Landscape Composition Metrics Research Articles

Heterogeneity of wetland landscapes and their relationships with anthropogenic disturbances and precipitation in a semiarid region of China.

Wetlands in semiarid regions have important ecological functions and provide services that are partly dependent on the landscape pattern and structure; therefore, it is important to assess the heterogeneity of wetland landscape conditions for wetland conservation and management. Widespread wetland degradation resulting from main factors in semiarid regions highlights the need for exploring the heterogeneity of wetland landscapes and their relationships with anthropogenic disturbances and precipitation. Wetlands in our study referred to natural wetlands, including marshes, rivers, and lakes. We used overall landscape metrics to assess wetland landscape conditions and used grid landscape metrics at a scale of 30km using a moving window method to explore the heterogeneity of wetland landscapes during the period (1986-2019) for western Jilin Province. We performed Spearman correlation analyses of the landscape metrics with the Hemeroby index (HI) and mean annual precipitation (MAP). We suggested that wetland landscape composition metrics (e.g., NP and PD) and configuration metrics (e.g., ED and AI) had positive correlations with HI by 2019 (p < 0.01); in addition, wetland landscape composition metrics (e.g., NP and PD) and configuration metrics (e.g., ED) were negatively correlated with MAP during 1986-2019. In conclusion, wetland landscapes have become increasingly fragmented over the past few decades (1986-2019) in western Jilin Province and the reasons for this result mainly include the household contract responsibility system, along with rural productivity, severe overgrazing and reclamation, and climate change. Wetland landscape composition was positively correlated with human interference and negatively correlated with mean annual precipitation.

Relating landscape ecological metrics with public survey data on perceived landscape quality and place attachment

ContextIt is essential for policy-making and planning that we understand landscapes not only in terms of landscape ecological patterns, but also in terms of their contribution to people's quality of life.ObjectivesIn this study our objective is to test relationships between landscape ecology and social science indicators, by investigating how landscape patterns are linked to people’s perception of landscape quality.MethodsTo assess public views on landscapes we conducted a survey among 858 respondents in Switzerland. We combined this survey data on perceived landscape quality and place attachment with landscape metrics (e.g. diversity, naturalness of land cover, urban sprawl, fragmentation) in a statistical model to test hypotheses about the relationships between the different variables of interest.ResultsOur results illustrate the contribution of both landscape composition metrics and social science indicators to understanding variation in people’s perception and assessment of landscape. For example, we found the landscape ecology metrics on urban sprawl and fragmentation to be a negative predictor of overall satisfaction with landscape, and that perceived landscape quality positively predicted place attachment and satisfaction with the municipality landscape.ConclusionsThis study highlights the importance and feasibility of combining landscape ecology metrics and public survey data on how people perceive, value and relate to landscape in an integrated manner. Our approach has the potential for implementation across a variety of settings and can contribute to holistic and integrated landscape assessments that combine ecological and socio-cultural aspects.

Spatial scale effects of landscape metrics on stream water quality and their seasonal changes

Physiography and land use patterns influence streams water quality by affecting non-point source (NPS) pollution process. However, each landscape factor may affect the NPS pollution process differently with the variations of the spatial scale and season. Thus, quantitative analysis of each landscape metrics scale effect and determination of the abrupt change-point in the relationship between stream water quality and the metrics is very helpful for landscape planning of water quality protection. Based on water quality monitoring data for four years in 12 sub-watersheds of a typical headwater watershed in Eastern China, we adopted regular and partial redundancy methods to quantify the spatial scale effects and seasonal dependence of various landscape metrics impact on stream water quality, and then to identify the abrupt change-point of the water quality along the gradient of landscape metrics. Results revealed that the pure effects of different categories of landscape metrics on stream water quality were in the following order: landscape configuration metrics (20.5–31.6%) > physiographic metrics (4.0–15.9%) >landscape composition metrics (3.2–7.5%). The spatial scale effect of physiography impact on stream water quality was the most significant, while the impact of landscape configuration on water quality had the highest seasonal sensitivity. The overall water quality variation was better explained by buffer zone scale than by catchment scale landscape characteristics, and this phenomenon was more obvious during the wet season than during the dry season. In the studied watershed, we identified the largest patch index of farmland (LPIfar) and the landscape shape index of forest (LSIfor) as the key landscape metrics at sub-watershed scale and buffer zone scale, respectively. The LPIfar > 7.0% at the sub-watershed scale and LSIfor < 5.5 at the buffer zone scale were suggested as the preferred landscape planning parameters to protect the stream water quality efficiently. Results indicated that, to protect water quality, landscape regulation should follow the scale-adaptability measures and consider the landscape thresholds, which cause abrupt changes in water quality.

