Land reclamation is a practice that dates back thousands of years, due to population growth and social development over time. In the past, reclaimed lands were mainly used as croplands with intensive land use. In recent decades, conversion to lower-intensity use has occurred, such as on the coast of China. Here, we selected a study area that was reclaimed approximately 100 years ago on the coast of the Yellow Sea, Jiangsu, China. We identified different land uses: paddy, upland, upland-forest, forest, and vegetable garden. Land use intensity was assigned and scored by input and output indicators. Macrofaunal communities and soil properties were analyzed to detect variations among habitats. The structural and functional composition of the soil macrofaunal community varied significantly with soil properties. After the cropland was converted to forest, the biodiversity indices increased and the soil macrofaunal community became more complex, with expanding groups of detritivores and predators. However, trends observed among herbivores and omnivores did not vary significantly. The highest salinity and bulk density were found in the forest. The highest nutrient contents, such as soil organic carbon, total nitrogen, and total phosphorous, were found in the vegetable garden. Higher soil moisture content was found in the forest and vegetable garden. Soil moisture was identified as the key soil property in shaping the soil macrofaunal community. Furthermore, soil moisture and salinity were selected in the optimal regression models for explaining the measured parameters of soil macrofaunal communities, including taxonomic richness, density, Shannon index, and Margalef index. Variations in the soil macrofaunal community should be regarded as a comprehensive response to the changes in soil properties co-varying with land use conversion. Our findings indicated that land use conversion with lower land use intensity increased soil macrofaunal biodiversity at the reclaimed coast.

CONTEXT: While land use change is the main driver of biodiversity loss, most biodiversity assessments either ignore it or use a simple land cover representation. Land cover representations lack the representation of land use and landscape characteristics relevant to biodiversity modeling. OBJECTIVES: We developed a comprehensive and high-resolution representation of European land systems on a 1-km² grid integrating important land use and landscape characteristics. METHODS: Combining the recent data on land cover and land use intensities, we applied an expert-based hierarchical classification approach and identified land systems that are common in Europe and meaningful for studying biodiversity. We tested the benefits of using this map as compared to land cover information to predict the distribution of bird species having different vulnerability to landscape and land use change. RESULTS: Next to landscapes dominated by one land cover, mosaic landscapes cover 14.5% of European terrestrial surface. When using the land system map, species distribution models demonstrate substantially higher predictive ability (up to 19% higher) as compared to models based on land cover maps. Our map consistently contributes more to the spatial distribution of the tested species than the use of land cover data (3.9 to 39.1% higher). CONCLUSIONS: A land systems classification including essential aspects of landscape and land management into a consistent classification can improve upon traditional land cover maps in large-scale biodiversity assessment. The classification balances data availability at continental scale with vital information needs for various ecological studies.

Land-use conversion from natural areas to agriculture and human settlements is causing global biodiversity loss. We proposed a human land-use disturbance index (LDI) to assess habitat loss and fragmentation in global biodiversity hotspots from 1992 to 2015. Negative (LDI > 1) and positive (LDI < 1) impacts on habitat were observed in 30 and 6 biodiversity hotspots, respectively. The hotspots with a relatively small proportional area of nature-dominated land were more likely to face habitat loss. The area of nature-dominated land in the global biodiversity hotspots decreased by 287.37 × 10³ km², which was 1.56% of the level in 1992. Agricultural land occupation and urban encroachment contributed to approximately 90% and 10% of the habitat loss in global biodiversity hotspots, respectively. Habitat loss caused by agricultural land occupation was notable in the hotspots (Sundaland, Indo-Burma and Cerrado) within the developing nations, whereas urban expansion threats to biodiversity were more evident in the hotspots (North American Coastal Plain and Forests of East Australia) within the developed nations. Together with the human development index (HDI) results, we found that low land-use disturbance did not occur as expected in developed areas. The 6 biodiversity hotspots with positive effects were all located in the developing nations. The results indicated that human development did not necessarily result in negative effects on habitats; however, human development goals oriented by the HDI without environmental and ecological dimensions would not be beneficial to biodiversity conservation. These findings shed new light on the impacts of land-use change on global biodiversity hotspots and provide valuable information for global land-use monitoring and biodiversity risk assessments.

