Patch Richness Research Articles

Impact of green space patterns on PM2.5 levels: A local climate zone perspective

Green spaces have been demonstrated to significantly decrease PM2.5 levels. However, the impact of green spaces on PM2.5 levels in different spatial forms of urban blocks is not yet fully understood. This research utilized ensemble machine learning algorithms to investigate the impact of green space spatial patterns, assessed through landscape pattern analysis, to PM2.5 concentrations during summer and winter based on local climate zones (LCZ). The results revealed significant differences in PM2.5 concentration across the seven types of primary LCZs, primarily influenced by meteorological factors. In winter, eight green space indicators exhibited a more substantial contribution to PM2.5 levels in comparison to the summer season. Significant discrepancies were noted in the contributions of these indicators across different LCZs. Patch density (PD) and landscape shape index (LSI) made a more substantial contribution, while relative patch richness (RPR) and Shannon's evenness index (SHEI) showed a less significant contribution. The spatial pattern of green spaces was significantly related to their contributions. Among the seven predominant LCZs, the five key indicators included PD, LSI, Shannon's diversity index (SHDI), area-weighted mean patch area (AREA_AM), and connectance index (CONNECT). Spatial heterogeneity was further observed in the positive and negative contributions of each green space indicator. This study enhances understanding of the impact of green spaces on PM2.5 across LCZs and provides valuable insights for urban management and planning.

Insights of ecological resilience in Caatinga assemblages – landscape configuration drives Chrysomeloidea (Coleoptera) diversity in a seasonally dry tropical forest

Seasonally Dry Tropical Forests (SDTFs) have long suffered from chronic, intensive and extensive landscape transformations due to human activities, challenging biodiversity conservation in this biome. The Caatinga is the largest Neotropical SDTF, serving as an excellent model to understand how land use changes modulate ecological communities. In this study, we assessed the effects of landscape configuration and composition on the abundance and diversity of Chrysomeloidea in a region of Caatinga. We sampled beetles in 10 landscape units, in which we measured landscape configuration (patch size, edge density) and composition (landscape diversity, patch richness). Hill numbers (q0, q1, q2) were used to analyze the diversities of Chrysomeloidea. A total of 484 individuals belonging to 82 species were collected. Landscape configuration affected Chrysomeloidea assemblages with a positive correlation between edge density and Chrysomeloidea diversity. We provide a new perspective regarding the spatial distribution of Chrysomeloidea in Caatinga and conclude that landscape configuration, but not composition, directly affects Chrysomeloidea assemblages.

Unravelling the paradox of spatial homogeneity: An analysis of land use planning and monofunctional development in Wroclaw, Poland

Processes of urban development progressing across Europe, with built-up areas expanding at a faster rate than the population, especially in the Central and Eastern Europe region. The importance of ensuring a high quality of urban life and effective environmental planning is widely acknowledged for the sustainable development of functional urban areas. While the literature extensively covers the implementation stage of monofunctional development in Polish suburban zones, the origins of spatial homogeneity in planning have received limited attention in research. This paper aims to assess local zoning plans and examine the diversity of urban planning in the subregion of Wroclaw, Poland. Employing exploratory experts interviews and landscape metrics such as Patch Richness Density and Simpson Diversity Index, along with spatial statistics as the Hot Spot tool and Cluster and Outlier analysis, the findings reveal that monofunctional residential development in Polish functional urban areas may result from a complex interplay of various economic, institutional, educational, professional responsibility, and political, social, as well as historical factors on the urban planning stage. These results contribute to a deeper understanding of the extent and spatial distribution of planning diversity, opening paths for further discussions on the professional requirements of urban planners.

Evaluating the impact of landscape configuration, patterns and composition on land surface temperature: an urban heat island study in the Megacity Lahore, Pakistan.

