Mean Patch Research Articles

Is There a Spatial Relationship between Urban Landscape Pattern and Habitat Quality? Implication for Landscape Planning of the Yellow River Basin.

The extent to which landscape spatial patterns can impact the dynamics and distribution of biodiversity is a key geography and ecology issue. However, few previous studies have quantitatively analyzed the spatial relationship between the landscape pattern and habitat quality from a simulation perspective. In this study, the landscape pattern in 2031 was simulated using a patch-generating simulation (PLUS) model for the Yellow River Basin. Then, the landscape pattern index and habitat quality from 2005 to 2031 were evaluated using the Fragstats 4.2 and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Furthermore, we analyzed the spatial distribution characteristics and spatial spillover effects of habitat quality using spatial autocorrelation analysis. Finally, the spatial association between the landscape pattern index and habitat quality was quantitatively revealed based on a spatial lag model. The simulation results showed that: (1) from 2005 to 2031, the landscape of the Yellow River Basin would be dominated by grassland and unused land, and the areas of construction land and water body will increase significantly, while the area of grassland will decrease; (2) patch density (PD) and Shannon’s diversity index (SHDI) show significant increases, while edge density (ED), landscape shape index (LSI), mean patch area (AREA_MN), and contagion index (CONTAG) decrease; (3) from 2005 to 2031, habitat quality would decrease. The high-value areas of habitat quality are mainly distributed in the upper reaches of the Yellow River Basin, and the low-value areas are distributed in the lower reaches. Meanwhile, both habitat quality and its change rate present positive spatial autocorrelation; and (4) the spatial relationships of habitat quality with PD and COHESION are negative, while ED and LSI have positive impacts on habitat quality. Specifically, landscape fragmentation caused by high PD has a dominant negative influence on habitat quality. Therefore, this study can help decision makers manage future landscape patterns and develop ecological conservation policy in the Yellow River Basin.

Where are the trees? Extent, configuration, and drivers of poor forest recovery 30 years after the 1988 Yellowstone fires

Postfire recovery of fire-adapted forests remains uncertain as climate and fire regimes continue to change. Areas of poor postfire tree regeneration following late-20th-century fires may reveal characteristics associated with increased vulnerability to forest decline. However, sufficient time must have elapsed and pre- and postfire forest cover must be compared to distinguish areas that have not recovered. We used remotely sensed data and the Normalized Difference Vegetation Index (NDVI) to detect areas of poor forest recovery across > 250,000 ha of area burned as stand-replacing fire 30 years after the 1988 Yellowstone fires. We asked three questions: (1) What is the extent and configuration of sparse and reduced forest recovery? (2) How do vegetation characteristics compare between areas of sparse and reduced recovery vs. recovered forest? (3) What environmental characteristics explain the distribution and patch size of sparse and reduced recovery? We related postfire (2013–14) NDVI to field-measured stem density to establish an NDVI threshold of sparse tree regeneration, and we contrasted pre- (1986–87) and postfire (2018–19) NDVI as a proxy for pre- and postfire forest cover. Sparse and reduced forest recovery occupied ∼ 41,000 ha across the burned area, about half of which was ≥ 150 m from ex situ seed sources. Patches of poor recovery were generally large, with ∼ 13,400 ha in patches ≥ 50 ha and an area-weighted mean patch size of 97 ha. Vegetation was short (<2 m) in areas of sparse and reduced recovery and non-evergreen biomass was three times greater than in recovered forest. Sparse and reduced recovery was more likely at high elevations, on steep slopes, and far from ex situ seed sources, and patches were larger at high elevations and far from seed sources. It took 20 years for sparse and reduced recovery to be distinguishable from recovered forest using NDVI, suggesting a time lag before remotely sensed data can detect alternative pathways of postfire forest recovery. Now, 30 years after the 1988 fires, ∼16 % of the forest burned as stand-replacing fire has failed to recover. The extent and configuration of these areas suggest some may persist as sparse or non-forest for the foreseeable future, with implications for biodiversity and ecosystem processes.

