were deposited in OSR, cereals or grasslands, field size was used as a proxy of the amount of floral resources at the field scale. %OSR (oilseed rape), %SNH (semi-natural habitats) and %OF (organic farming) were used to estimate ‘Landscape Flower Quantity’. Information about the amount and type of floral resources provided by the habitats (focal or landscape ones) were provided based on literature (Hegland and Boeke 2006; Hardman et al. 2016; Gaba et al. 2020; Bourgeois et al. 2020; Sidemo‐Holm et al. 2021): +  +  + : flower cover > 50%, +  + : flower cover > 10%, + : flower cover > 0%, 0: flower cover = 0%. The amount of flowers provided by wild plants is rather constant (Cole et al. 2017). To account for the temporal variation of OSR flower cover two variables were developed: ‘Phytometer–Regional Flowering Temporal Matching’ described the period of OSR flowering during which phytometers were placed in the fields (during or after OSR flowering peak) and ‘Focal Field–Regional Flowering Temporal Matching’ defined the temporal deviation between flowering in the OSR focal field and OSR flowering in the study site and was thus designed only for OSR fields in contrast to the other three variables
Variation in OSR fruiting success with ‘Phytometer–Regional Flowering Temporal Matching’ in OSR (a), cereals (b) and grasslands (c). Thresholds discriminating ‘Phytometer–Regional Flowering Temporal Matching’ (i.e. during and after OSR flowering / flowering peak) were defined differently for phytometers in OSR fields and for phytometers placed in cereals and grasslands (see ‘Methods’ for details). Quantiles and means (black dots) of raw data are represented and numbers show the sample sizes. Significant differences between levels of ‘Phytometer–Regional Flowering Temporal Matching’ are indicated by asterisks (p-value: *** < 0.001; ** < 0.01 and * < 0.05). Effect sizes: fruiting success was on average greater during OSR peak flowering than after in OSR (0.21 ± 0.05 se; from Table 1a model), in cereals (0.15 ± 0.05; from Table 1b model) and in grasslands (0.19 ± 0.07; from Table 1c model)
Effect of ‘Landscape Flower Quantity’ on fruiting success of plant phytometers. The effects of %OSR (a, b, c), %SNH (d, e, f) and %OF (g–i) were quantified in buffer radii (r value in brackets) estimated by optimization of likelihood outside the OSR fields (a, d, g), cereal fields (b, e, h) and grasslands (c, f, i) in which the phytometers were placed. Dots of different shapes show raw data for each year. The lines show the adjusted relationships and the shaded area the standard error (based on models presented in Table 1). Lines in bold show significant effects (p-value < 0.05) and dotted lines non-significant effects. Effect sizes: in OSR, fruiting success increased with %OSR (mean ± SE: 0.0030 ± 0.0013 per %OSR) and %SNH (0.0126 ± 0.0057 per %SNH) and decreased with %OF (− 0.0034 ± 0.0015 per %OF). Fruiting success increased with %OSR (0.0057 ± 0.0026 per %OSR) in cereals and in grasslands (0.0134 ± 0.0065 per %OSR), while it decreased with %SNH (-0.0305 ± 0.0080 per %SNH) in grasslands
Fruiting success (in %) of self-pollination between crops (a, b) and effect sizes of other pollination processes in OSR fields (c), cereal fields (d) and grasslands (e) and the variation between categories of ‘Phytometer-Regional Flowering Temporal Matching’: during (orange) versus after (green) OSR flowering (peak). Significance of effect size are tested against 0, using unilateral sign tests (p-value: *** < 0.001; ** < 0.01 and * < 0.05 into brackets). Asterisks (p-value: *** < 0.001; ** < 0.01 and * < 0.05) also indicate significant differences between categories of ‘Phytometer-Regional Flowering Temporal Matching’ (see Table 2; c–e). Mean ± se of raw data are presented, numbers indicate the sample sizes. Estimated effects: self-pollination was 0.11 ± 0.05 se more important during the OSR peak flowering than after in OSR fields, and 0.16 ± 0.04 (estimation from Table 2a model) greater during OSR flowering than after in cereals (from Table 2b model). Insect-pollination was 0.27 ± 0.11 se greater during OSR peak flowering than after in OSR (from Table 2a model). Insect-pollination (0.92 ± 0.18) and large insect-pollination (0.88 ± 0.17) were greater after OSR flowering than during, while wind-pollination was 0.73 ± 0.23 more important during OSR flowering than after in grasslands (from Table 2c model)
Variation in the magnitude of pollination processes with ‘Landscape Flower Quantity’ (%OSR: % oilseed rape, %SNH: % semi-natural habitats and %OF: % organic farming) and ‘Phytometer-Regional Flowering Temporal Matching’ in OSR fields (a–f), cereals (g) and grasslands (h–j). The scale of effects was estimated by likelihood optimizations. Raw data is shown for each year, the predicted relationships are based on models presented in Table 2 (bold if a significant effect, or else dotted) and their standard errors. Black colour was used when landscape effect was independent of Phytometer-Regional Flowering Temporal Matching and green and orange colours were used for significant interaction terms
Context Recognized as a critical ecosystem service in farmland, pollination is threatened by the decline of pollinators, notably due the homogenization of the landscape and the decline of floral resources. However, there is still a limited understanding of the interplay between landscape features and the pulses of floral resources provided by mass-flowering crops. Objective The goals of this study were to (i) determine how pollination efficiency varies with the amount of floral resources at field and landscape scales through the oilseed rape (OSR) flowering period and (ii) quantify the magnitude of the pollination processes involved. Methods Pollination efficiency (fruiting success) was measured using OSR plant phytometers placed in grasslands, cereals and OSR fields varying in quantity of floral resources at both field and landscape scales. The individual contributions of different processes to pollination were determined using a bagging experiment on plant phytometers. Results Pollination efficiency was enhanced during both the temporal period and in landscapes with a high amount of OSR flowers, and semi-natural habitats as a result of higher pollinator presence. The bagging experiment also supported a complementarity between habitats for pollinators, as insect-pollination in grasslands and cereals was higher after OSR flowering, especially in OSR-rich landscapes, in regard to large-insect-pollination. Conclusions The floral resource availability drives insect-pollination through attraction, spillover, and spatial and temporal complementarities between habitats. These results suggest that maximizing pollination efficiency in farmland landscapes partly consisting of OSR fields should include a combination of habitats that provide continuous floral resources.
 