The urban heat island (UHI) phenomenon is negatively impacted by rapid urbanization, which significantly affects people's everyday lives, socioeconomic activities, and the urban thermal environment. This study focuses on the impact of composition, configuration, and landscape patterns on land surface temperature (LST) in Lahore, Pakistan. The study uses Landsat 5-TM and Landsat 8-OLI/TIRS data acquired over the years 2000, 2010 and 2020 to derive detailed information on land use, normalized difference vegetation index, LST, urban cooling islands (UCI), green cooling islands (GCI) and landscape metrics at the class and landscape level such as percentage of the landscape (PLAND), patch density (PD), class area (CA), largest patch index (LPI), number of patches (NP), aggregation index (AI), Landscape Shape Index (LSI), patch richness (PR), and mean patch shape index (SHAPE_MN). The study's results show that from the years 2000 to 2020, the built-up area increased by 17.57%, whereas vacant land, vegetation, and water bodies declined by 03.79%, 13.32% and 0.4% respectively. Furthermore, landscape metrics at the class level (PLAND, LSI, LPI, PD, AI, and NP) show that the landscape of Lahore is becoming increasingly heterogeneous and fragmented over time. The mean LST in the study area exhibited an increasing trend i.e. 18.87°C in 2000, 20.93°C in 2010, and 22.54°C in 2020. The significant contribution of green spaces is vital for reducing the effects of UHI and is highlighted by the fact that the mean LST of impervious surfaces is, on average, roughly 3°C higher than that of urban green spaces. The findings also demonstrate that there is a strong correlation between mean LST and both the amount of green space (which is negative) and impermeable surface (which is positive). The increasing trend of fragmentation and shape complexity highlighted a positive correlation with LST, while all area-related matrices including PLAND, CA and LPI displayed a negative correlation with LST. The mean LST was significantly correlated with the size, complexity of the shape, and aggregation of the patches of impervious surface and green space, although aggregation demonstrated the most constant and robust correlation. The results indicate that to create healthier and more comfortable environments in cities, the configuration and composition of urban impermeable surfaces and green spaces should be important considerations during the landscape planning and urban design processes.

Modern pollen-plant diversity relationship in open landscapes of Tibetan Plateau

Past plant diversity dynamics can provide a historical perspective on biodiversity change and ecosystem dynamics as a guide to conservation management in the future. Pollen analysis is one of the most widely used approaches to reconstruct plant diversity change through time, but the representativeness of pollen diversity to plant diversity is not yet fully understood and especially in open landscapes. In this paper, we compare pollen assemblages from topsoil in the open landscapes of the Tibetan Plateau with the surrounding vegetation to investigate their compositional similarity and the correlations between their diversity (richness and evenness), and to evaluate the influence of climate variables, landscape characteristics, and human disturbance on the pollen diversity. The results show that pollen assemblages vary obviously in different vegetation types and they can represent their surrounding plant communities at low taxonomic levels. Pollen richness boarders a better match with plant richness than pollen evenness with plant evenness across the whole study area, but the correlation is weak (radj2 < 0.2, p < 0.001). Transforming plants into their pollen equivalents has little improvement for the correlations between pollen and plant richness/evenness. Pollen richness and evenness can capture well the trend in plant richness and evenness along the vegetation gradient, suggesting that they can be used as gamma (landscape scale) rather than alpha (local scale) diversity metrics in the study area. Mean annual precipitation (MAP), mean annual temperature (MAT), and patch richness (PatchS) are positively associated with pollen richness. MAP, PatchS, and human disturbance (HD) are significantly predictors for pollen evenness. These confirm that pollen richness and evenness can provide insights into past plant diversity dynamics and help evaluating the effects of climate and habitat changes and human disturbance on long timescales.