Agricultural Landscape Composition Linked with Acoustic Measures of Avian Diversity

Measuring, monitoring, and managing biodiversity across agricultural regions depends on methods that can combine high-resolution mapping of landscape patterns with local biodiversity observations. This study explores the potential to monitor biodiversity in agricultural landscapes by linking high-resolution remote sensing with passive acoustic monitoring. Land cover maps produced using a small unmanned aerial system (UAS) and PlanetScope (PS) satellite imagery were used to investigate relationships between landscape patterns and an acoustically derived biodiversity index (vocalizing bird species richness) across 12 agricultural sample locations equipped with acoustic recorders in Iowa, USA during the 2018 growing season. Statistical assessment revealed a significant direct association between vocalizing bird richness and percent noncrop vegetation cover. High spatial resolution (1 m) UAS mapping produced stronger statistical associations than PS-based maps (3 m) for landscape composition metrics. Landscape configuration metrics (Shannon’s diversity index, contagion, perimeter-area-ratio, and circumscribing circle index) were either cross-correlated with composition metrics or unusable owing to complete landscape homogeneity in some agricultural landscape samples. This study shows that high resolution mapping of noncrop vegetation cover can be linked with acoustic monitoring of unique bird vocalizations to provide a useful indicator of biodiversity in agricultural landscapes.

Patterns and drivers of the scale of effect of landscape structure on diurnal raptors in a fragmented tropical dry forest

Theoretical models propose that the spatial extent at which landscape structure best predicts species responses (scale of effect, SoE) depends on habitat and dietary specialization, landscape metrics, and response variables. However, empirical support for such models is scarce, especially for apex predators. To determine SoE for diurnal raptors, and test for differences among ecological traits of habitat and dietary specialization, landscape metrics, and response variables. We conducted 1.5 km transect surveys of diurnal raptors at 26 sites in a tropical dry forest from western Mexico. We measured four landscape metrics (forest cover, matrix hardness, forest patch density, edge density) in 16 concentric landscapes (400 to 3400 ha) around each survey site. We then assessed the landscape size at which each landscape metric best predicted each response variable (abundance, species richness, temporal beta diversity). Finally, we tested for differences in SoE among ecological traits of raptors, landscape metrics, and response variables. Landscape composition metrics of forest cover and matrix hardness decreased with increasing landscape size, and were the main landscape predictors influencing diurnal raptors. SoE differed significantly among landscape metrics, being larger for forest cover (2433.3 ha) than matrix hardness (1500 ha). However, SoE did not differ significantly among ecological traits of raptors, or response variables. SoE was mainly driven by spatial metrics, with diurnal raptors being more strongly associated with forest cover measured over larger scales. This supports findings for other taxa and suggests that forest cover influences dispersal success across larger scales.

Performance of landscape composition metrics for predicting water quality in headwater catchments

Land use is a predominant threat to the ecological integrity of streams and rivers. Understanding land use-water quality interactions is essential for the development and prioritization of management strategies and, thus, the improvement of water quality. Weighting schemes for land use have recently been employed as methods to advance the predictive power of empirical models, however, their performance has seldom been explored for various water quality parameters. In this work, multiple landscape composition metrics were applied within headwater catchments of Central Europe to investigate how weighting land use with certain combinations of spatial and topographic variables, while implementing alternate distance measures and functions, can influence predictions of water quality. The predictive ability of metrics was evaluated for eleven water quality parameters using linear regression. Results indicate that stream proximity, measured with Euclidean distance, in combination with slope or log-transformed flow accumulation were dominant factors affecting the concentrations of pH, total phosphorus, nitrite and orthophosphate phosphorus, whereas the unweighted land use composition was the most effective predictor of calcium, electrical conductivity, nitrates and total suspended solids. Therefore, both metrics are recommended when examining land use-water quality relationships in small, submontane catchments and should be applied according to individual water quality parameter.

