Fragmented Agricultural Landscape Research Articles

A Method to Determine the Optimal Period for Field-Scale Yield Prediction Using Sentinel-2 Vegetation Indices

This study proposes a method for determining the optimal period for crop yield prediction using Sentinel-2 Vegetation Index (VI) measurements. The method operates at the single-field scale to minimize the influence of external factors, such as soil type, topography, microclimate variations, and agricultural practices, which can significantly affect yield predictions. By analyzing historical VI data, the method identifies the best time window for yield prediction for specific crops and fields. It allows adjustments for different space–time intervals, crop types, cloud probability thresholds, and variable time composites. As a practical example, this method is applied to a wheat field in the Po River Valley, Italy, using NDVI data to illustrate how the approach can be implemented. Although applied in this specific context, the method is exportable and can be adapted to various agricultural settings. A key feature of the approach is its ability to classify variable-length periods, leveraging historical Sentinel-2 VI compositions to identify the optimal window for yield prediction. If applied in regions with frequent cloud cover, the method can also identify the most effective cloud probability threshold for improving prediction accuracy. This approach provides a tool for enhancing yield forecasting over fragmented agricultural landscapes.

Intrapopulation germinability may help the Mediterranean plant species Poterium spinosum L. to cope with climate changes and landscape fragmentation.

Poterium spinosum L. is a key plant species forming typical shrub communities, distributed across the Mediterranean eastern coasts. The conservation of P. spinosum is thus of the utmost importance, especially due to the ever-increasing environmental pressures like climate changes and habitat fragmentation. This study, in particular, investigated for the first time the germination variability of P. spinosum at intrapopulation level, by analysing the germination behavior of five different subpopulations growing along the coasts of Sicily. For a more exhaustive picture of the main drivers of biodiversity loss affecting the distributional area of P. spinosum, the trends of climate and land-cover changes were also studied over the periods 1931-2020 and 1958-2018, respectively. The results found significant intrapopulation variability in P. spinosum, whose germination parameters showed that fruits and seeds from distinct subpopulations respond differently to diverse temperatures. Seeds showed generally higher values of final germination percentage (FGP) compared to fruits, and at higher temperatures: the highest FGP in seeds was 70% at 20°C, whereas in fruits was 58.2% at 15°C. The environmental threats showed worrying trends across the study area: during 1931-2020, the average temperature increased by 1.5°C, whereas the average rainfall declined from 710 to 650mm. Similarly, in the period 1958-2018, the analysis of the CORINE land-cover changes showed a highly fragmented agricultural landscape, where natural areas were reduced to 2.5-5.0%. Germination variability at intrapopulation level should be considered as a fundamental adaptation strategy, which can increase the reproductive success of P. spinosum under climate and land-cover changes.

Modeling honeybee flower visitation rates in the fragmented agricultural landscapes based on Lévy-flight behavior

AbstractTypically, honeybees (Apis mellifera L.), rely on waggle dances performed by scout bees to communicate information about fruitful nectar and pollen sources across the landscape. However, when this communication is absent, inaccurate, or when resources become depleted, bees resort to alternative search strategies. Field experiments utilizing harmonic radar have revealed that honeybees follow flight patterns that demonstrate a scale-free (Lévy-flight) behavior, representing an optimal search strategy for relocating the original feeder location. If honeybees adhere to a Lévy flight pattern to discover resources, where would honeybees demonstrate the highest flower visitation rates in agricultural landscapes? We generated simulated landscapes with varying proportions of forest cover scenarios, ranging from 5 to 50% of the total landscape area, along with different levels of fragmentation per se. Subsequently, we constrained the richness of flower farm cells in each landscape. To predict honeybee visitation rates, three different methodologies based on random movement were utilized: (1) moving window, (2) random walk, and (3) Lévy flight. We found that honeybee visitation rates were influenced by the degree of forest fragmentation in each scenario. Across all visitation scenarios, the highest average number of visited flowers per cell was observed in landscapes with maximum fragmentation per se. In landscapes with lower forest cover and higher fragmentation, honeybees were more likely to visit a greater number of flowers due to the increased probability of traversing the landscape and encountering more flower cells. honeybee visitation rates in agricultural landscapes are significantly influenced by the degree of forest fragmentation. The study highlights the importance of considering landscape structure, specifically forest fragmentation, when predicting honeybee visitation rates and underscores the need for further research to better understand the intricate relationship between landscape characteristics and pollinator behavior.