Correlation between Landscape Pattern and Net Primary Productivity of Vegetation: A Case Study in the Arid and Semi-Arid Regions of Northwest China

Human activities and environmental changes have influenced the changes in landscape patterns, which in turn profoundly impact the variation in net primary productivity (NPP) of vegetation. Understanding the relationship between landscape patterns and NPP is of significant importance for maintaining ecosystem stability and improving the ecological environment. In this study, six land use types in the arid and semi-arid regions of Northwest China were selected, and five landscape pattern indices at the landscape level and four landscape pattern indices at the class level were used. Pearson correlation and multiple linear regression models were employed to analyze the relationship between landscape indices and NPP at a 100 km × 100 km grid scale. The results indicate that there are varying degrees of correlation between landscape pattern indices and NPP from 2001 to 2020, with different levels of variation over the 20-year period. The correlation between indices and NPP is higher at the class level than at the landscape level, and the increase in landscape abundance and fragmentation promotes an increase in NPP. At the landscape level, three landscape indices, namely NP (Number of Patches), PR (Patch Richness), and SHDI (Shannon’s Diversity Index), explain 45.4% of the variation in NPP. At the class level, NP, TE (Total Edge Length), and IJI (Dispersion and Juxtaposition Index) are the main influencing factors for NPP in cropland, forestland, and grassland. Therefore, in ecological governance, it is necessary to consider landscape pattern changes appropriately to maintain ecosystem stability.

The Impact of Land—Use Composition and Landscape Pattern on Water Quality at Different Spatial Scales in the Dan River Basin, Qin Ling Mountains

To study the impact of land—use structure and landscape pattern on water quality at different spatial scales in the Dan River Basin (Qin Ling Mountains, China), water samples from 21 sites along the Dan River were collected in 2022 during the dry and wet seasons, and nine water quality indices were tested. Land—use composition and landscape pattern indices at riverine reach, riparian, and sub—basin were obtained, and correlation analysis and redundancy analysis (RDA) were used to determine the relationship with water quality. The results are as follows. (1) Water quality in the Dan River is better in the wet season than in the dry season; the main pollutants are total nitrogen (TN) and total phosphorus (TP). (2) The impact of land—use composition and landscape pattern on water quality has a scale effect; riverine reach can best explain the water quality. (3) Agricultural land and forest have the greatest impacts on water quality; agricultural land and construction land aggravate the deterioration of water quality, while forest, grassland, and water area have positive effects on water quality. The largest patch index (LPI) and contagion index (CONTAG) were positively correlated with pollutants, while Patch richness density (PRD), Patch shape (PD), Shannon’s diversity index (SHDI), and landscape shape index (LSI) were negatively correlated with pollutants, indicating that with an increase in the impact of human activities on landscapes, the degree of fragmentation decreases patch richness, landscape shape tends to be simplified, and water pollution is eventually aggravated. Land planners should focus on optimizing the land—use structure and landscape pattern to increase the diversity of the landscape. Therefore, strict environmental regulations must be established.

Factors impacting bat activity and species richness in protected parks in the oak openings region of Northwest Ohio.

Protected areas are important for wildlife, especially in heavily developed areas. Bats are one group utilizing protected areas, but it is unclear what makes an ideal place for bats to live in parks, especially since preferences vary between open and forest foraging species and at different scales. The main objective of this study was to determine the landscape and vegetation factors at multiple scales most associated with higher bat activity and species richness in protected parks. Total bat activity, species richness, and activity for open and forested foraging species were compared to small-scale data vegetation structure collected in the field and larger-scale landscape data calculated in ArcGIS and FRAGSTATS. Bat activity and species richness increased with higher percentages of dry and open land cover types such as sand barrens, savanna, cropland, and upland prairie and decreased with higher percentages of forest and wet prairies. Patch richness, understory height, and clutter at the 3-6.5 m level were negatively associated with total bat activity. The most important variables for bats differed depending on spatial scale measured and if species were open or forest adapted. When managing for bats in parks, it would be advantageous to restore open land cover types such as savanna and mid-level clutter, and excessive fragmentation. Whether species are open or forest adapted and scale-specific differences should also be considered.