Context Plant diversity can sometimes determine the distribution of pests in ecosystems, but the effects of plant diversity at local and landscape scales on the occurrence of pests on novel marginal hosts are still unknown. Objectives We explored the direct effects of plant diversity at local and landscape scales on the colonization of the marginal host walnut by Helicoverpa armigera (Lepidoptera: Noctuidae). Methods We surveyed and compared the occurrence (and damage) of H. armigera in walnut orchards embedded in simple vs. diverse landscapes, and with weeds or without weed cover. The surrounding landscape composition and weed communities in walnut orchards were also investigated to shed light on the mechanisms driving H. armigera responses. Results Diverse landscapes were associated with lower densities of overwintering adult H. armigera, first generation eggs and larvae, as well as with lower infestation rates of walnut fruits. Weed presence in walnut orchards had no significant effects on the abundance of H. armigera adults or eggs, but was associated with higher larvae densities in orchards, in both simple and diverse landscapes. The effect of within-orchard weed cover on larvae was stronger in diverse landscapes. Conclusions Our study demonstrated that landscape composition coupled with local orchard ground cover vegetation mediated the occurrence of H. armigera on a marginal host walnut. Monoculture production increases walnut’s exposure to the pest and may accelerate its evolutionary adaptation to this poor host; weed cover in individual orchards may increase larval density by providing floral resources for adults.
 
Context The cane toad ( Rhinella marina ) is one of the most globally significant and well-studied invasive alien species, and the detrimental impacts of its invasions warrant the design and application of decision support tools. While many models have been developed for guiding policies addressing cane toad invasions, none reliably predict the species’ population dynamics at scales relevant to on-the-ground management. Objectives We describe virToad —an individual-based life-history simulator of the cane toad. We then illustrate virToad ’s ability to forecast the cane toad’s spatiotemporal population dynamics at local- to landscape-scales, and its potential for improving management responses to cane toad invasions. Methods We designed virToad to make population dynamics an emergent consequence of the cane toad’s fitness-maximising behavioural responses to mechanistic constraints (e.g., water availability, kin selection), and to management actions. We used virToad to simulate cane toad population dynamics in the absence of management, and under alternative management strategies implemented across a spectrum of effort: hand-capturing and trapping of juveniles and adults, fencing waterbodies, and trapping and chemically suppressing tadpoles. Results virToad produced plausible predictions of cane toad population densities, detection probabilities, distributions, and spatial segregation. Simulation experiments indicated that the efficacy of competing management actions varied significantly, and that only moderate to high effort hand-capturing and trapping of juveniles and adults had the potential to suppress invasions. Conclusion virToad is an open-source, rigorous, and extensible decision support platform that will enable researchers and practitioners to defensibly forecast local- to landscape-scale cane toad spatiotemporal population dynamics and management outcomes.
 
Maps of study area showing patterns of the 10 landscape variables used in the Circuitscape-based ResistanceGA analysis, organized by categories according to Table 1
Map of sample sites used in population structure analysis. Sample sites are shown as pie graphs with the colors representing the average proportion of assignment probability to one of the K = 6 genetic clusters as estimated by discriminant analysis of principal components (DAPC) for individuals in that sample site. The six sample sites that were dropped due to insufficient numbers of individuals that passed quality control filters are represented by black points. Background shows USGS NLCD land use categories. DAPC results for Discriminant Functions 1 and 2 with K = 6 genetic clusters are presented in a scatterplot graph below the map
Plots of three pairwise genetic distances against geographic distance. All genetic distances had a significant relationship with geographic distance, indicating a pattern of isolation by distance among the sample sites. Shaded area is 95% confidence interval
Maps of the study area showing the weighted model average landscape resistances for models based on GST genetic distances (A) and DJ genetic distances (B)
Context Agricultural land-use conversion has fragmented prairie wetland habitats in the Prairie Pothole Region (PPR), an area with one of the most wetland dense regions in the world. This fragmentation can lead to negative consequences for wetland obligate organisms, heightening risk of local extinction and reducing evolutionary potential for populations to adapt to changing environments. Objectives This study models biotic connectivity of prairie-pothole wetlands using landscape genetic analyses of the northern leopard frog ( Rana pipiens ) to (1) identify population structure and (2) determine landscape factors driving genetic differentiation and possibly leading to population fragmentation. Methods Frogs from 22 sites in the James River and Lake Oahe river basins in North Dakota were genotyped using Best-RAD sequencing at 2868 bi-allelic single nucleotide polymorphisms (SNPs). Population structure was assessed using STRUCTURE, DAPC, and fineSTRUCTURE. Circuitscape was used to model resistance values for ten landscape variables that could affect habitat connectivity. Results STRUCTURE results suggested a panmictic population, but other more sensitive clustering methods identified six spatially organized clusters. Circuit theory-based landscape resistance analysis suggested land use, including cultivated crop agriculture, and topography were the primary influences on genetic differentiation. Conclusion While the R. pipiens populations appear to have high gene flow, we found a difference in the patterns of connectivity between the eastern portion of our study area which was dominated by cultivated crop agriculture, versus the western portion where topographic roughness played a greater role. This information can help identify amphibian dispersal corridors and prioritize lands for conservation or restoration.
 
Context - Disentangling the effect of environment and biological interaction on community composition with observational data, within the environmental filtering framework, is challenging because the two processes produce non independent results. Objectives - Adopting community N-mixture models with symmetric interactions, we aimed at estimating differential effects of landscape structure and biotic interactions on the local abundance of a Mediterranean reptile community including four lizards (Lacerta bilineata; Podarcis siculus; P. muralis; Chalcides chalcides) and two snakes (Hierophis viridiflavus; Natrix Helvetica). Methods - We sampled reptiles for three consecutive years (2019–2021; 4 surveys/year) on 52 linear transects on a Mediterranean coastal landscape. We analyzed count data by means of a multi-species N-mixture model with symmetric interactions. Interactions within pair of species were estimated from the residual correlation of their realized abundances, after accounting for four landscape features: landscape heterogeneity calculated from land cover data, edge density of woody vegetation patches, tree cover density, net primary productivity. Results - Most species displayed very low detection probability (p ~ 0.10). All species responded with different intensity and sensitivity to landscape predictors. Two biological interactions resulted significant: L. bilineata and P. siculus showed a positive interaction, while P. muralis and C. chalcides displayed a negative interaction. Conclusions - Using community N-mixture models we demonstrated that, also with observational data obtained from a realized community, partitioning the filtering process of the landscape from the one of biotic interactions is possible.
 
Map of study sites, showing location of study area within South Africa. N north-orientated transects, S south-orientated transects, Ref reference sites within corridor interiors (> 100 m from nearest plantation edge)
Depiction of transect position in relation to plantation edge, with the austral north-facing grassland edge exposed to sunshine in comparison with the shaded austral south-facing grassland edge
Model predicted marginal effects of edge orientation on abundance of all species and residents, as well as grassland specialist species richness and abundance, and abundance of forest specialists. S shady austral south-orientated sites, N sunny austral north-orientated sites
ContextHabitat edges are integral features of conservation corridors and can influence corridor function and effectiveness. Edge orientation is linked to corridor design and can shape edge responses by changing habitat conditions along edges as well as contrast between conserved habitats and transformed areas.Objectives We assess whether corridor orientation affects butterfly assemblages in conservation corridors. To do this, we investigate how edge orientation influences butterfly diversity and abundance along forestry plantation edges, and compare this to another important design variable, corridor width.Methods Butterflies were recorded along the sunny austral north- and shady austral south-orientated edges in grassland conservation corridors that dissect forestry plantations, as well as corridor interior sites. Species richness, abundance and similarity to interior sites were modelled using local habitat variables (ambient temperature, floral resources, and time of day), as well as corridor design variables (corridor width, orientation and an estimate of edge contrast influenced by orientation).ResultsBoth edge orientation and corridor width were important for butterfly diversity along corridor edges. Wider corridors enhanced overall species richness and promoted similarity between edge and interior habitats. Concurrently, grassland specialist species preferred the sunnier edges (i.e., north facing in the southern hemisphere) while forest- specialists showed a preference for the shadier edges (south facing edges). Edge orientation influenced resident butterflies more strongly than transient butterflies and influenced specialists more strongly than generalists.Conclusions Corridor orientation and width are complementary design variables for butterfly conservation. Wide corridors at a variety of orientations benefit different subsets of the butterfly assemblage, and the whole corridor (including both edges) is important to consider in conservation planning to capture all biodiversity.
 