Climate change and biodiversity loss are amongst the most pressing environmental issues internationally, with far-reaching impacts that place natural and semi-natural habitats at ever greater risk of degradation. My project explores the effects of land-use on plant biodiversity in semi-natural grazing lands using datasets covering Sweden’s southernmost region, Scania. Comparative analyses were performed using biodiversity measures and a number of landscape variables. The measures of biodiversity in terms of plant species richness were calculated using a grazing land inventory subset with records from the latest years. These measures were correlated with landscape variables, relating to hydrology and vegetation phenology. Such variables include a Wetness Index and Plant Phenology Index, computed using recent remotely sensed data, namely Sentinel-2 time series data. Land cover data was processed to cluster the study sites into distinct land cover groups which facilitated further correlation analysis between plant biodiversity and the landscape variables. These variables were also analysed at two spatial scales, i.e. at the extent of both the grazing lands and their 1 km buffer zones. | Grazing land has been described as a biodiversity-rich habitat, and modifying agricultural practices which sustain both fodder production for grazing livestock and the grazing activity itself could disturb the ecosystems in such semi-natural landscapes. Livestock grazing is extensive in Scania and other Swedish regions, and significant changes to this practice could negatively impact biodiversity. The relevance of land-use within semi-natural habitats that have an agricultural purpose is important to study, given the risk that such grazing lands are lost to cropland or other land-use whenever grazing practices are abandoned. The potential for biodiversity loss in these rural settings is known to be significant with increasing land-use change. Finding out if any landscape variables can be associated with the plant biodiversity found in these sites is useful for revealing which, if any, land-use types and/or agricultural practices apart from the grazing itself can be associated to grassland biodiversity. My project explores the effects of land-use on plant biodiversity in semi-natural grazing lands using datasets covering Sweden’s southernmost region, Scania. Comparative analyses were performed using biodiversity measures and a number of landscape variables. The measures of biodiversity in terms of plant species richness were calculated using a grazing land inventory subset with records from the latest years. These measures were correlated with landscape variables, relating to hydrology and vegetation phenology. Such variables include a Wetness Index and Plant Phenology Index, computed using recent remotely sensed data, namely Sentinel-2 satellite data. Land cover data was processed to cluster the study sites into distinct land cover groups which facilitated further correlation analysis between plant biodiversity and the landscape variables. These variables were also analysed at two spatial scales, i.e. at the extent of both the grazing lands and their 1 km buffer zones. Following the use of standard biodiversity metrics and multiple landscape descriptors, the importance of present land-use for plant biodiversity patterns in Scanian grazing lands could not be established. Whilst avoiding unnecessary complexity in my attempt to relate land-use with biodiversity, the results and implications are in agreement with recent literature suggesting that rather than the present land-use, the grassland plant biodiversity is due to historical land-use to a greater extent. Historical land-use remains a valid explanation of present-day plant richness in grazing lands as most land-use effects cannot be associated with biodiversity.

The growing replacement of native vegetation by forest plantations is considered a global threat to biodiversity. Significant variation in biotic communities among stands with similar management suggests that previous land use might have an effect on the capacity of forest plantations to harbor native species. The goal of our study was to determine the effect of land-use history on the biodiversity currently present in pine plantations in the coastal range of Central Chile. In particular, we hypothesized that plantations that directly replaced native forests should have higher diversity of plants and birds than plantations that were established in agricultural areas. We also expected that plantations of higher number of rotations should have fewer habitat-specialists and more generalists/exotics, reflecting a process of biotic homogenization. Using aerial photographs and satellite images encompassing a period of six decades, we classified 108 4-ha sampling units into native forests, and mature (17-20 year) pine plantations of first, second, and third rotation, of either forest or agricultural origin. At each site, we collected data on the abundance and richness of diurnal birds and understory plants, and analyzed their behavior in relation to the land-use history using Generalized Linear Models (GLMs). Also, we evaluated dissimilarity of communities of each pine plantation "treatment" to assess the occurrence of biotic homogenization. As predicted, pine plantations that directly replaced native forests had a higher abundance of forest specialists and less abundance of exotics and generalists than plantations of agricultural origin. In contrast, the number of rotations of pine plantations not only did not affect negatively the diversity and abundance of forest specialist species, but the models showed some signs of naturalization in the studied systems over time, such as the increase in the abundance of native herbs and a reduction in the abundance of their exotic counterparts. These results agree with the lack of evidence for a decrease in the dissimilarity of biotic communities in plantations with time, suggesting that the management of pine plantations in Central Chile is not promoting biotic homogenization, beyond the impact of the initial stages of land use change. | Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 1080463 1120314 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) 21170437 Vicepresidency of Academic Affairs of the University of Chile School of Forest Science and Nature Conservation