Factors affecting land use and land cover change and fragmentation in selected protected areas in the Philippines

The establishment of protected areas (PA) is one of the most prominent biodiversity conservation policies in the Philippines. Functions of the country’s PA policy include the reduction of deforestation, forest conservation, and decreasing human pressure on natural resources. This study aimed to assess the drivers of forest intactness and fragmentation in selected terrestrial PAs. Management Effectiveness Tracking Tool (METT) scores were used to consider the impact of drivers of land use and land cover (LULC) change, including economic development, population, road networks, cultivation, and PA management, on the percentage of forest cover and different fragmentation metrics in the PAs. The study conducted a Canonical Correlation Analysis (CCA) and Multiple Regression Analysis to identify the significant drivers of LULC change for each response variable. CCA and Multiple Regression Analysis results showed the most important predictors of LULC based on their canonical weights: cultivation within the PA, the human development index, and the regional gross domestic product. Aside from being the most important driving factor based on the CCA, cultivation inside the PA was also found to be a common significant variable for most dependent variables. Other significant factors were poverty index, population, and road density, which could all provide a basis for the observed PA fragmentation. The study also found that higher METT scores had a negative influence on the open forest and a positive effect on patch richness. This indicates that open forests tend to decrease as PA management improves while patch richness tends to increase. Furthermore, there is no data to support that METT scores have a significant influence on other response variables.

Quantitative analysis of the spatial diversity of Moraceae in China

Changes in distribution patterns of economically essential forest species under global change are urgently needed in the scientific forecast, and large-scale spatial modeling is a crucial tool. Using diversity pattern indicators and other data obtained through geographic information systems (GIS) and spatial data on Moraceae species obtained from published data, we quantitatively studied the spatial diversity patterns of genera in the Moraceae in China. The results revealed that the patch richness, diversity index, and total shape index of the genera with multiple species were significantly higher than those of the monotypic genera. Monotypic genera had no spatial diversity and no distribution in patterns of spatial diversity. Maclura had the most concentrated spatial distribution and the lowest distribution area among the Moraceae in China. The number of patches and the total area were the smallest, while the most significant patch index was the highest. Maclura had no spatial diversity. Streblus had the highest patch abundance compared to other genera with fewer species. Streblus had the smallest number of patches and total area of distribution, the lowest spatial distribution, and a small total shape index, indicating its concentrated distribution. The values of the Shannon’s Diversity Index (SHDI) and Simpson’s Diversity Index (SIDI) were the highest, and the spatial distribution was the most diverse among the genera with fewer species. The patch type of Streblus had a more considerable value than other genera, but the number of patches was small, and the total shape index was low. Streblus was primarily distributed in the south of Yunnan, western Guangxi, the west and central parts of Hainan, and southern Guangdong. Most of these areas were mountainous. The temperature decreased with elevation, providing diverse environmental conditions for the narrow-stem genus. Among the Moraceae in China, the spatial distribution of Ficus was the most diverse, with the highest number of patches, patch types, total shape index, SHDI, and SIDI values. The spatial diversity of Ficus could be used as a protected area for Moraceae in China.

Effects of Landscape, Climate and Hunting on the Occurrence of White-Browed Guan Penelope jacucaca in Central-North Caatinga, Brazil

The White-browed Guan Penelope jacucaca is an endangered member of the family Cracidae endemic to the Caatinga region of northeast Brazil. Hunting and habitat loss are the presumed major threats to the species, however, their impacts on its occurrence have been insufficiently tested. We used field observations and information on hunting activity to assess whether hunting, landscape and/or climate affect the occurrence of the White-browed Guan in the Caatinga. We sampled 23 localities within the Caatinga and recorded the species' occurrence and hunting encounters. For each locality, we extracted three landscape metrics (vegetation cover, patch aggregation, and patch richness); two climate variables (temperature seasonality and mean precipitation of the wettest month); and one topographic variable (mean slope). We then used generalized linear models based on proportion to determine if the species' occurrence was related to these environmental factors. We found a greater likelihood of detecting the species in localities with greater slope, greater heterogeneity of habitats and more stable temperatures, whereas hunting activity presented a trend of negative impact on occurrence. Our results emphasize the relevance of using different environmental metrics to implement conservation programs for this threatened species.