ContextSeveral studies have explored using Boltzmann entropy to quantify the configurational complexity of landscapes. Additionally, these studies claimed that the Boltzmann entropy provides a link between landscape ecology and thermodynamics. However, these claims remain unsubstantiated.Objectives The goal of this perspective paper is to discuss whether such claims have merit. This includes checking whether the method of calculating the Boltzmann entropy adheres to the statistical mechanics (SM) framework.Methods and resultsThe paper starts by recalling crucial concepts of SM. An illustrative example of a system similar to that used to derive Boltzmann entropy of landscapes and adherent to the SM framework is given and fully analyzed. Next, the system used to derive Boltzmann entropy of landscapes is analyzed and compared to the analysis of the illustrative example.Conclusion Computation of the Boltzmann entropy in the context of landscapes does not comply with SM. Therefore, although the Boltzmann entropy is a metric of a landscape’s configurational complexity, it does not provide a link between landscapes and thermodynamics. In addition, claims that the Shannon entropy cannot be used to assess the configurational complexity of landscape are incorrect.
 
Conservation units (shaded polygons) for eastern indigo snakes (EIS, Drymarchon couperi) across their historical distribution. Units with and without recent (2000–2020) eastern indigo snake records are denoted. Open polygons represent eastern indigo snake population units identified in the U.S. Fish and Wildlife Service’s (USFWS) Species Status Assessment
U.S. Fish and Wildlife Service eastern indigo snake (Drymarchon couperi) Representative Units
Median root mean squared error (RMSE) plotted against the bandwidth (σ) of a half-normal standard kernel under linear and negative exponential functional relationships between habitat suitability and resistance for eastern indigo snakes (Drymarchon couperi) on Fort Stewart, Georgia, USA. The exponential parameter is given as c. Results are presented for wetland constant = 0.50 (see Supplementary Material, Fig. S3 for all results). The horizontal black line represents the lowest median RMSE across all scenarios
Conservation units for the eastern indigo snake (Drymarchon couperi) were delineated from a habitat suitability surface (A). The habitat suitability surface was modified to better account for snake use of wetlands and the potential barrier effects of roads (B), and this modified surface was then used to create a resistance surface (C). Landscape connectivity was modeled using this resistance surface (D). The black arrow in Fig. 4D highlights the Alapaha River corridor
Context Wildlife distributions are often subdivided into discrete conservation units to aid in implementing management and conservation objectives. Habitat suitability models, resistance surfaces, and resistant kernels provide tools for delineating spatially explicit conservation units but guidelines for parameterizing resistant kernels are generally lacking. Objectives We used the federally threatened eastern indigo snake (Drymarchon couperi) as a case study for calibrating resistant kernels using observed movement data and resistance surfaces to help delineate habitat-based conservation units. Methods We simulated eastern indigo snake movements under different resistance surface and resistant kernel parameterizations and selected the scenario that produced simulated movement distances that best approximated the maximum observed annual movement distance. We used our calibrated resistant kernel to model range-wide connectivity and compared delineated conservation units to Euclidean distance-based population units from the recent eastern indigo snake species status assessment (SSA). Results We identified a total of 255 eastern indigo snake conservation units, with numerous large (2500–5000 ha of suitable habitat) conservation units across the eastern indigo snake distribution. There was substantial variation in the degree of overlap with the SSA population units likely reflecting the spatial heterogeneity in habitat suitability and landscape resistance. Conclusion Our calibration approach is widely applicable to other systems for parameterizing biologically meaningful resistant kernels. Our conservation units can be used to prioritize future eastern indigo snake conservation efforts, identify areas where more survey work is needed, or identify small, isolated populations with high extinction risks.
 
Two research areas to the north and south-west of Bangalore displaying the location of plots sampled along the rural–urban interface of Bangalore, Southern India (N=127\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N=127$$\end{document}). Panels on the right show zoomed-in representations of the grey-shaded squares in the large map. The village and plot coordinates were collected during the household survey in 2018. All other map features were downloaded from OpenStreetMap and visualized with QGIS
Descriptive statistics for the dependent variables and agricultural input use ( n = 127)
Context The agricultural landscape in many low- and middle-income countries is characterized by smallholder management systems, often dependent on ecosystem services, such as pollination by wild pollinator populations. Increased adoption of modern inputs (e.g., agrochemicals) may threaten pollinators and smallholder crop production. Objective We aimed to identify the link between the use of agrochemicals and wild bee populations in Southern India, while explicitly considering the effects of temporal and spatial scaling. Methods For our empirical analysis, we combined data from pan trap samples and a farm management survey of 127 agricultural plots around Bangalore, India. We implemented a Poisson generalized linear model to analyze factors that influence bee abundance and richness with a particular focus on the present, past, and neighboring management decisions of farmers with respect to chemical fertilizers, pesticides, and irrigation. Results Our results suggest that agricultural intensification is associated with a decrease in the abundance and richness of wild bees in our study areas. Both time and space play an important role in explaining farm-bee interactions. We find statistically significant negative spillovers from pesticide use. Smallholders’ use of chemical fertilizers and irrigation on their own plots significantly decreases the abundance of bees. Intensive past management reduces both bee abundance and richness. Conclusions Our results suggest that cooperative behavior among farmers and/or the regulation of agrochemical use is crucial to moderate spatial spillovers of farm management decisions. Furthermore, a rotation of extensive and intensive management could mitigate negative effects.
 