Increasing land-use intensity is a main driver of biodiversity loss in farmland, but measuring proxies for land-use intensity across entire landscapes is challenging. Here, we develop a novel method for the assessment of the impact of land-use intensity on biodiversity in agricultural landscapes using remote sensing parameters derived from the Sentinel-2 satellites. We link crop phenology and productivity parameters derived from time-series of a two-band enhanced vegetation index with biodiversity indicators (insect pollinators and insect-pollinated vascular plants) in agricultural fields in southern Sweden, with contrasting land management (i.e. conventional and organic farming). Our results show that arable land-use intensity in cereal systems dominated by spring-sown cereals can be approximated using Sentinel-2 productivity parameters. This was shown by the significant positive correlations between the amplitude and maximum value of the enhanced vegetation index on one side and farmer reported yields on the other. We also found that conventional cereal fields had 17% higher maximum and 13% higher amplitude of their enhanced vegetation index than organic fields. Sentinel-2 derived parameters were more strongly correlated with the abundance and species richness of bumblebees and the richness of vascular plants than the abundance and species richness of butterflies. The relationships we found between biodiversity and crop production proxies are consistent with predictions that increasing agricultural land-use intensity decreases field biodiversity. The newly developed method based on crop phenology and productivity parameters derived from Sentinel-2 data serves as a proof of concept for the assessment of the impact of land-use intensity on biodiversity over cereal fields across larger areas. It enables the estimation of arable productivity in cereal systems, which can then be used by ecologists and develop tools for land managers as a proxy for land-use intensity. Coupled with spatially explicit databases on agricultural land-use, this method will enable crop-specific cereal productivity estimation across large geographical regions.

PURPOSE: The impact of land use on biodiversity is a topic that has received considerable attention in life cycle assessment (LCA). The methodology to assess biodiversity in LCA has been improved in the past decades. This paper contributes to this progress by building on the concept of conditions for maintained biodiversity. It describes the theory for the development of mathematical functions representing the impact of land uses and management practices on biodiversity. METHODS: The method proposed here describes the impact of land use on biodiversity as a decrease in biodiversity potential, capturing the impact of management practices. The method can be applied with weighting between regions, such as ecoregions. The biodiversity potential is calculated through functions that describe not only parameters which are relevant to biodiversity, for example, deadwood in a forest, but also the relationships between those parameters. For example, maximum biodiversity would hypothetically occur when the nutrient balance is ideal and no pesticide is applied. As these relationships may not be readily quantified, we propose the use of fuzzy thinking for biodiversity assessment, using AND/OR operators. The method allows the inclusion of context parameters that represent neither the management nor the land use practice being investigated, but are nevertheless relevant to biodiversity. The parameters and relationships can be defined by either literature or expert interviews. We give recommendations on how to create the biodiversity potential functions by providing the reader with a set of questions that can help build the functions and find the relationship between parameters. RESULTS AND DISCUSSION: We present a simplified case study of paper production in the Scandinavian and Russian Taiga to demonstrate the applicability of the method. We apply the method to two scenarios, one representing an intensive forestry practice, and another representing lower intensity forestry management. The results communicate the differences between the two scenarios quantitatively, but more importantly, are able to provide guidance on improved management. We discuss the advantages of this condition-based approach compared to pre-defined intensity classes. The potential drawbacks of defining potential functions from industry-derived studies are pointed out. This method also provides a less strict approach to a reference situation, consequently allowing the adequate assessment of cases in which the most beneficial biodiversity state is achieved through management practices. CONCLUSIONS: The originality of using fuzzy thinking is that it enables land use management practices to be accounted for in LCA without requiring sub-categories for different intensities to be explicitly established, thus moving beyond the classification of land use practices. The proposed method is another LCIA step toward closing the gap between land use management practices and biodiversity conservation goals.