The Effects of Landscape Heterogeneity on East China Anuran Communities: Identifying Spatial Scales in an Urbanizing Landscape

Cities are growing rapidly worldwide, with over half of the human population living in cities. Amphibians are the most threatened vertebrates on our planet and are particularly vulnerable to the effects of urbanization. While it is known that landscape features and scales are important for amphibians in urban areas, we do not adequately understand how the urban landscape affects diversity patterns, nor have we identified which spatial scale is most appropriate for evaluating how amphibians respond to urban environments. In this study, we examined the relationships between anuran abundance/richness and landscape features at four spatial scales in Shanghai, China. In order to determine the relative importance of landscape variables and the most appropriate spatial scale, a multi-model inference approach was used to evaluate and compare model weighted mean coefficients. Our results show that large spatial scales, i.e., 1500 m and 2000 m, best predicted relative anuran abundance and richness, while the total anuran abundance responded most strongly to landscape variables at smaller scales, i.e., 500 m and 1000 m. Patch richness and the interspersion and juxtaposition index play a large role in predicting the anuran species’ richness and abundance. The abundance of P. nigromaculatus, F. multistriata, and B. gargarizans increased with patch richness. Species richness and total abundance were most strongly related to the interspersion and juxtaposition index. Our research highlights the importance of identifying the most suitable spatial scale in urban environments because not all anuran respond to the same spatial scale. We found that the relationships between anuran relative abundance and species and urban habitat features are not consistent with the prediction of other landscapes (e.g., farmland, forest, and island). Additionally, constructing diverse habitat patches and more neighboring habitats may maintain or improve anuran communities in urbanizing landscapes.

Dispersal and Space Use of Captive-Reared and Wild-Rehabilitated Harpy Eagles Released in Central American Landscapes: Implications for Reintroduction and Reinforcement Management

Understanding the spatial context of animal movements is fundamental for the establishment and management of protected areas. We tracked, by telemetry devices, 31 captive-reared and 5 wild-rehabilitated Harpia harpyja and estimated the dispersal and space use after release in Mesoamerica. We evaluated the effectiveness of protected areas in the protection of home ranges and examined how individual traits, release methods and landscape features influenced the dispersal and home range using mixed-effects models. The mean post-release dispersal was 29.4 km (95% CI: 22.5–38.5), and the annual home ranges averaged 1039.5 km2 (95% CI: 627–1941). The home ranges were influenced by the release method, patch richness, patch and edge density and contagion. The currently protected areas in Mesoamerica may not be effective conservation units for this species. The Harpy Eagle average home range greatly exceeded the average size of 1115 terrestrial protected areas (52.7 ± 6.1 km2) in Mesoamerica. Due to their wide use of space, including transboundary space, Harpy Eagle conservation efforts may fail if they are not carefully coordinated between the countries involved. Future restoration efforts of umbrella forest-dwelling raptors should select release sites with highly aggregated and poorly interspersed forests. The release sites should have a buffer of approximately 30 km and should be located completely within protected areas.