Annual Change Analysis of Mangrove Forests in China during 1986–2021 Based on Google Earth Engine

Mangroves are a key type of protected coastal wetland, with a range of benefits such as protection from wave damage, sand fixation, water purification and ecological tourism. As the academic knowledge of mangroves has gradually increased, the use of remote sensing to monitor their dynamic changes in China has become a hot topic of discussion and has received attention in academic circles. Remote sensing has also provided necessary auxiliary decision-making suggestions and data support for the scientific and rational conservation, restoration and management of mangrove resources. In this paper, we used Landsat satellite series data combined with the normalized difference vegetation index (NDVI) and adaptive threshold partitioning (OTSU method) to monitor mangrove dynamics in coastal China from 1986 to 2021 based on Google Earth Engine (GEE), with three main results. (1) Based on the massive data and efficient computational capability of the GEE platform, we achieved large-scale interannual mangrove distribution extraction. The overall classification accuracy for 2019 exceeded 0.93, and the mangrove distribution extraction effect was good. (2) The total mangrove area and the mean patch size in China showed overall increasing trends, and Guangdong and Guangxi were the top two provinces in China in terms of the largest mangrove area. (3) Except for Dongzhaigang National Nature Reserve, the mangrove areas in other national mangrove reserves mainly showed increasing trends, confirming the effectiveness of the reserves. Data on the spatial structure and area trends of mangroves in China can provide scientific references for mangrove conservation and development, and serve in the further restoration of mangrove ecosystems.

Landscape metrics analysis of land use patterns and changes in suburban local government areas of Ibadan, Nigeria

This study examined the spatiotemporal pattern of urban growth and magnitude of changes in selected Local Government Areas (LGAs) in the suburban area of Ibadan using remote sensing cum spatial metrics. Data for the study were obtained from administrative maps of the study area, population data and satellite imageries. All of these were complemented with ground validations using Global Positioning System (GPS) receiver. Periods of change analysis were divided into two epochs; 1986 to 2002 and 2002 to 2017. The imageries were classified into four landuse/cover classes based on Anderson’s modified version of supervised classification scheme. Accuracy assessment of the imageries was carried out using 100 randomly sampled ground truth points. Ten spatial metrics were selected for analysis using Fragstats program. Results show a significant loss in vegetation due to conversion (Land Consumption Rate, LCR = 0.025, 0.019 and 0.027; Land Absorption Coefficient, LAC = 0.012 and 0.048). Moreover, there was a persistent increase in Number of Patches (NP) indicating a scattered and fragmented but continuous development. However, dwindling values of Patch Density (PD = 90.94, 27.07 and 30.30) indicate expansion through conversion of other landuses at varying rates. Results also indicate an incidence of fragmented low density development in the fringe areas (Area Weighted Mean Patch Fractal Dimension, AWMPFD = 1.37 and 1.39). The study highlights the chaotic land development and unrestrained urban expansion in the study area.

Scale‐dependent responses of understory vegetation to the physical structure of undisturbed tundra shrub patches

Much of the Arctic is experiencing rapid change in the productivity and recruitment of tall, deciduous shrubs. It is well established that shrub expansion can alter tundra ecosystem composition and function; however, less is known about the degree to which variability in the physical structure of shrub patches might mediate these changes. There is also limited information as to how different physical attributes of shrub patches may covary and how they differ with topography. Here, we address these knowledge gaps by measuring the physical structure, abiotic conditions, and understory plant community composition at sampling plots within undisturbed green alder patches at a taiga–tundra ecotone site in the Northwest Territories, Canada. We found surprisingly few associations between most structural variables and abiotic conditions at the plot scale, with the notable exceptions of canopy complexity and snow depth. Importantly, neither patch structure nor abiotic conditions were associated with the vegetation community at the plot scale when among-patch variation was accounted for. However, among-patch variation in plant community composition was significant and represented a gradient in the richness of tundra specialists and Sphagnum moss abundance. This gradient was strongly associated with mean patch snow depth, which was likely controlled at least in part by mean patch canopy complexity. Overall, natural variability in green alder patch structure had less of an association with abiotic conditions than expected, suggesting future changes in physical structure at undisturbed sites may have limited environmental impact at the plot scale. However, at the patch scale, increases in snow depth, likely related to canopy complexity, were negatively associated with tundra specialist richness, potentially due to phenological limitations associated with shortened growing seasons. In summary, our data suggest emergent properties exist at the patch scale that are not apparent at the plot scale such that plot-scale measurements do not represent variation in understory community composition across the landscape. The results presented here will inform future work addressing spatial variability in shrub impacts on ecosystem function and increase our understanding of understory community variation within alder patch habitats at the taiga–tundra ecotone.