Changes in climate and land use are major drivers of biodiversity loss. These drivers likely interact and their mutual effects alter biodiversity. These interaction mechanisms are rarely considered in biodiversity assessments, as only the combined individual effects are reported. In this study, we explored interaction effects from mechanisms that potentially affect biodiversity under climate change. These mechanisms entail that climate-change effects on, for example, species abundance and species’ range shifts depend on land-use change. Similarly, land-use change impacts are contingent on climate change. We explored interaction effects from four mechanisms and projected their consequences on biodiversity. These interactions arise if species adapted to modified landscapes (e.g. cropland) differ in their sensitivity to climate change from species adapted to natural landscapes. We verified these interaction effects by performing a systematic literature review and meta-analysis of 42 bioclimatic studies (with different increases in global mean temperature) on species distributions in landscapes with varying cropland levels. We used the Fraction of Remaining Species as the effect-size metric in this meta-analysis. The influence of global mean temperature increase on FRS did not significantly change with different cropland levels. This finding excluded interaction effects between climate and landscapes that are modified by other land uses than cropping. Although we only assessed coarse climate and land-use patterns, global mean temperature increase was a good, significant model predictor for biodiversity decline. This emphasizes the need to analyse interactions between land-use and climate-change effects on biodiversity simultaneously in other modified landscapes. Such analyses should also integrate other conditions, such as spatial location, adaptive capacity and time lags. Understanding all these interaction mechanisms and other conditions will help to better project future biodiversity trends and to develop coping strategies for biodiversity conservation.

Changes in climate and land use are major drivers of biodiversity loss. These drivers likely interact and their mutual effects alter biodiversity. These interaction mechanisms are rarely considered in biodiversity assessments, as only the combined individual effects are reported. In this study, we explored interaction effects from mechanisms that potentially affect biodiversity under climate change. These mechanisms entail that climate-change effects on, for example, species abundance and species’ range shifts depend on land-use change. Similarly, land-use change impacts are contingent on climate change. We explored interaction effects from four mechanisms and projected their consequences on biodiversity. These interactions arise if species adapted to modified landscapes (e.g. cropland) differ in their sensitivity to climate change from species adapted to natural landscapes. We verified these interaction effects by performing a systematic literature review and meta-analysis of 42 bioclimatic studies (with different increases in global mean temperature) on species distributions in landscapes with varying cropland levels. We used the Fraction of Remaining Species as the effect-size metric in this meta-analysis. The influence of global mean temperature increase on FRS did not significantly change with different cropland levels. This finding excluded interaction effects between climate and landscapes that are modified by other land uses than cropping. Although we only assessed coarse climate and land-use patterns, global mean temperature increase was a good, significant model predictor for biodiversity decline. This emphasizes the need to analyse interactions between land-use and climate-change effects on biodiversity simultaneously in other modified landscapes. Such analyses should also integrate other conditions, such as spatial location, adaptive capacity and time lags. Understanding all these interaction mechanisms and other conditions will help to better project future biodiversity trends and to develop coping strategies for biodiversity conservation.

Land-use intensification is a major driver of biodiversity loss. However, understanding how different components of land use drive biodiversity loss requires the investigation of multiple trophic levels across spatial scales. Using data from 150 agricultural grasslands in central Europe, we assess the influence of multiple components of local- and landscape-level land use on more than 4,000 above- and belowground taxa, spanning 20 trophic groups. Plot-level land-use intensity is strongly and negatively associated with aboveground trophic groups, but positively or not associated with belowground trophic groups. Meanwhile, both above- and belowground trophic groups respond to landscape-level land use, but to different drivers: aboveground diversity of grasslands is promoted by diverse surrounding land-cover, while belowground diversity is positively related to a high permanent forest cover in the surrounding landscape. These results highlight a role of landscape-level land use in shaping belowground communities, and suggest that revised agroecosystem management strategies are needed to conserve whole-ecosystem biodiversity.