The Atlantic Ocean landscape: A basin-wide cluster analysis of the Atlantic near seafloor environment

Landscape maps based on multivariate cluster analyses provide an objective and comprehensive view on the (marine) environment. They can hence support decision making regarding sustainable ocean resource handling and protection schemes. Across a large number of scales, input parameters and classification methods, numerous studies categorize the ocean into seascapes, hydro-morphological provinces or clusters. Many of them are regional, however, while only a few are on a basin scale. This study presents an automated cluster analysis of the entire Atlantic seafloor environment, based on eight global datasets and their derivatives: Bathymetry, slope, terrain ruggedness index, topographic position index, sediment thickness, POC flux, salinity, dissolved oxygen, temperature, current velocity, and phytoplankton abundance in surface waters along with seasonal variabilities. As a result, we obtained nine seabed areas (SBAs) that portray the Atlantic seafloor. Some SBAs have a clear geological and geomorphological nature, while others are defined by a mixture of terrain and water body characteristics. The majority of the SBAs, especially those covering the deep ocean areas, are coherent and show little seasonal and hydrographic variation, whereas other, nearshore SBAs, are smaller sized and dominated by high seasonal changes. To demonstrate the potential use of the marine landscape map for marine spatial planning purposes, we mapped out local SBA diversity using the patch richness index developed in landscape ecology. It identifies areas of high landscape diversity, and is a practical way of defining potential areas of interest, e.g. for designation as protected areas, or for further research. Clustering probabilities are highest (100%) in the center of SBA patches and decrease towards the edges (&amp;lt; 98%). On the SBA point cloud which was reduced for probabilities &amp;lt;98%, we ran a diversity analysis to identify and highlight regions that have a high number of different SBAs per area, indicating the use of such analyses to automatically find potentially delicate areas. We found that some of the highlights are already within existing EBSAs, but the majority is yet unexplored.

Exploring spatial nonstationarity for four mammal species reveals regional variation in environmental relationships

AbstractBroad‐scale ecological research on species distributions commonly presumes that the correlative relationships discovered are stationary over space. This is an assumption of most species distribution models (SDMs) that combine observations of species occurrence with environmental characteristics to understand current ecological correlates and to predict distributions based on those relationships. However, ecological relationships may vary spatially because of changes in the environment (i.e., resource availability) or the organism itself (i.e., local adaptation). Discovering this within‐species variation typically requires dense datasets over large geographic areas, which are now being provided by the recent proliferation of open‐access biodiversity occurrence records. Using nearly 4000 sampling locations from an open‐access, state‐wide camera‐trapping project, we explore the space‐varying effects of covariates on the distribution of four mammal species at two scales: region‐specific and fine resolution, with the latter estimated using spatially varying coefficients (SVC) models, to understand the scale of spatial variation in ecological relationships. Among the four species tested, the ecological relationships for two were best explained with the regional models, equivocal results for one species, while the SVC model had superior fit and predictive performance for the final species (white‐tailed deer, Odocoileus virginianus). Spatial nonstationarity was more common in relationships with landscape composition characteristics, such as housing density, than in landscape configuration metrics, such as patch richness density. One of the most appealing results of an SVC approach is not only the improved predictions across large landscapes but also understanding how animals are responding to the environment differently at the management unit level. For example, we found that deer's spatially varying relationship with forest cover was best explained by an interactive effect of deer management units (i.e., differences in deer populations) and predator pressure. These findings lead to a new hypothesis about how deer may be differentially using forested environments across space and could be a promising area of future research. Given sufficient data, accounting for nonstationarity in SDMs can show large‐scale ecological patterns while also detecting local level changes in animal ecology in areas small enough that management or protection can be readily implemented.

Response of Soil Fauna Diversity to Agricultural Landscape Het-Erogeneity in the Middle and Lower Reaches of the Yellow River—A Case Study in Gongyi City, China