Effects of agricultural lands on habitat selection and breeding success of American kestrels in a boreal context

Sudden changes in habitat quality during the breeding season may mislead individuals when selecting their nesting site and result in population declines. In such cases, even semi-natural and extensive agricultural lands may become ecological traps. We examined how the availability of six open habitat types (i.e. agricultural lands, open forests, alder swamps, young forests, regeneration, and wetlands) could be affecting the habitat selection process, as well as the hatching and fledging successes of American kestrels (Falco sparverius). We hypothesized that natural open habitats are less disturbed by anthropogenic activities than extensive agricultural lands and thus represent higher quality habitats for kestrels. We also considered weather conditions during the breeding season as possible factors affecting hatching and fledging successes. We monitored 200 pairs of American kestrels during 11 years (2006–2016) within a network of 155 nest box stations and we characterized landscape composition metrics within 800 m radii from each nest box. We used generalized linear mixed models and multimodel inference to quantify the effects of landscape composition metrics on the probabilities of using nesting site, hatching success, and fledging success of American kestrels. We also tested the effects of weather conditions and clutch initiation date on hatching and fledging successes of kestrels. We found that the probability of nesting site use increased with the amount of agricultural lands. Hatching success decreased with the amount of agricultural lands, whereas the fledging success of kestrels did not vary with the amount of agricultural lands. Both the probabilities of hatching and of fledging increased with the area of young forests. There was no evidence of a weather effect on hatching success. However, the probability of fledging success increased with mean temperature during the raising period of nestlings. Although fledging success alone does not determine fitness or population dynamics, our results suggest that kestrels nesting in this region at the northern limit of their range may be caught in an ecological trap by extensive agricultural lands. Indeed, kestrels were attracted by meadows when selecting nesting habitat, but the hatching and early nestling periods coincided with hay harvesting which could reduce the hunting success of breeding adults and suddenly alter food availability. Although the causes of recent kestrel declines remain unclear, our results suggest that harvesting practices, even those related to extensive perennial agriculture, may have a negative effect on the breeding success of the species compared to areas dominated by young forests.

Multilevel assessments reveal spatially scaled landscape patterns driving coastal fish assemblages

Ecological research, particularly in marine environments, tends to focus on single habitats and often single spatial scales, and thus not account for ecological processes operating at multiple spatial scales. Here we aim to explore how coastal fish assemblages are influenced by landscape patterns integrating multiple spatial scales, to assess the strength of these associations and to identify the most relevant spatial scales at which these associations occur. We use a multiscale approach through multilevel modelling to evaluate the association of landscape metrics with fish assemblages, at three nested spatial scales, in temperate coastal seascapes composed of seagrass meadows, sandy bottoms and rocky reefs. Landscape composition metrics, expressed as cover of vegetated habitats, significantly influenced fish assemblages at small (metres) and intermediate (hundred of metres) scales, while landscape configuration metrics did it at all three scales assessed (from metres to kilometres). Species richness was only influenced by small scale landscape patterns (cover of rocky reefs, positive association), whereas total abundance was associated with landscape patterns measured at small and intermediate scales, encompassing metrics associated with landscape composition (rocky reef cover, positive association), and those indicating landscape heterogeneity (negative association). Similarly, the abundances of different functional groups were influenced by metrics consistent with their mobility and their ecological and behavioural traits at all the spatial scales assessed. These results show how landscape patterns influence coastal fish assemblages, and particularly show that spatially scaled landscape patterns, measured in complex ecological systems as a whole, act simultaneously but not always equally on species assemblages.

The relationship between urban form and air pollution depends on seasonality and city size.

Understanding how urban form is related to air pollution is important to urban planning and sustainability, but the urban form-air pollution relationship is currently muddled by inconsistent findings. In this study, we investigated how the compositional and configurational attributes of urban form were related to different air pollution measures (PM2.5, API, and exceedance) in 83 Chinese cities, with explicit consideration of city size and seasonality. Ten landscape metrics were selected to quantify urban form attributes, and Spearman's correlation was used to quantify the urban form-air pollution relationship. Our results show that the urban form and air pollution relationship was dominated by city size and moderated by seasonality. Specifically, urban air pollution levels increased consistently and substantially from small to medium, large, and megacities. The urban form-air pollution relationship depended greatly on seasonality and monsoons. That is, the relationship was more pronounced in spring and summer than fall and winter, as well as in cities affected by monsoons. Urban air pollution was correlated more strongly with landscape composition metrics than landscape configuration metrics which seemed to affect only PM2.5 concentrations. Our study suggests that, to understand how air pollution levels are related to urban form, city size and seasonality must be explicitly considered (or controlled). Also, in order to mitigate urban air pollution problems, regional urban planning is needed to curb the spatial extent of built-up areas, reduce the degree of urban fragmentation, and increase urban compactness and contiguity, especially for large and megacities.