Ecological networks and corridors development in urban areas: An example of Tabriz, Iran

Landscape fragmentation and the elimination of urban green spaces are the results of human activities which put significant pressure on urban sustainability. The planning and developing of urban ecological networks and corridors as an effective approach is a response to rapid urbanization and fragmentation of natural areas. The Tabriz metropolitan as the fourth-largest city in Iran was selected as a case study that has grown rapidly over the past few decades. This study presents a practical approach and framework for assessing and enhancing ecological connectivity of landscape. The framework was developed based on the landscape metrics, graph theory, least-cost modeling, and geographic information system tools during three different periods (1984-2000-2020). The results using the values of landscape metrics such as CA, Edge Distance, Mean Patch Size, Mean Shape Index, large patch index, NP, Landscape shape index, and COHESION indicate that the urban landscape of Tabriz has been more fragmented over the past 3 decades and has lost its connectivity. To reduce the effects of fragmentation and enhance landscape connectivity in study area, we proposed a network of ecological corridors that passes through the core ecological patches. Our results also indicate that the core patches and the least-cost created corridors are mainly located in the suburbs of Tabriz and the central part of the city cannot be suitable for ecological development. Therefore, using the above-mentioned methods could be an effective approach to develop ecological networks and improve landscape connectivity that can encourage urban planners and managers to protect and develop green networks.

Structural Connectivity of Asia’s Protected Areas Network: Identifying the Potential of Transboundary Conservation and Cost-Effective Zones

Human activities can degrade landscape connectivity and disrupt ecological flows, jeopardising the functional integrity of processes. This study presents a quantitative assessment of Asia’s protected areas’ (PAs) structural connectivity using landscape metrics, as well as analyses of the Cost-Effective Zones’ (CEZs). Using nine landscape metrics, we assessed connectivity at zonal (country borders and interior), national, regional, and geographical (islands and continent) levels. The results showed that the structural connectivity of Asia’s PAs network measured by a Connectance index was very low (0.08% without country borders and 9.06% for the average country analysis). In general, connectivity was higher within borders (0.36%) than within the countries (0.22%). Islands exhibited significantly higher Area-weighted mean patch area, Proximity index and Largest patch index, suggesting more integrity and connectiveness. When comparing Asian regions, Western Asia presented the lowest values for Percentage of landscape and Proximity index. We found that only 15% of the CEZs in Asia were under PAs designation, and more CEZs are located in the interior, but the majority with the highest priority was in the borders (9%). We advocate that expanding PAs coverage, specifically targeting areas that increase connectivity (e.g., through transboundary PAs), should be a priority to maintain their ecological function.

These hills called home: quantifying urban forest dynamics in the hills of the Guwahati metropolitan area, india

ABSTRACT Forest loss and fragmentation are critical issues that confront urban landscapes. The urban forests in the hills of the Guwahati Metropolitan Area (GMA) in India have experienced significant transformations. This study assesses the temporal changes of forests in protected and non-protected hills of the GMA. Landsat imageries between 1976 and 2018 were used to understand changes in forest composition and fragmentation using landscape metrics namely, percentage of landscape, number of patches, mean patch size, patch density and largest patch index. The results revealed that the forests of GMA were experiencing intense losses and fragmentation due to increasing non-forest anthropogenic developments. The dense and moderately dense forests declined by 44 and 43%, respectively, as non-forest area increased by 1475 ha between 1976 and 2018. Dense forest demonstrated increasing fragmentation due to the rising number of small patches from 568 to 780. Today, dense forest patches are limited to only three of the eight reserved forests within GMA. The non-protected hills reported a significant 1309% increase of non-forest landuse. Thus, both protected and non-protected forests sustained substantial losses and fragmentation. The analysis could enable policymakers to prioritize urban forest conservation efforts in the GMA.