a Historical fire perimeters in the East Kootenays, British Columbia, Canada from 1919–2019. b Annual number of fires, area burned (ha), and mean fire size (ha) by fire cause (lighting and human) derived from historical fire perimeter records in the East Kootenays, British Columbia, Canada from 1919–2019
Breakpoints (dashed lines) and model predictions (black lines) for annual number of fires, area burned (ha), and mean fire size (ha) by fire cause in the East Kootenays, British Columbia, Canada from 1919–2019 (see Table 3)
Using breakpoint analysis, we identify three distinct phases of fire activity: (i) active but altered fire from 1919–1939, (ii) a fire suppression era from 1940–2002, (iii) the modern era of wildfire from 2003–present
a Time since a fire event (years) and b number of burn events since 1919 (reburns) derived from historical fire perimeter records in the East Kootenays, British Columbia, Canada from 1919–2019. The majority of the flammable landscape (89-percent) has not experienced a fire event since 1959
a Historical fire regime classification and b historical fire return interval (FRI), derived from fire history reconstructions in the East Kootenays, British Columbia, Canada (see Table 1). c Fire regime interval departure (FRID) for the flammable landscape (non-fuel shown in grey), calculated as the current (1919–2019) FRI divided by the historical (1200–1850) FRI (see Table 5). Values between 0–1 indicate no fire deficit; values ≥ 1 indicate the number of fires missed. 46.4-percent of the flammable landscape is in a fire deficit, missing between 1 and 10 fires since 1919
Context In fire-excluded forests across western North America, recent intense wildfire seasons starkly contrast with fire regimes of the past. The last 100 years mark a transition between pre-colonial and modern era fire regimes, providing crucial context for understanding future wildfire behavior. Objectives Using the greatest time depth of digitized fire events in Canada, we identify distinct phases of wildfire regimes from 1919 to 2019 by evaluating changes in mapped fire perimeters (> 20-ha) across the East Kootenay region (including the southern Rocky Mountain Trench), British Columbia. Methods We detect transitions in annual number of fires, burned area, and fire size; explore the role of lightning- and human-caused fires in driving these transitions; and quantify departures from historical fire frequency at the regional level. Results Relative to historical fire frequency, fire exclusion has created a significant fire deficit in active fire regimes, with a minimum of 1–10 fires missed across 46.4-percent of the landscape. Fire was active from 1919 to 1939 with frequent and large fire events, but the regime was already altered by a century of colonization. Fire activity decreased in 1940, coinciding with effective fire suppression influenced by a mild climatic period. In 2003, the combined effects of fire exclusion and accelerated climate change fueled a shift in fire regimes of various forest types, with increases in area burned and mean fire size driven by lightning. Conclusions The extent of fire regime disruption warrants significant management and policy attention to alter the current trajectory and facilitate better co-existence with wildfire throughout this century.Graphical abstract
 
Context Spatial variation in life history traits plays a crucial role in shaping the current and future dynamics of populations, particularly in systems where expanding hybrid zones could further shape population structure. The demographic responses of local populations to fine-scale habitat heterogeneity have consequences for species at a broader scale and demographic responses often vary across spatial scales. Objectives We evaluated spatial variation in population size and demographic traits (e.g., survival, individual growth, movement, and reproduction) of a montane endemic species of lungless terrestrial salamander across elevation and stream distance gradients representing broad and fine spatial scales, respectively. Methods Using 4 years of mark-recapture and count data from the Plethodon shermani × P. teyahalee hybrid system, whereby phenotypic hybrids occur at mid-elevations between low and high elevation congeners, we modeled demographic rates across environmental gradients and spatial scales using a combination of tools including individual growth models, and a spatially explicit Cormack-Jolly Seber model and Integrated Population Model. Results We found that high elevation animals grow faster and move more, especially far from streams, likely as a result of local microclimate conditions. Survival was highest but recruitment rates were lowest at low elevations and significantly declined with distance to stream. We also found that phenotypic hybrids at low elevations had higher survival probabilities. Conclusions Our study reveals nuanced spatial variation in demographic rates that differ in magnitude depending on the scale at which they are assessed. Our results also suggest that animals exhibit demographic compensation across abiotic gradients, underscoring the need for future conservation and management efforts to implement spatially explicit and dynamic strategies to match the demographic variation exhibited by populations across space.
 
Graphical hypotheses regarding the effects of landscape structure, environmental characteristics, and effective population size on genetic parameters of Cecropia hololeuca. A predictions at the node level. Landscapes with higher forest cover (%FC; i), compositional heterogeneity (CH; i), effective population size (Ne; i), slope and elevation (ii) have higher genetic variability (AR, He;A1) and lower inbreeding (f;A2). Landscapes with higher pasture cover (%PC; iii), number of patches (NP; iii), and higher average patch shape index (APSI; iii) have lower AR and He (A1) and higher f (A2). Landscapes with dominance of permeable matrices have higher genetic variability (AR, He) than landscapes with dominance of less permeable matrices (A3). Landscapes with dominance of permeable matrices have lower inbreeding (f) than landscapes with dominance of less permeable matrices (A4). Matrices such as pasture and Eucalyptus have higher permeability than matrices such as sugarcane and other crops. B predictions at the link level. Higher land cover resistance (i), geographic distance (ii), and slope and elevation (iii) strengthen genetic differentiation between populations
Geographic distribution of the 18 landscapes and sampling sites of Cecropia hololeuca in the Atlantic Forest. A The Atlantic Forest ecoregion in Brazil (gray), the red square indicates the region where the genetic data were sampled. B Example of how we identified the individuals of Cecropia hololeuca to define the sampling sites using Google Earth images. C The multi-scale sampling design used to calculate the landscape metrics at node level, where landscapes are represented by buffers of 250, 500, 1000, 1500, 2000, and 2500 m of radius. The set of buffers correspond to one sampling site. D The land cover composition of the study area and location of each sampled site (N = 18) represented by black dots
Relationship between allelic richness and matrix dominance at 1500 m spatial scale in Cecropia hololeuca considering 18 landscapes in the Atlantic Forest ecoregion, Brazil. Boxplot represents the median (dark line), the first and third quartile (box), and the minimum (lower bar) and maximum values (upper bar). Only the pasture and eucalyptus were the dominant matrices within 18 landscapes at 1500 m of spatial scale
Response curves of resistance values based on slope and elevation after optimization of continuous surfaces using Resistance GA for 18 pairs of populations of Cecropia hololeuca sampled in the Atlantic Forest ecoregion. The X axis represents the raw values of slope or elevation observed on the continuous surfaces and the Y axis represents the transformed values after optimization. Bars along the axis represent the distribution of resistance values. A Resistance values based on Inverse Monomolecular transformation of the slope surface on pairwise FST. B Resistance values based on Ricker transformation of the elevation surface on pairwise FST. C Resistance values based on Inverse Monomolecular transformation of the slope surface on pairwise GST. D Resistance values based on Ricker transformation of the elevation surface on pairwise GST
Connectivity surfaces based on random-walk commute times considering elevation, slope, and land cover types for genetic differentiation between 18 populations of Cecropia hololeuca in the Atlantic Forest ecoregion, based on genetic algorithm implemented in ResistanceGA. A Surfaces for FST: B Surfaces for GST. Black points are the centroids of the sampling sites
Context Despite the importance of secondary forests for the maintenance of biodiversity, the impact of pioneer trees on habitat loss and fragmentation is poorly understood. Objectives We analyzed the effects of landscape structure on genetic variability (node level analyses) and genetic differentiation (link level analyses) of the pioneer tree Cecropia hololeuca in the Atlantic Forest of southeastern Brazil. At the node level, we analyzed the effects of landscape structure (forest and pasture amount, compositional heterogeneity, number and shape of patches, and matrix dominance), topography (slope and elevation), and effective population size (Ne) on allelic richness (AR), genetic diversity (He) and inbreeding coefficient (f). At the link level, we analyzed the effect of four resistance surfaces (isolation by geographic distance, land cover, elevation, and slope) on genetic differentiation between populations (FST, G’ST, Jost’s D). Methods We genotyped 257 individuals of C. hololeuca using eight microsatellite loci. At the node level, we calculated landscape and topographic variables at six spatial scales. At the link level, we optimized the resistance surfaces using ResistanceGA. We used a model selection approach to select the most parsimonious models. Results At the node level, matrices dominated by pastures had higher AR than landscapes dominated by Eucalyptus at the 1500 m spatial scale. At the link level, FST was best explained by slope, with lower values imposing more resistance. All resistance surfaces explained G’ST. Conclusions In addition to conserving and restoring habitats, management practices that improve matrix permeability should be adopted to favor the movement of dispersers, consequently improving natural regeneration, increasing allelic richness, and decreasing genetic differentiation of natural populations.
 