Soil fauna contribute to important ecological functions such as improving soil structure and promoting nutrient circulation. They are the common environmental indicators in agricultural landscape. Therefore, this study took Gongyi City, Henan Province, China, located in the middle and lower reaches of the Yellow River, as the study area, to explore the impact of multi-scale landscape heterogeneity on soil fauna diversity and the response of soil fauna to it. Our results showed that patch types and degree of fragmentation in the study area increased significantly with the increase of spatial scale. The diversity indices of soil fauna in different habitats showed that the Shannon-Wiener diversity index, Simpson’s diversity index, Pielou’s evenness index, and Margalef richness index were the lowest in farmland habitat. Diversity indices of artificial forests were higher than those of natural forests. Diversity indices of soil fauna under different geomorphic conditions showed that Pielou’s evenness index and Margalef richness index had significant differences under different geomorphic conditions (p &lt; 0.05). The effects of multi-scale landscape heterogeneity on soil fauna diversity were different. In the 150 m buffer zone, soil fauna community composition and diversity indices were strongly correlated with patch richness index, patch richness density, and other landscape indices (p &lt; 0.05). Meanwhile, the contribution rate of landscape index to soil fauna community composition were 45.05%, 32.5%, and 42% in farmland, plantation, and natural forest, respectively. Therefore, the 150 m buffer zone could be used as the characteristic response scale of soil fauna diversity. The multi-scale interaction of landform, habitat, and landscape also had a significant impact on soil fauna diversity.

Olive Landscape Affects Bactrocera oleae Abundance, Movement and Infestation

The economic importance of Bactrocera oleae (Rossi) and the problems associated with insecticides make necessary new management approaches, including deeper biological knowledge and its relationship with landscape structure. Landscape complexity reduces B. oleae abundance in late summer–autumn in areas of high dominance of olive groves, but the effect of landscape structure in spring and in areas less dominated by olive groves has not been studied. It is also unknown whether the insect disperses from olive groves, using other land uses as a refugee in summer. This work evaluates the effect of landscape structure on olive fruit fly abundance and movement in spring and autumn, and infestation in autumn, in central Spain, an area where the olive crop does not dominate the landscape. A cost–distance analysis is used to evaluate the movement of the fly, especially trying to know whether the insects move away from olive groves in summer. The results indicate that B. oleae abundance is consistently lower in complex landscapes with high scrubland area (CAS), patch richness (PR) and Simpson landscape diversity index (SIEI), and low olive grove area (CAO). The cost–distance analysis shows that the fly moves mainly in spring, and amongst olive groves, but there is no evidence that land uses other than olive groves serve as a summer refuge. Olive fly infestation decreased with decreasing CAO and increasing CAS and SIEI, accordingly with the effect of landscape on abundance. Thus, mixing olive groves with other land uses, which are not a source of flies, can help improve control of this important pest.

Wild bee responses to cropland landscape complexity are temporally-variable and taxon-specific: Evidence from a highly replicated pseudo-experiment

Wild bees may benefit from the restoration of natural areas in agricultural regions. The abundance and diversity of wild bee species responds to the amount of nesting and foraging habitat, but it is less clear how the distribution of these resources (e.g., the landscape complexity) may affect bees. We implemented a pseudo-experiment to disentangle the effects of three components of landscape complexity for wild bees in a Canadian Prairie cropland region. We used an algorithm to identify 146 sites that minimized correlations in indices of patch richness (i.e., the diversity in land cover types) and contagion (i.e., their degree of interspersion), and that collectively captured a cross-section of landscape contexts that differed in the relative proportion of cropland to other non-crop land covers. We trapped bees at these locations repeatedly over time (1119 unique collection events; equivalent to 10,471 trap-days over two consecutive years), identifying 22,493 bees of 213 taxa, in order to model trends for bees at different times of the season. We found that increasing patch richness may support a greater number of bee taxa, but individual bee taxa varied considerably in their response to components of landscape complexity. The effect on the total abundance of wild bees was temporally-variable, with the amount of cropland positively associated with abundance earlier in the season when mass-flowering crops are in bloom, and negatively later in the season when semi-natural areas are likely to provide the most forage. The response of bee abundance to contagion also varied temporally, and demonstrated a “humped” effect later in the growing season, suggesting there is an optimum in the complementary resources provided by adjacent habitat types. Our study shows that increasing the amount or diversity of non-crop land covers in this region is not likely to have a consistent effect for the majority of species across the season. We argue that modifying croplands to support wild bees is likely to be a complex task, requiring study of the functional responses to landscape of bee species present in the region, and their interactions with the phenological variability in resources.