Attribution identification of terrestrial ecosystem evolution in the Yellow River Basin

Abstract The aim of this study was to identify the impacts of different driving factors on terrestrial ecosystem evolution. The Yellow River Basin was selected as the study area, of which terrestrial ecosystem was deeply affected by climatic change and human activities. We constructed four scenarios (including without any impacts, affected by climate change, by human activities and by both impacts), and the discrepancies between them reflected the impacts of climate change or human activities. Based on this, the future land use simulation model was used to simulate the land use distribution under the four scenarios, and then, the ecosystem services values (ESV) and landscape patterns index were evaluated. The results indicated that affected by climate change during 1995–2015, the Mean Patch Area of the forestland decreased by 0.19% and the landscape patterns became fragmented. Meanwhile, the total ESV decreased by 0.03 billion dollars and the ecosystem regulation services were weakened. Under the influences of human activities, the Contagion index decreased by 1.71% and the landscape patterns became dispersed. Simultaneously, the total ESV increased by 0.56 billion dollars, but the function tends to be unitary. In addition, these effects showed great spatial heterogeneity. This study provides scientific support for ecological protection in the Yellow River Basin.

HISTORIC LAND COVER CHANGE ASSESSMENT OF CHILEAN MEDITERRANEAN COAST: DID FOREST PLANTATIONS REALLY CAUSED FRAGMENTATION?

Abstract. Currently, the Mediterranean coast of Chile has large areas of commercial pine and eucalyptus plantations. Several authors have reported habitat loss and fragmentation of the original “Maulino” native forest due to the expansion of plantations. However, in the Maule coast took place a previous process that affected the native forest. This is the extensive use of forestlands with a moderate slope for the cultivation of wheat. The peak of this process occurred in the second half of the 19th century and meant a great degradation of the soil in the area. In this work, we first look for whether the overexploitation of wheat is the primary cause of the fragmentation of the Maulino forest in the study area. Secondly, we wanted to determine what proportion of the area currently occupied by plantations was previously used for growing wheat. First, using the community of Constitución as a representative area of the central coast we use Species Distribution Modelling to identify the potential original extent of the Maulino forest. Secondly, we created a spatial model to estimate the extension of wheat production using historical data. Finally, we used Landsat-8 images to assess the actual extension of forest plantation. By comparing the three former results, we were able to quantify the effects of wheat production and forest plantation separately, for the first time. The results indicate that 41% of the original native forest was first affected by wheat production and that only 34 % was later replaced by forest plantations. Therefore, the rest of the plantations were installed in places where the forest had previously been cut down for use in wheat production. The main cause of deforestation and forest fragmentation in this landscape was originally the wheat extensive production boosted by the gold rush in California and Australia. Forest commercial plantations further augmented the Maulino forest fragmentation process by significantly reducing its mean patch size and increasing the number of patches.

Scale dependence of landscape-structure-based estimation of abundance of Eurasian skylark (Alauda arvensis)

The habitat and occurrence of farmland birds are strongly determined by the agricultural-landscape structure. Changes in land cover composition and configuration are one of the main causes of the significant decline in abundance of the Eurasian skylark (Alauda arvensis) in recent decades. This farmland-bird species is common in agricultural areas of Eurasia. In this study we investigate the land use factors involved in the decline in Central Europe, Hungary. We used two different land use/land cover (LULC) datasets, which were compiled at different scales: the Ecosystem Map of Hungary (EMH), a very precise LULC map based on a 0.04-ha minimum mapping unit, and the Corine Land cover (CLC) dataset, built using a 25-ha minimum mapping unit. We studied the impact of landscape composition and configuration on skylark abundance by using negative-binomial generalized linear models. After identifying skylark preferences among LULC categories at different scales, we calculated the EMH and CLC dataset-based landscape indices (such as mean patch size and mean fractal dimension index) of the skylark preferred (arable lands, pastures, grasslands and meadows) and the nonpreferred (artificial surfaces, forests, complex cultivation patterns and waters) LULC classes. Then we compared the results with field observations of skylark abundance in the database of Hungarian Common Bird Monitoring (MMM). On the basis of statistical analysis of connections between the landscape indices and the skylark-abundance data, we estimated skylark abundance for those areas where the skylark-abundance datasets from field observation were not available. We also tested the estimates by assessing model sensitivity when we input different-scale LULC data and used different observation windows (grain size). Our statistical model using EMH dataset explained 41.22% variance of the skylark abundance data, while the rest, 58.78% of variance is not accounted by the model presumably due to local environmental factors not considered in our model. The regional scale (CLC-based) estimation of skylark abundance yielded significantly lower accuracies (33.76% in 1200 m radius buffer zones and 34.11% in 600 m radius buffer zones). We conclude that the size of the landscape windows (grain size) land cover has a significant impact on the relationship between skylark and landscape structure. Our results can help to test the usefulness and limitations of the different-scale LULC databases and to find the optimal grain size for modelling and estimating farmland-bird abundance data and may support landscape scale conservation management plans.

