Abstract:Landscape metrics constitute one of the main tools for the study of the changes of the landscape and of the ecological structure of a region. The most popular software for landscape metrics evaluation is FRAGSTATS, which is free to use but does not have free or open source software (FOSS). Therefore, FOSS implementations, such as QGIS's LecoS plugin and GRASS' r.li modules suite, were developed. While metrics are defined in the same way, the "cell neighborhood" parameter, specifying the configuration of the mo… Show more
“…Division index yields the probability that two randomly selected cells are not located in the same patch. Mean Euclidean nearest‐neighbour distance accounts for the number of highly isolated patches, whereas aggregation index evaluates the frequency with which patch pairs occur side‐by‐side in the landscape (McGarigal et al 2012, Zatelli et al 2019). We selected these metrics as they can be successfully used to compare fragmentation among different landscapes and, in our case, different time bins (He et al 2000, Cornejo‐Denman et al 2020).…”
The idea that several small, rather than a single large, habitat areas should hold the highest total species richness (the so-called SLOSS debate) brings into question the importance of habitat fragmentation to extinction risk. SLOSS studies are generally addressed over a short time scale, potentially ignoring the long-term dimension of extinction risk. Here, we provide the first long-term evaluation of the role of habitat fragmentation in species extinction, focusing on 22 large mammal species that lived in Eurasia during the last 200 000 years. By combining species distribution models and landscape pattern analysis, we compared temporal dynamics of habitat spatial structure between extinct and extant species, estimating the size, number and degree of the geographical isolation of their suitable habitat patches. Our results evidenced that extinct mammals went through considerable habitat fragmentation during the last glacial period and started to fare worse than extant species from about 50 ka. In particular, our modelling effort constrains the fragmentation of habitats into a narrow time window, from 46 to 36 ka ago, surprisingly coinciding with known extinction dates of several megafauna species. Landscape spatial structure was the second most important driver affecting megafauna extinction risk (ca 38% importance), after body mass (ca 39%) and followed by dietary preferences (ca 20%). Our results indicate a major role played by landscape fragmentation on extinction. Such evidence provides insights on what might likely happen in the future, with climate change, habitat loss and fragmentation acting as the main forces exerting their negative effects on biodiversity.
“…Division index yields the probability that two randomly selected cells are not located in the same patch. Mean Euclidean nearest‐neighbour distance accounts for the number of highly isolated patches, whereas aggregation index evaluates the frequency with which patch pairs occur side‐by‐side in the landscape (McGarigal et al 2012, Zatelli et al 2019). We selected these metrics as they can be successfully used to compare fragmentation among different landscapes and, in our case, different time bins (He et al 2000, Cornejo‐Denman et al 2020).…”
The idea that several small, rather than a single large, habitat areas should hold the highest total species richness (the so-called SLOSS debate) brings into question the importance of habitat fragmentation to extinction risk. SLOSS studies are generally addressed over a short time scale, potentially ignoring the long-term dimension of extinction risk. Here, we provide the first long-term evaluation of the role of habitat fragmentation in species extinction, focusing on 22 large mammal species that lived in Eurasia during the last 200 000 years. By combining species distribution models and landscape pattern analysis, we compared temporal dynamics of habitat spatial structure between extinct and extant species, estimating the size, number and degree of the geographical isolation of their suitable habitat patches. Our results evidenced that extinct mammals went through considerable habitat fragmentation during the last glacial period and started to fare worse than extant species from about 50 ka. In particular, our modelling effort constrains the fragmentation of habitats into a narrow time window, from 46 to 36 ka ago, surprisingly coinciding with known extinction dates of several megafauna species. Landscape spatial structure was the second most important driver affecting megafauna extinction risk (ca 38% importance), after body mass (ca 39%) and followed by dietary preferences (ca 20%). Our results indicate a major role played by landscape fragmentation on extinction. Such evidence provides insights on what might likely happen in the future, with climate change, habitat loss and fragmentation acting as the main forces exerting their negative effects on biodiversity.
“…Consequently, the assessment of the landscape metrics was applied to obtain landscape statistics [13]. Subsequently, landscape metrics were calculated in relation to the raster layer [14].…”
The use of remote sensing in the design of land use mapping allows analyses of landscape evolution during a certain period of time which helps studies in a global scope. The objective of this study is to identify and analyze changes in characteristics of rural land use in the municipality of Passo Fundo, located in the state of Rio Grande do Sul, Brazil, during the years 2001 and 2020, through images taken from the Landsat TM-7 and TM-8 satellites. Methodologically, satellite images were classified by supervised methods, generating thematic maps, and taking into account the following groups: tillage (growing area), forest, exposed soil and water resources. Results demonstrated that the process of connecting agricultural crop patches went from 5.495 in 2001 to a figure of 10.812 in 2020, thus having an increase of 96%.
“…The oldest imagery set belongs to the "Volo GAI" (Gruppo Areonautico Italiano, Italian Aeronautical Group) of 1954. The aereal images are available as 23 × 23 cm prints and have been digitized at 600dpi after tests have shown that higher resolutions do not carry any significative advantage (Gobbi et al, 2019a).…”
Section: Orthophotosmentioning
confidence: 99%
“…The flight covers the whole Italy and the ortho-rectified images were not available for the Trentino region, but for a few small areas. Therefore a set of 130 B/W images, with mean scale of 1:35,000 and ground resolution of 2m, have been ortho-rectified in the Italian national Rome40 datum using GRASS GIS (Gobbi et al, 2019a…”
Abstract. In the last decades the Alpine landscape has dramatically changed due to social and economic factors. The most visible impact has been the reduction of the population for mid and high altitude villages and the shrinking of the part of the land used for agriculture and grazing, with a progressive reduction of pastures and meadows and the expansion of the forested areas. For these reasons, a dataset describing the forest, meadows and pasture coverage for the Trentino region, in the eastern Italian Alps, has been created. A set of heterogeneous sources has been selected so that maps and images cover the longest possible time span on the whole Trentino region with comparable quality, creating a Land Use/Land Cover (LULC) map based on historical maps from 1859 to 1936 and aerial images from 1954 to 2015. The achieved accuracy ranges from 98% for historical maps to 94% for aereal imagery. The analysis of selected landscape metrics provided preliminary results about the forest distribution and patterns of recolonization during the last 155 years. It has been possible to create future scenarios for the forest evolution for the next 85 years. Given the large number of maps involved, the great flexibility provided by FOSS for spatial analysis, such as GRASS, R, QGIS and GAMA and the possibility of scripting all the operations have played a pivotal role in the success both in the creation of the dataset and in the extraction and modeling of land use changes.
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