Spatial correlations between landscape patterns and net primary productivity: A case study of the Shule River Basin, China

Human activities and environmental degradation have resulted in landscape pattern changes and can eventually profoundly affect net primary productivity (NPP) at different scales worldwide. A comprehensive understanding of how the relationship between landscape patterns (composition and configuration) and NPP changes across scales, is helpful for landscape planning and ecological protection and restoration. However, relevant research is currently understudied. Therefore, this study selected 39 landscape metrics and 5 types of land use in the Shule River Basin (SRB), and analysed their correlation under eight different scales via multiple linear regression models, aiming to determine the core landscape metrics to assess the NPP. Results indicate obvious spatial variations in the landscape metrics. At the same time, NPP in SRB was relatively small and showed obvious spatial heterogeneity. Landscape metrics and NPP showed different degrees of positive or negative correlation at different grid scales, and there were higher correlation at the 30 km scale. The increase in patch fragmentation and diversity promoted an increase in NPP. The correlation between landscape metrics and NPP was higher and more significant at the class level than at the landscape level, except in the case of unused land. Configuration metric (patch density and patch richness) explained 68% of the variation in NPP at the landscape level. At the class level, composition metrics (class area and percentage of landscape) played an important role in farmland, forestland, and grassland, while edge density (configuration metric) played an absolute role in the built-up land and unused land; overall, the effectiveness of the model was stronger at the class level than at the landscape level. The generated regression model allows us to quantitatively understand how to characterize changes in NPP through changes in landscape patterns. Appropriate landscape pattern and optimal scale should be considered in landscape planning and land use management to reduce the expected ecological impact.

Anthropogenic and Environmental Factors Determining Local Favourable Conditions for Wolves during the Cold Season

Simple SummaryWolves normally howl in response to unfamiliar vocalisations, to defend their territory and the important resources within it (e.g., pups and prey). During the non-rendezvous period (late autumn and winter), the protectiveness of adults towards pups decreases, as well as reactions to unfamiliar vocal stimuli. In the late fall of 2010, we performed a saturation wolf howling design in the Cicolano area (Central Apennines, Italy), aiming to identify environmental and human-related characteristics of locations where wolves are prone to respond to unfamiliar howling and to assess their eventual ability to provide insights into the distribution of valuable resources (aside from pups) during the cold season. We found that winter response sites (WRS) were characterized by diverging conditions, with respect to all available sites, suggesting that they are non-randomly located but, instead, had been selected by wolves for some reason. We recorded a positive role of thermal refuges and the occurrence of wild boar drive hunts, as well as the negative roles of other forms of human presence and activities, including the occurrence of free-ranging dogs. These results could be of interest both for conservation purposes and for assessing interactions with human activities.Winter resources are crucial for wildlife, and, at a local scale, some anthropogenic and environmental factors could affect their availability. In the case of wolves, it is known that vocalisations in response to unfamiliar howls are issued to defend their territory and the important resources within it. Then, we studied the characteristics of winter response sites (WRS) during the cold season, aiming to assess their eventual ability to provide insights into the distribution of valuable resources within their territories. Within this scope, we planned a wolf-howling survey following a standardised approach. The study covered an Apennine (Central Italy) area of 500 km2. A hexagonal mesh was imposed on the area, in order to determine the values of different variables at the local scale. A logistic LASSO regression was performed. WRS were positively related to the presence of thermal refuges (odds = 114.485), to patch richness (odds = 1.153), wild boar drive hunting areas (odds = 1.015), and time elapsed since the last hunt (odds = 1.019). Among negative factors, stray dogs reply considerably affects wolves’ responsiveness (odds = 0.207), where odds are the exponentiated coefficients estimated by the logistic lasso regression. These results suggest that WRS are related to anthropogenic and environmental factors favouring the predation process.