Example of a resistance surface used in Elliot et al. (2014a). The surface was created using a path-selection function with telemetry data gathered from dispersing lions
Landscape connectivity, the extent to which a landscape facilitates the flow of ecological processes such as organism movement, has emerged as a central focus of landscape ecology and conservation science. Connectivity modelling now encompasses an enormous body of work across ecological theory and application. The dominant connectivity models in use today are based on the framework of ‘landscape resistance’, which is a way of measuring how landscape structure influences movement patterns. However, the simplistic assumptions and high degree of reductionism inherent to the landscape resistance paradigm severely limits the ability of connectivity algorithms to account for many fundamental aspects of animal movement, and thus greatly reduces the effectiveness and relevance of connectivity models for conservation theory and practice. In this paper, we first provide an overview of the development of connectivity modelling and resistance surfaces. We then discuss several key drivers of animal movement which are absent in resistance-based models, with a focus on spatiotemporal variation, human and interspecies interactions, and other context-dependent effects. We look at a range of empirical studies which highlight the strong impact these effects have on movement and connectivity predictions. But we also provide promising avenues of future research to address this: we discuss newly emerging technologies and interdisciplinary work, and look to developing methodologies, models and conversations which move beyond the limiting framework of landscape resistance, so that connectivity models can better reflect the complexities and richness of animal movement.
 
Example of two TartanGraphs. Habitat-patch cells displayed in blue and LLE corridor cells in violet. Matrix cells in white. Both measure 188 cells in width and 221 cells in length. Habitat patches are both 17 cells long and connecting-edges are 177 cells long. All LLEs are two cells wide. Left: TartanGraph consisting of five connecting-edges and three transecting-edges. Right: TartanGraph consisting of three connecting-edges and seven transecting-edges
The per-individual Traversal-Probabilities of 25 TartanGraphs. Each point represents a single TartanGraph with a unique combination of numbers of connecting (indicated by the key) and transecting edges (x-axis). Error bars indicate standard deviation of Probability across three replicate simulations under DP 7.5 treatment. Generally negative trend in Traversal-Probability with increasing transecting-edge number is clearly contrasted with a positive effect of greater connecting-edge quantities
The mean Traversal-Probability recorded from 48 TartanGraphs in response to connecting-edge width (x-axis) and movement correlation (DP parameter; coloured). Each point shows mean Probability of sixteen TartanGraphs with varying numbers of both edge types. Bars indicate standard error. Results show positive trend in likelihood of inter-patch dispersal and corridor width only for highly correlated movers (blue). Individuals parameterised to change direction more frequently fail to show same benefit from widening of connecting-edges and show very limited inclines or declines in connectivity in response to wider corridors (red, orange)
Both panels: Mean Traversal-Probability across twelve TartanGraphs under DP 7.5. X-axis indicates number of connecting-edges in each landscape and their cellular widths by colour. Error bars show standard deviation between replicate three simulations and points are offset along the x-axis to improve clarity. Left: all TartanGraphs with zero transecting-edges. Inter-patch dispersal rates show considerable overlap between corridor width treatments and no treatment appears constantly better for connectivity than the others in the absence of intersecting LLEs. Right: all TartanGraphs with 15 transecting-edges. Dispersal rates are consistently higher when TartanGraphs bear wider connecting-edges
Mean Traversal-Probability of 36 TartanGraphs under DP 7.5. Each point indicates the mean Traversal-Probability of four TartanGraphs sharing the same number of transecting-edges (X-axis) and cellular width of transecting-edges (coloured). Bars indicate standard error within TartanGraph groupings. A consistently negative correlation between Probability and both explanatory variables is apparent, whereby a greater quantity or diameter of transecting-edges reduced an individual’s chance of inter-patch dispersal
Context Linear landscape elements (LLEs) such as ditches and hedgerows can increase the ecological connectivity of habitat embedded within agricultural areas by acting as corridors for animal movement. However, we lack knowledge on how the spatial arrangement of LLEs influence dispersal, impeding our ability to offer robust advice on how best to add new LLEs to improve connectivity. Objectives To examine how the width and spatial orientations of LLEs composing an intersecting network might influence connectivity across landscapes. Methods We used an individual-based dispersal model to simulate the stochastic movement of small organisms through stylised LLEs of different characteristics. Landscapes were composed of two habitat patches separated by a grid-like network of LLEs composed of two types: (1) connecting-edges (touching patches on either end) and (2) transecting-edges (running perpendicular to connecting-edges). By altering numbers and widths of each LLE type we sought to understand the effect of these variables on inter-patch dispersal rates. Results Increasing the number or width of connecting-edges improved connectivity but, conversely, increasing numbers or widths of transecting-edges reduced it. The greater freedom of movement offered by increasing numbers of transecting-edges may have inhibited connectivity, as individuals with limited perceptual-range were more likely to become trapped in complex networks and thus fail to navigate to suitable habitat patches. Conclusions Orientation of LLEs with respect to landscape resources greatly affects their impact on connectivity. The addition of LLEs to landscapes may decrease their connectivity for small, flightless species if they do not directly channel dispersers toward landscape resources.
 
Context Cross-scale analyses are central to understanding patterns and processes in hierarchically structured ecological systems. Systematic conservation planning has progressed in recent years, but the utility of cross-scale planning efforts has received little valuation. Objectives Our goal was to develop and evaluate a scale-linked framework to prioritize spatial units for conservation. We sought to compare the spatial configuration and cost-efficiency of a conservation network designed using data collected and analyzed at two spatial scales (e.g., both regional and local) with that produced using a more traditional single-scale approach (e.g., local). Methods We sampled macroinvertebrate communities from 48 representative streams within the Congaree Biosphere Region in 2019. We developed random forest models to predict distributions of community-level metrics at regional (subwatershed) and local (local catchment) spatial scales. Finally, we prioritized planning units according to their conservation value, relative to three biotic metrics, under two different scenarios: a traditional ‘single-scale’ and novel ‘scale-linked’ approach. Results Spatial differences between our single-scale and scale-linked scenarios were apparent. On average, solutions produced by our scale-linked scenario were 4.96% less costly and required 4.71% fewer planning units than our single-scale scenario. Scale-linked solutions were penalized an average of 15.90% less than single-scale solutions, reflecting a greater capacity to adequately represent the biotic metrics of interest. Conclusion Our comparisons suggest that scale-linked approaches can decrease cross-scale disparities and better reflect hierarchical processes without sacrificing planning efficiency. Thus, scale-linked conservation planning may help ease implementation efforts while enhancing the long-term resilience and sustainability of landscapes surrounding protected areas.
 