Evaluating the effectiveness of landscape configuration metrics from landscape composition metrics

Although landscape configuration and landscape composition metrics are correlated theoretically and empirically, the effectiveness of configuration metrics from composition metrics has not been explicitly investigated. This study explored to what extent substantial information of configuration metrics increases from certain easily calculated and extensively used composition metrics and how strongly the effectiveness is influenced by different factors. The effectiveness of 12 landscape configuration metrics from the percentage of landscape (PLAND) of each land-use class and patch density (PD) was evaluated through the coefficient of determination (R 2) of multivariate stepwise linear regression analysis of 150 town-based landscape samples from three regions. The different landscape configuration metrics from PLAND and PD presented significantly different performances in terms of effectiveness [the contagion index and aggregation index possess minimal information, and the effective mesh size (MESH) and area-weighted mean patch fractal dimension possess abundant information]. Furthermore, the effectiveness of configuration metrics showed different responses to changing cell sizes and different land-use categorization in different regions (interspersion and juxtaposition index, patch cohesion index, and MESH exhibited large variations in R 2 among the different regions). No single, uniform, consistent characteristic of effectiveness was determined across different factors. This new approach to understanding the effectiveness of configuration metrics helps clarify landscape metrics and is fundamental to landscape metric assessment.

Land-use and land-cover effects on soil microbial community abundance and composition in headwater riparian wetlands

Headwater riparian wetlands are relatively small in size but functionally significant as expected hot spots of microbial activity in the landscape. Despite their roles as biogeochemical drivers, little is known about how microbial communities in headwater riparian wetlands are affected by surrounding land-uses and land-covers (LULCs). The primary objective of this study was to determine if and how wetland soil microbial abundance and community composition varied as a function of landscape metrics as mediated through on-site edaphic properties. Forty-two soil samples, collected from eight headwater riparian wetlands in the Ridge and Valley Region of central Pennsylvania, were used for phospholipid fatty acid (PLFA) profiling of soil microbial communities. These samples were used to create microbial habitat models describing plot-level relationships between edaphic properties and microbial measures (i.e., microbial biomarker abundances, ratios and composition). Soil organic matter (SOM) content was a strong predictor of microbial biomarker abundances and fungi/bacteria ratios, while soil pH was a strong predictor of microbial composition (i.e., relative abundance of individual fatty acids) and potential microbial stress indices (i.e., cy19:0a/18:1ω7c and cy17:0/16:1ω7c ratios). Soil texture, soil moisture, and litter total nitrogen had smaller, but significant effects in these empirical microbial habitat models. Microbial habitat models were subsequently used to estimate microbial measures for a larger regional headwater riparian wetland dataset (n = 87), where edaphic property information was compiled. Site-average microbial measures were correlated with wetland elevation, and with landscape composition metrics in a landscape assessment area (i.e., 125,664 m2). Wetland elevation explained high among-site variability in microbial abundance measures, as mediated through SOM content, in headwater riparian wetlands in forested landscapes. However, wetland elevation was confounded by landscape composition, for headwater riparian wetland in mixed forested-agricultural landscapes. Hydrology, geomorphology, and changes in vegetation could be used to explain SOM variation across wetland elevation and variability in surrounding landscape composition. Microbial composition measures, including the cy19:0/18:1ω7c ratio and principal component (PC) axes derived from 23 individual PLFA biomarkers were negatively related to the Landscape Development Intensity (LDI) index, and the percent area of row crops, pastures and grasslands, and positively related to the percent area of forest. The cy17:0/16:1ω7c ratio was positively related to the LDI index, and the percent area of pasture and grasslands, and negatively related to the percent area of forest and wetland elevation. Differences along PC axes were most clearly related to the relative abundances of fungal communities; headwater riparian wetland in forested landscapes had significantly higher relative abundances of saprophytic fungi biomarkers, while headwater riparian wetland in mixed landscapes had significantly higher relative abundances of the arbuscular mycorrhizal fungi biomarker. This study highlights the utility of simple landscape metrics, which describe wetland position and landscape composition, for predicting differences in edaphic variability and associated microbial community composition and biomass among headwater riparian wetlands at a regional scale.