Spatial heterogeneity of ecosystem services in response to landscape patterns under the Grain for Green Program: A case‐study in Kaihua County, China

AbstractUnderstanding the impact of landscape patterns on ecosystem services (ES) is crucial for the design and management of ecological restoration programs. However, the impacts of landscape configuration on ES from a geospatial perspective have not been studied sufficiently. This study analyzed the landscape composition and pattern changes and quantified the changes in water yield, carbon storage, soil retention, water purification, and habitat quality under the Grain for Green Program (GFGP) from 2000 to 2020 in Kaihua County. The responses of ES to landscape patterns were revealed by geographic detector and geographically weighted regression models. The results showed that landscape fragmentation and diversity increased, and forest landscape area and aggregation increased because of the GFGP implementation. Water yield declined with the decrease in the mean patch area of the landscape, while other ES improved due to the increase in Shannon's diversity index and largest patch index. The interspersion and juxtaposition of cropland and forest land played an important role in improving ES under the GFGP. In addition, some ES may be closely related to natural factors and connection to landscape patterns, such as soil retention and topography, water yield and precipitation, but this response to landscape pattern changes was captured due to the subjective nature of the GFGP. The results can provide scientific guidance for forest landscape restoration to better improve ES.

A Spectral Mixture Analysis and Landscape Metrics Based Framework for Monitoring Spatiotemporal Forest Cover Changes: A Case Study in Mato Grosso, Brazil

An increasing amount of Brazilian rainforest is being lost or degraded for various reasons, both anthropogenic and natural, leading to a loss of biodiversity and further global consequences. Especially in the Brazilian state of Mato Grosso, soy production and large-scale cattle farms led to extensive losses of rainforest in recent years. We used a spectral mixture approach followed by a decision tree classification based on more than 30 years of Landsat data to quantify these losses. Research has shown that current methods for assessing forest degradation are lacking accuracy. Therefore, we generated classifications to determine land cover changes for each year, focusing on both cleared and degraded forest land. The analyses showed a decrease in forest area in Mato Grosso by 28.8% between 1986 and 2020. In order to measure changed forest structures for the selected period, fragmentation analyses based on diverse landscape metrics were carried out for the municipality of Colniza in Mato Grosso. It was found that forest areas experienced also a high degree of fragmentation over the study period, with an increase of 83.3% of the number of patches and a decrease of the mean patch area of 86.1% for the selected time period, resulting in altered habitats for flora and fauna.

The dominant factors and influence of urban characteristics on land surface temperature using random forest algorithm

• Multicellularity detection and random forest regression in the data analysis. • Partial dependence plots to reveal complex correlation. • BD is the most important factor influencing LST. • Social-economic factors are the least important to LST. • Non-linear relationship between urban characteristics and LST. Land surface temperature (LST) is influenced by a variety of factors. Relative studies have been carried out extensively in recent years. However, the intricate relationship has yet to be fully investigated. To assess the relative importance and interdependent effects of urban characteristics on LST, we chose the central part of Shanghai as the study area and used the random forest (RF) regression model to quantify 19 different variables on 3 different grid scales. Our results suggest that Building density (BD), percentage of water area (P_Water), percentage of vegetation area (P_Veg), building height (BH), distance to the Yangtze River (DIST), and mean patch area of green space (AREA_MN) are the 6 most important influencing factors, whereas socioeconomic factors such as point of interest (POI) and nighttime light data (NTL) are the least correlated to LST changes. Meanwhile, most of these 6 variables showed non-linear correlations with LST. In particular, the Yangtze River has a steady cooling effect up to 20 km, after which it declines rapidly and loses its effect at 25 km, and BH shows a fluctuated correlation with LST. This improved knowledge of the relationship between urban characteristics and LST will serve as a guide for future urban policymakers and planners.