Context Large-scale programs for eradication of pest mammals are confronted with the challenge of managing reinvasion. Exploiting high-elevation landscape features that naturally limit the rate of reinvasion is a strategy that is presumed to improve the success of such initiatives, however, the efficacy of doing so has not yet been investigated. Objectives We aimed to assess whether high-elevation landforms limit the movements of 10 species of invasive small mammal in New Zealand to such a degree that they could be exploited in landscape-scale eradication programmes. Methods We determined the upper elevation limits of species’ distributions, and made spatial predictions based on occupancy models. We applied these in concert to a 310,000 ha area of rugged mountainous environments and identified landforms that function as dispersal barriers to each species of interest. We validated our predictions with existing presence/absence and GPS movement data, and tested our predictions of high-elevation landform barriers with the GPS movement data of a sample of European hedgehogs ( Erinaceus europaeus ). Results We found that the extent of barriers which limited movement ranged from widespread (5/10 species), to localised, (3/10 species) to limited (2/10 species). Our predictions of hedgehog movement barriers were strongly supported by GPS movement data of 26 hedgehogs that were tracked in the study area. Conclusions Our findings show there is enormous potential to advance landscape-scale eradication of invasive small mammals in areas adjacent to high-elevation landforms by identifying and exploiting landscape features that limit the movement of target species in the strategies of eradication programmes.
 
Context Seagrasses are submerged marine plants that have been declining globally at increasing rates. Natural resource managers rely on monitoring programs to detect and understand changes in these ecosystems. Technological advancements are allowing for the development of patch-level seagrass maps, which can be used to explore seagrass meadow spatial patterns. Objectives Our research questions involved comparing lacunarity, a measure of landscape configuration, for seagrass to assess cross-site differences in areal coverage and spatial patterns through time. We also discussed how lacunarity could help natural resource managers with monitoring program development and restoration decisions and evaluation. Methods We assessed lacunarity of seagrass meadows for various box sizes (0.0001 ha to 400.4 ha) around Cat Island and Ship Island, Mississippi (USA). For Cat Island, we used seagrass data from 2011 to 2014. For Ship Island, we used seagrass data for seven dates between 1963 and 2014. Results Cat Island, which had more continuous seagrass meadows, had lower lacunarity (i.e., denser coverage) compared to Ship Island, which had patchier seagrass beds. For Ship Island, we found a signal of disturbance and path toward recovery from Hurricane Camille in 1969. Finally, we highlighted how lacunarity curves could be used as one of multiple considerations for designing monitoring programs, which are commonly used for seagrass monitoring. Conclusions Lacunarity can help quantify spatial pattern dynamics, but more importantly, it can assist with natural resource management by defining fragmentation and potential scales for monitoring. This approach could be applied to other environments, especially other coastal ecosystems.
 
Extent of the Downeast and Midcoast growing regions within the Maine, USA, lowbush blueberry production landscape. Map insets display representative landscape contexts of the a Downeast (light gray) and b Midcoast (dark gray) growing regions. Bar charts indicate proportion of eight land cover types in the Downeast (top) and Midcoast (bottom) growing regions
Bee species richness in power line corridor sites in two Maine, USA, lowbush blueberry growing regions within four body size classes: small (< 6 mm), medium (6–9 mm), large (9–12 mm), and extra-large (> 12 mm) (Russell et al. 2018), 2014–2015. **significant at p < 0.01 and ***significant at p < 0.001
Species richness of a social and solitary wild bees and b ground and cavity nesting wild bees in power line corridor sites in Downeast and Midcoast Maine, USA, 2014–2015. *significant at p < 0.05, **significant at p < 0.01, and ***significant at p < 0.001
Interactive effect of percent lowbush blueberry surrounding power line sampling sites at three spatial scales on solitary bee species richness in the Midcoast (gray circles, dashed line) and Downeast (black triangles, solid line) growing regions of the Maine, USA lowbush blueberry production landscape, 2014–2015
Species richness of a social and solitary and b ground and cavity nesting wild bees in power line corridor sites in sites isolated from and near to lowbush blueberry fields in Downeast and Midcoast Maine, USA, 2014–2015. *significant at p < 0.05
Context Power line corridors have been repeatedly assessed as habitat for wild bees; however, few studies have examined them as bee habitat relative to nearby crop fields and surrounding landscape context. Objectives We surveyed bee communities in power line corridors near to and isolated from lowbush blueberry fields in two landscape contexts in Maine, U.S.A. We examined the influences of blooming plant abundance and diversity and bee life-history traits including sociality, nesting preference, and body size. Methods We surveyed wild bees and blooming plants in power line corridors from 2013 to 2015. We calculated landscape composition surrounding sites at multiple scales and gathered bee trait information from the literature. We assessed differences in bee communities owing to landscape context with generalized linear models. Results We collected 125 wild bee species and observed a rare plant-pollinator relationship within power line corridors. We found greater bee abundance and species richness throughout a complex, resource-rich landscape, while mass-flowering lowbush blueberry fields enhanced bee species richness only in a simple, resource-poor landscape. Landscape composition and blooming plant diversity varied with landscape context, though only landscape composition influenced bee communities. Solitary and ground-nesting species were more sensitive to landscape context than social or cavity-nesting species. Conclusions Power line corridors provide crucial refugia for crop pollinating wild bees in agricultural landscapes with resource-poor natural habitat, while bees may selectively forage in power line corridors within agricultural landscapes containing resource-rich natural habitat. We found high-quality forage within corridors; quantifying nesting resources could clarify corridor use by wild bees.
 