Análisis del cambio en la conectividad estructural del paisaje (1975-2008) de la cuenca del lago Cuitzeo, Michoacán, México

The Cuitzeo Lake Basin is covered with forests and scrublands with high ecological importance, which have experienced fragmentation resulting from human activities. In this study we analyzed changes in the structural connectivity of the Cuitzeo Lake basin, Michoacan, between 1975 and 2008. We used land cover databases for the years 1975, 1996, 2000, 2003 and 2008. For each year, landscape composition metrics were applied, such as isolation and structural connectivity metrics at the landscape and class levels. Finally we performed a comparative analysis. The forest cover presented low structural connectivity values in contrast to the human activity related land covers, which dominated the landscape over time. The information on changes in landscape structural connectivity and fragmentation from this study provide a useful tool for landscape management and biodiversity conservation.

Influence of landscape composition on northern bobwhite population response to field border establishment

ABSTRACTSince the 1960s, habitat loss resulting from cleaner farming, increased urbanization, and maturation of early successional cover has caused range‐wide decline of northern bobwhite (Colinus virginianus). Although field borders increase bobwhite habitat and increase local populations, understanding how the surrounding landscape influences bobwhite response to this management practice is critical to efficient implementation. We determined the relative influence of landscape composition and field border implementation on bobwhite densities and occupancy dynamics around crop fields in North Carolina and South Carolina, USA. We used 10‐minute distance point counts to estimate density, occupancy, colonization, and extinction rates of male bobwhite around 154 agriculture fields, half of which had a fallow field border. We estimated percent of cropland, forest, pasture, early successional, and urban cover within 1‐km radius buffers (314 ha) surrounding all point count locations. We examined the influence of 6 predictor variables (landscape composition metrics and field border presence) on bobwhite density and occupancy dynamics. Bobwhite density increased with the presence of field borders. Conversely, bobwhite density decreased as the percentage of urban, pasture, and forest lands increased. The presence of a field border did not influence occupancy, colonization, or extinction rates. However, as the percentage of cropland increased within the landscape, bobwhite occupancy increased and as the percentage of pasture increased, bobwhite colonization decreased. As the percentage of forest and urban increased and cropland decreased, bobwhite extinction rate increased. Our results indicated that local establishment of field borders does not increase bobwhite occupancy rates, but field borders can increase densities in suitable landscapes where bobwhite are present. Habitat restoration for bobwhite will most effectively increase population densities if focused in landscapes dominated by suitable cover types, where bobwhite occurrence is high. © 2013 The Wildlife Society.

Modeling broad-scale patterns of avian species richness across the Midwestern United States with measures of satellite image texture

Avian biodiversity is threatened, and in order to prioritize limited conservation resources and conduct effective conservation planning a better understanding of avian species richness patterns is needed. The use of image texture measures, as a proxy for the spatial structure of land cover and vegetation, has proven useful in explaining patterns of avian abundance and species richness. However, prior studies that modeled habitat with texture measures were conducted over small geographical extents and typically focused on a single habitat type. Our goal was to evaluate the performance of texture measures over broad spatial extents and across multiple habitat types with varying levels of vertical habitat structure. We calculated a suite of texture measures from 114 Landsat images over a study area of 1,498,000km2 in the Midwestern United States, which included habitats ranging from grassland to forest. Avian species richness was modeled for several functional guilds as a function of image texture. We subsequently compared the explanatory power of texture-only models with models fitted using landscape composition metrics derived from the National Land Cover Dataset, as well as models fitted using both texture and composition metrics. Measures of image texture were effective in modeling spatial patterns of avian species richness in multiple habitat types, explaining up to 51% of the variability in species richness of permanent resident birds. In comparison, landscape composition metrics explained up to 56% of the variability in permanent resident species richness. In the most heavily forested ecoregion, texture-measures outperformed landscape metrics, and the two types of measurements were complementary in multivariate models. However, in two out of three ecoregions examined, landscape composition metrics consistently performed slightly better than texture measures, and the variance explained by the two types of measures overlapped considerably. These results show that image texture measures derived from satellite imagery can be an important tool for modeling patterns of avian species richness at broad spatial extents, and thus assist in conservation planning. However, texture measures were slightly inferior to landscape composition metrics in about three-fourths of our models. Therefore texture measures are best considered in conjunction with landscape metrics (if available) and are best used when they show explanatory ability that is complementarity to landscape metrics.