Landscape context determines soil fungal diversity in a fragmented habitat

Habitat fragmentation is a primary cause of biodiversity loss. As an essential part of the ecosystem, soil microorganisms participate in a series of ecosystem processes. However, the role of landscape factors on soil microorganisms is not well understood. Based on high-throughput sequencing of soil samples at three depths (0–10 cm, 10–20 cm, 20–30 cm) from 30 landscape sites along a habitat fragmentation gradient, we calculated soil bacterial and fungal diversity in the agro-pastoral ecotone of northern China. We then investigated the impact of climatic factors, soil characteristics, and landscape context (patch density, edge density, mean patch size, and mean nearest-neighbor distance) on soil bacterial and fungal diversity. We found that soil fungal richness increased with habitat fragmentation (patch density and edge density), although soil bacterial richness did not change significantly. Soil bacterial and fungal community composition both changed with habitat fragmentation. Soil characteristics were key factors determining soil bacterial diversity, especially in the 10–20 cm soil depth. Soil fungal diversity was closely related to landscape context, showing a significant positive correlation with patch density and edge density, and a significant negative correlation with mean patch size. Structural equation modeling showed that landscape factors directly affected soil fungal diversity but indirectly affected soil bacterial diversity by changing soil characteristics. We highlight that soil fungal diversity shows an increasing trend with increased habitat fragmentation. Landscape context plays a stronger role in maintaining soil fungal diversity than soil characteristics and climatic factors.

Application of Landscape Metrics and Object-Oriented Remote Sensing to Detect the Spatial Arrangement of Agricultural Land

AbstractThis study aims to investigate crop selection and spatial patterns of agricultural fields in a drought-affected region in Isfahan Province, central Iran. Based on field surveys portraying growth stages of the main crops including wheat, alfalfa, vegetables and fruit trees, three Landsat 8 operational land imager (OLI) images were acquired on March 15 (L1), June 27 (L2) and October 1 (L3), 2015. After performing radiometric and atmospheric corrections, Normalized Difference Vegetation Index (NDVI) maps of the images were produced and introduced to the Multi-Resolution Segmentation algorithm to delineate agricultural fields. An NDVI-based decision algorithm was then developed to identify crops devoted to each field. Finally, a set of landscape metrics including Number of Patches (NP), mean patch size (MPS), mean shape index (MSI), perimeter-to-area ratio (PARA) and Euclidian Nearest Neighborhood Distance (ENN) was utilized to evaluate their respective spatial formation. The results showed that nearly 46% of fields are devoted to wheat indicating that the landscape has been dramatically shifted towards wheat monoculture farming. Moreover, the farmers’ inclination to grow crops in large fields (approximate area of 1 ha) with more regular geometric shapes are considered as an effective way of optimising water use efficiency in areas experiencing significant water shortage.

Mapping disturbance in mangrove ecosystems: Incorporating landscape metrics and PCA-based spatial analysis

Mangrove ecosystems are of high ecological value and occur in tropical and subtropical coastal areas. They consist of salt-tolerant woody plants and are highly complex and productive.Here, a novel approach was developed to map disturbances and evaluate the ecological status of Iranian mangroves based on multispectral WorldView-2 and Sentinel-2 and remote sensing techniques. A land cover map was generated that serves as basis to assess changes in the composition and structure of mangrove ecosystems. The spatial disturbance index (SDI) was calculated based on landscape metrics and principal component analysis (PCA) in order to detect mangroves degraded on Qeshm Island, Iran. The approach comprised: (I) selection of landscape metrics, (II) definition of the hexagon cell scale, (III) completion of a PCA of the standardized values ​​of eight selected metrics, and (IV) modeling of the SDI based on eigenvalues and a loading factor. For validation, the variance inflation factor (VIF) was used. The results indicate that the best set of key metrics to quantify SDI comprises the eight metrics patch density (PD), total edge (TE), mean patch size (MPS), mean shape index (MSI), Euclidean nearest neighbor distance (ENND), contagion (CONT), splitting index (SPLIT), and Shannon’s diversity index (SHDI). The first three components of the PCA explained in total 87% (47%, 27%, and 13%, respectively) of the variance of information distribution, demonstrating the effectiveness of the PCA. Then, based on the disturbance index, five spatial levels, ranging from very low to very high disturbances, were identified. This approach enabled to detect the zonation of disturbance that has occurred in the region and supports the sustainable management of mangrove ecosystems and future planning efforts, thus making their conservation more realistic.