Statistically significant simplified ecological niche models (S3ENM) for aAraucaria araucana;bAustrocedrus chilensis; cFitzroya cupressoides; dPilgerodendron uviferum north and d′Pilgerodendron uviferum south; eLepidothamnus fonkii north; e′Lepidothamnus fonkii south; f: Podocarpus nubigenus; gPodocarpus salignus; hPrumnopitys andina; iSaxegothaea conspicua east and i′Saxegothaea conspicua west. Grey line splits divergent ecological conditions
Examples of pairwise niche equivalency tests and niche overlap results among sympatric and parapatric species. aAustrocedrus chilensis and Prumnopitys andina are sympatric and their niches are equivalent and overlapped, bFitzroya cupressoides and Prumnopitys andina are parapatric and their niches are equivalent and overlapped, cAraucaria araucana and Prumnopitys andina are sympatric, and their niches are not equivalent nor overlapped, dLepidothamnus fonkii show divergent ecological condition between its north and south sector. 1. Principal component analysis (multivariate space), showing the density occurrence of each pair of species (denser shading indicates higher density) and available environments (continuous line = 100%, dashed line = 75%) for each species (different colors for each species); overlapped area between niches in grey, for each comparison, respectively. 2. Represents the equivalency frequency test estimated by Schoener's D index and p value, for each comparison, respectively. The observed Schoener's D metric (D obs.) is indicated in red with a dot and vertical line
Areas of moderate to high overlapping suitability for Patagonian Temperate Forest gymnosperm species (yellow—red) and preserved areas (in blue). a: Detailed overlapping suitability for Podocarpus salignus, Araucaria araucana and Prumnopitys andina in the Costal Range from -35° to -40° latitude; b: Details overlapping suitability for Austrocedrus chilensis, Fitzroya cupressoides and Saxegothaea conspicua west and east from − 39° to − 43° latitude; C: Moderate to high overlapping suitability for nine gymnosperm species and preserved areas of PTF
Context Biodiversity hotspots harbor 77% of endemic plant species. Patagonian Temperate Forest (PTF) is a part of a biodiversity hotspot, but over the past centuries, has been over-exploited, fragmented and replaced with exotic species plantations, lately also threatened by climate change. Objectives Our aim is to better understand patterns of habitat suitability and niche overlap of nine endemic gymnosperm species, key elements of the PTF, complementing traditional approaches of biodiversity conservation. Methods Using R packages and 3016 occurrence data, we deployed ecological niche models (ENM) in MaxEnt via kuenm, and classified species according to Rabinowitz’s types of rarity. We then overlapped their niches calculating Schoener's D index, and considered types of rarity in a spatial ecological context. Finally, we overlay high species’ suitability and protected areas and detected conservation priorities using GapAnalysis. Results We generated simplified ENMs for nine Patagonian gymnosperms and found that most niches overlap, and only one species displayed a unique niche. Surprisingly, we found that three species have divergent suitability of habitats across the landscape and not related with previously published geographic structure of neutral genetic variation. We showed that the rarer a species is the smaller niche volume tend to have, that six out of nine studied species have high conservation priority, and that there are conservation gaps in the PTF. Conclusion Our approach showed that there are unprotected suitable areas for native key species at high risk in PTF. Suggesting that integrating habitat-suitability models of multiple species, types of rarity, and niche overlap, can be a handy tool to identify potential conservation areas in global biodiversity hotspots.
 
Context Connectivity between habitat patches is a recognized conservation action to conserve biodiversity in a rapidly changing world. Resistance surfaces, a spatial representation of cost of movement across the landscape, are often the foundation for connectivity analyses but working with them can be daunting due to the diversity and complexity of software tools. Objectives We present an overview of the steps involved when working with resistance surfaces, identify tools that perform specific tasks, evaluate user experience with the tools, identify needs of the user community, and present some recommendations for users and developers. Methods We identified tools applicable at each of the three steps (i) preparing data, (ii) constructing and optimizing surfaces, and (iii) using resistance surfaces. We conducted an online survey of the connectivity user community to assess the popularity and experience with tools on five criteria and identified characteristics important in the selection of connectivity tools. Results We reviewed a total of 43 tools, of which 10 are useful for data preparation, 27 allow construction, and 30 tools that use resistance surfaces. A total of 148 survey participants working in 40 countries were familiar with 37 tools. Tools are ranked heterogeneously for the five criteria. Crucial avenues for future development of connectivity tools identified by respondents are incorporation of uncertainties, dynamic connectivity modelling, and automated parameter optimization. Conclusions Since resistance surfaces are used for a variety of applications, it is important that users are aware about the appropriate tools. We anticipate that future tools for connectivity research will incorporate more complex and biologically more realistic analytical approaches.
 
Context Maps of C3 and C4 plant abundance and stable carbon isotope values (δ¹³C) across terrestrial landscapes are valuable tools in ecology to investigate species distribution and carbon exchange. Australia has a predominance of C4-plants, thus monitoring change in C3:C4 cover and δ¹³C is essential to national management priorities. Objectives We applied a novel combination of field surveys and remote sensing data to create maps of C3 and C4 abundance in Australia, and a vegetation δ¹³C isoscape for the continent. Methods We used vegetation and land-use rasters to categorize grid-cells (1 ha) into woody (C3), native herbaceous, and herbaceous cropland (C3 and C4) cover. Field surveys and environmental factors were regressed to predict native C4 herbaceous cover. These layers were combined and a δ¹³C mixing model was used to calculate site-averaged δ¹³C values. Results Seasonal rainfall, maximum summer temperature, and soil pH were the best predictors of C4 herbaceous cover. Comparisons between predicted and observed values at field sites indicated our approach reliably predicted generalised C3:C4 abundance. Southern Australia, which has cooler temperatures and winter rainfall, was dominated by C3 vegetation and low δ¹³C values. C4-dominated areas included northern savannahs and grasslands. Conclusions Our isoscape approach is distinct because it incorporates remote sensing products that calculate cover beneath the canopy, the influence of local factors, and extensive validation, all of which are critical to accurate predictions. Our models can be used to predict C3:C4 abundance under climate change, which is expected to substantially alter current C3:C4 abundance patterns.
 
Context Several plant traits are associated with resistance to fire, thus fire-resistant species may give rise to more fire-resistant landscapes. However, up-scaling from plant traits to landscape- and regional-scale fire effects remains a challenge. Objectives We test two hypotheses: (1) forests composed of fire-resistant species experience lower fire severity than forests composed of less fire-resistant species; and (2) wildfires affecting forests with greater fire resistance experience smaller patches of high-severity fire. Methods We used a predictive map of existing forest types (major tree species dominating forest composition) and a trait-based map of fire resistance. We examined large-scale spatial patterns of fire severity derived from Landsat imagery in 611 wildfires across the range of western larch in the Inland Northwest USA (1985-2014). We then applied structural equation modeling to study complex relationships between fire resistance and high-severity fire in each wildfire. Results Forest types dominated by fire-resister species (e.g., ponderosa pine) experienced lower fire severity than forest types dominated by non-resister species such as lodgepole pine (fire-embracer) and subalpine fir (fire-avoider). We found a strong negative correlation between the fire resistance index and average values of the relative differenced normalized burn ratio (RdNBR), as well as an indirect relationship between fire resistance and high-severity patch size. Conclusions The large-scale differences in fire severity among forest types generally reflect the degree of fire resistance that fire-related traits confer to individual trees species, providing evidence that incorporating plant traits has the potential to assist in assessing fire resistance at large spatial scales.
 