Identifying differences in roadless areas in Canada based on global, national, and regional road datasets

AbstractRoads are an overwhelming component of the global human footprint and their absence helps identify intact areas with high ecological value. Road‐free areas are decreasing globally, making accurate estimation of their location and size of great importance. Identification of such regions requires accurate data, but substantial variability exists in road network datasets created and maintained at different spatial scales. We compared estimates of road length, density, and roadless areas across Canada, which contains a high proportion of the world's remaining undisturbed and road‐free areas. Global‐ and national‐scale datasets included, on average, only 11%–14% of roads represented in regional‐scale data or volunteered geographic information (VGI), with the most pronounced differences in less‐developed areas. Regional‐scale datasets, with the lowest estimates of amount of roadless area and smallest mean roadless patch size, are likely the most complete road datasets but are not available for all jurisdictions, limiting their national‐scale utility. VGI provides a national‐scale alternative but still lacks many low‐use roads. Available global and national datasets have insufficient information for accurate assessments of roadless areas in Canada, which will require detailed, consistent subnational datasets assembled and maintained by each province and territory in a coordinated fashion to achieve national coverage.

Simultaneous effect of habitat remnancy, exotic species, and anthropogenic disturbance on orchid diversity in South Australia

AbstractOrchids are potentially useful as ecological indicators because of their sensitivity to habitat fragmentation and anthropogenic disturbance. While many studies explore the effect of single factors on orchid diversity, few investigate how the extent, configuration, and condition of surrounding habitat affect whole orchid communities. Here, we unravel the effect of biological invasions, anthropogenic disturbance (i.e., grazing pressure, ecological condition), and habitat fragmentation on an Australian orchid community. We sampled 39 plots across nine sites in the Mount Lofty Ranges, Australia. We recorded the number of orchid species and number of individuals per species in mid‐winter, early‐spring, and late‐spring to account for the effect of season on species visibility, with 115 surveys in total. We ranked grazing intensity and ecological condition, and estimated cover of exotic species. We analyzed the response of richness and diversity through generalized linear mixed models, and differences in species composition through non‐metric multidimensional scaling. Habitat configuration in the surrounding landscape had different effects at increasing radii, explaining 29%–87% of variance. Patch‐level orchid diversity was positively correlated with habitat edges in the immediate area, and with habitat cohesion at medium scales, whereas diversity was negatively correlated with increasing mean patch habitat area across larger surrounding areas. Orchids co‐existed with exotic species but were negatively affected once exotic cover exceeded 20%. Species composition was correlated with exotic cover. Our findings reveal a complex relationship between orchid communities and their surrounding environments suggesting orchids benefit from a somewhat disturbed environment at patch and landscape scales. These idiosyncratic responses suggest orchid diversity may be unreliable as early‐warning indicators of habitat disturbance.

A New Approach to Investigate the Spatially Heterogeneous in the Cooling Effects of Landscape Pattern

Although many prior studies have found that landscape pattern significantly affects urban heat environment globally, the spatially heterogeneous in the cooling effects of landscape pattern remains poorly understood. In addition, most previous studies have only employed a single landscape metric separately, without holistic consideration of the composition and configuration of different landscapes. Taking one of the new “stove” cities in China-Fuzhou City, Fujian Province, as an example, we employed the principal component analysis (PCA) to synthesize a landscape pattern comprehensive index (LPCI) composed of the four common landscape metrics (i.e., aggregation index, AI; mean patch area, Area mn; largest patch index, LPI; and percentage of landscape, PLAND) of the three major land surfaces (i.e., water, vegetation, and impervious surface). Then, the local model (geographically weighted regression, GWR) was proposed to explore the spatially heterogeneous in the cooling effects of urban landscape. The results revealed that: (1) from 2000 to 2016, the land surface temperature (LST) increased by 4.262 °C, and the proportion of the urban heat island region showed an upward trend, while the urban-heat-island ratio index (URI) increased from 0.328 to 0.457; (2) the cooling effect of different land surfaces ranked from high to low was: water (29.69 °C), vegetation (38.56 °C), and impervious surface (41.82 °C); (3) compared with vegetation patches, water patches had a more obvious cooling effect on the surrounding environment, with the cooling distance within 60–90 m for the vegetation, while reaching 120–150 m for water body; (4) the proposed LPCI could explain more than 80% of the information for all of the landscape metrics for all of the landscape types, and presented a patchy distribution in the study area; (5) the GWR results revealed that the cooling effect of the landscape pattern varied spatially across the study area, indicating that the configuration of landscapes is more important in an urban center in alleviating urban heat environment than in an urban fringe area. The proposed approach provides a new understanding of the interaction between the landscape patterns and urban heat environments, providing a strong basis for landscape planning strategies for specific local sites.