Context Landscape structure influences the spread of plant pathogens, including coffee leaf rust, a fungal disease affecting the coffee industry. Rust transmission is likely affected by landscape structure through the dispersal of wind-borne spores. Previous studies found positive associations between rust incidence and the proportion of pasture cover, suggesting deforestation may facilitate spore dispersal. Objectives We explored the links between landscape structure and coffee rust by modeling the spread of rust transmission. We investigated how (1) spatial clustering of coffee farms, (2) proportion of landscape deforestation, and (3) clustering of deforestation affects the speed of rust transmission. Methods We developed a probabilistic model to simulate within-patch and between-patch transmission in simulated and real landscapes. We modeled within-patch transmission using a probabilistic cellular automata model and between-patch transmission using a random walk with spore movement inhibited by canopy cover. Results Clustering of coffee farms is the primary driver of rust transmission. Deforestation is a secondary driver of rust spread: outbreaks spread more rapidly in landscapes where deforested areas are evenly dispersed throughout the landscape. When applied to real landscapes in Costa Rica, the model yields the same trends as simulated landscapes and suggests increased amounts of coffee near the starting location of the outbreak are correlated with more rapid rust spread. Conclusions It is essential to consider landscape structure when managing the spread of crop diseases. Increasing the spacing between coffee farms and reducing forest fragmentation in coffee-growing regions can benefit biodiversity conservation and reduce the economic impacts of coffee rust.
 
Context In the Tibetan Plateau (TP), the supply of cultural ecosystem services (CESs) is unique, and the demand for CESs is gradually increasing with rapid urbanization. Evaluating the relationship between the supply and demand for CESs is critical for guiding regional sustainable development. However, due to the difficulty in obtaining empirical data in the high altitude and complex topography of the TP, relevant research is still lacking. Objectives The objective of this study was to develop an approach to address the difficulty of obtaining the empirical data on the TP and to evaluate the relationship between the supply and demand for CESs. Methods Taking the Qinghaihu–Huangshui basin as an example, we combined the SolVES (Social Values for Ecosystem Services) model and social media big data to evaluate the supply and demand for CESs in the TP. Results Our results showed that the combined method can effectively evaluate the supply and demand for CESs in the basin, and can be used for other remote regions. The supply and demand for CESs in the basin exhibited obvious spatial mismatch. Among the two types of mismatch, in the areas of high supply and low demand of CESs, residents’ subjective well-being (SWB) were substantially lower. Being far away from central city was an important reason for the high supply and low demand of CESs. Conclusions Establishing and improving the transportation system connecting central cities with other counties is encouraged to utilize the rich cultural and tourism resources of the TP, as well as enhance the SWB and promote regional sustainable development.
 
Land use/cover classes in the studied regions from SMS, Oaxaca, Mexico. Sampled sites are shown, as well as the different scales (100, 200, 500, 1000, and 1500 m) in which landscape metrics were measured
Generalized additive model (GAM) plots showing partial effects of selected landscape metrics on amphibian richness and abundance from studied regions in SMS, Oaxaca, Mexico. Only metrics chosen in the best model at the 200 m scale are plotted. Tick marks on the y- and x-axis are observed data points. Grey points represent partial residuals. The y-axis represents the partial effect of each variable. Shaded areas indicate 95% confidence intervals
Generalized additive model (GAM) plots showing partial effects of selected landscape metrics on Agalychnis dacnicolor, Eleutherodactylus pipilans, Hypopachus ustus, Leptodactylus melanonotus and Rhinella horribilis abundance at the 200 m scale from studied regions in SMS, Oaxaca, México. Only plots with metrics that were chosen in the best model at 200 m scale and with an importance value greater than 0.70 are presented. Tick marks on the y- and x-axis are observed data points. Grey points represent partial residuals. The y-axis represents the partial effect of each variable. Shaded areas indicate 95% confidence intervals
Context Land use change modifies landscapes’ original compositions and configurations, which can have a positive, negative, or neutral effect on species diversity. The direction and magnitude of the effect depends on how each species responds to these conditions and can change depending on the scale in which it is evaluated. Objectives We evaluated the effect of landscape composition and configuration on amphibian diversity at multiple scales in two fragmented regions in the Sierra Madre del Sur, Oaxaca, Mexico, in order to identify the determinant landscape characteristics for amphibian species. Methods We sampled amphibian populations at 16 sites and measured 15 landscape metrics at five different scales from focal patches. We then modelled the association between these metrics and amphibian abundance and richness for each scale. Results We found positive associations between amphibian richness and abundance with Forest Patch Density at the 200 m scale, and negative associations with Urban Total Area and Forest Edge Density at 200 m, 500 m and 1000 m scales. Single-species models revealed different responses to landscape metrics at varying scales, suggesting a differential response to landscape’s transformations that could be due to species life history traits. Conclusions Most amphibian species in these regions may be abundant in heterogeneous and fragmented landscapes as long as small forest patches are present. Nevertheless, large scale changes in forest amount and patch size due to fragmentation and urbanization could eventually affect some species negatively. Other variables at finer scales may be important and will depend on species-specific requirements.
 
Exposition and height of nests relative to host trees
Number of nests in entirely, partly, and unshaded locations
Distribution of nests at trees of different height
Study area and habitat suitability model. a Aerial photograph of the study site as taken by the UAV. Open circles indicate records of nests of E. maturna that were used to model habitat suitability. b Projection of the inferred habitat suitability model to the study area and c to an enlarged section from the centre of the area. Pixels with warmer colours at the southern margin of forest edges and more isolated trees indicate better condition for E. maturna as inferred by the model
Response plots of the ensemble model for all variables. Higher values indicate increased importance of the respective variable values for E. maturna. x-axes represent values in units of the original variables
Context Intensification of land-use caused a reduction of ecosystem heterogeneity and diversity, and subsequently led to dramatic decrease of biodiversity. Species depending on dynamic ecosystems are particularly affected from this trend of land-use intensification, landscape homogenization, and the optimization of land-use. Forest species suffer under the intensification of forest management, in the worst case transforming light and heterogeneous deciduous forests into species-poor intensively used deciduous forests optimized for wood production. This lead to the destruction of a mosaic consisting of various successional stages in parallel. Objective In this study we analyse the relevance of forest heterogeneity, forest disturbance and microhabitat preferences of egg oviposition and larval development for a highly endangered butterfly species, Euphydryas maturna . This butterfly species mainly occurs in light and moist deciduous forests, such as riparian forests along mountain streams in northern Austria. Methods We combine detailed field observations with high resolution aerial pictures taken with an Unmanned Aerial Vehicle (UAV) to build ensemble habitat suitability models from GAM, GBM, GLM, and Maxent models. Results We found that egg ovipositions take place exclusively on the tree species Fraxinus excelsior , preferably exposed to the south, partly shaded, and at medium height (3 m). Our habitat suitability models based on high resolution aerial pictures indicate that egg ovipositions are clustered and accumulate along forest edges and at sites with high forest heterogeneity. Conclusion Our study underlines the high relevance and importance of light deciduous forest structures with environmental dynamics creating the preconditions of specific microhabitat structures for endangered species, such as E. maturna . Our study shows that UAV-captured high precision aerial imagery are well suited to optimally connect two spatial scales, the ecosystem and microhabitat scale.
 
Top-cited authors
Samuel Cushman
  • United States Forest Service, Rocky Mountain Research Station
Jianguo Wu
  • Arizona State University
Michel Baguette
  • Muséum National d'Histoire Naturelle
Hans Van Dyck
  • Université Catholique de Louvain - UCLouvain
Katherine A. Zeller
  • US Forest Service