Indirect and direct drivers of floristic condition in a threatened temperate woodland

Almost half of Earth's surface is threatened by agriculture, which has extensively degraded ecosystems and resulted in significant biodiversity loss. Remnant ecosystems in fragmented agricultural landscapes are threatened by past and present grazing and land-clearing. Declines in native diversity are common in these ecosystems, and their restoration is a key conservation goal globally. Understanding the drivers of change in floristic condition, reflecting continuity in floristic composition towards native plant communities, is fundamental to inform effective restoration practice. Previous investigations have demonstrated abiotic and biotic drivers of floristic condition independently. However, few consider the combined influence of these drivers on floristic condition, or the interactions between them, which may mediate indirect effects (e.g. plant-soil interactions). Despite this, ecological interactions may underpin changes in floristic condition, and provide critical insights needed to inform restoration. Here, we use structural equation modelling to disentangle the relationships between plants, soils and grass and litter biomass (leaf litter and fine woody debris) to elucidate the direct and indirect drivers of floristic condition in some of the most degraded landscapes globally: the critically endangered box-gum grassy woodlands in south-eastern Australia. We identify divergent plant-soil interactions between native versus exotic plants to key soil properties including soil nitrate and phosphorus. Specifically, native plants were negatively associated with increasing soil fertility, which favored exotic species. We also found evidence of indirect effects on floristic condition, mediated through interactions between litter biomass, soils and the basal area of overstorey trees. Our findings highlight the major role of soils in shaping floristic condition through direct and indirect pathways, and the role of multivariate interactions in mediating these pathways in a highly degraded, critically endangered ecosystem. Effective restoration must therefore consider the multivariate direct and indirect drivers of ecological condition to maximise positive outcomes in these landscapes and those similar.

Improved Wetland Mapping of a Highly Fragmented Agricultural Landscape Using Land Surface Phenological Features

Wetlands are integral components of agricultural landscapes, providing a wide range of ecological, economic, and social benefits essential for sustainable development and rural livelihoods. Globally, they are vulnerable ecological assets facing several significant threats including water extraction and regulation, land clearing and reclamation, and climate change. Classification and mapping of wetlands in agricultural landscapes is crucial for conserving these ecosystems to maintain their ecological integrity amidst ongoing land-use changes and environmental pressures. This study aims to establish a robust framework for wetland classification and mapping in intensive agricultural landscapes using time series of Sentinel-2 imagery, with a focus on the Gwydir Wetland Complex situated in the northern Murray–Darling Basin—Australia’s largest river system. Using the Google Earth Engine (GEE) platform, we extracted two groups of predictors based on six vegetation indices time series calculated from multi-temporal Sentinel-2 surface reflectance (SR) imagery: the first is statistical features summarizing the time series and the second is phenological features based on harmonic analysis of time series data (HANTS). We developed and evaluated random forest (RF) models for each level of classification with combination of different groups of predictors. Our results show that RF models involving both HANTS and statistical features perform strongly with significantly high overall accuracy and class-weighted F1 scores (p < 0.05) when comparing with models with either statistical or HANTS variables. While the models have excellent performance (F-score greater than 0.9) in distinguishing wetlands from other landcovers (croplands, terrestrial uplands, and open waters), the inter-class discriminating power among wetlands is class-specific: wetlands that are frequently inundated (including river red gum forests and wetlands dominated by common reed, water couch, and marsh club-rush) are generally better identified than the ones that are flooded less frequently, such as sedgelands and woodlands dominated by black box and coolabah. This study demonstrates that HANTS features extracted from time series Sentinel data can significantly improve the accuracy of wetland mapping in highly fragmentated agricultural landscapes. Thus, this framework enables wetland classification and mapping to be updated on a regular basis to better understand the dynamic nature of these complex ecosystems and improve long-term wetland monitoring.

Novel typing scheme reveals emergence and genetic diversity of Chlamydia pecorum at the local management scale across two koala populations

To overcome shortcomings in discriminating Chlamydia pecorum strains infecting the koala (Phascolarctos cinereus) at the local level, we developed a novel genotyping scheme for this pathogen to inform koala management at a fine-scale subpopulation level. We applied this scheme to two geographically distinct koala populations in New South Wales, Australia: the Liverpool Plains and the Southern Highlands to South-west Sydney (SHSWS). Our method provides greater resolution than traditional multi-locus sequence typing, and can be used to monitor strain emergence, movement, and divergence across a range of fragmented habitats. Within the Liverpool Plains population, suspected recent introduction of a novel strain was confirmed by an absence of genetic diversity at the earliest sampling events and limited diversity at recent sampling events. Across the partially fragmented agricultural landscape of the Liverpool Plains, diversity within a widespread sequence type suggests that this degree of fragmentation may hinder but not prevent spread. In the SHSWS population, our results suggest movement of a strain from the south, where diverse strains exist, into a previously Chlamydia-free area in the north, indicating the risk of expansion towards an adjacent Chlamydia-negative koala population in South-west Sydney. In the south of the SHSWS where koala subpopulations appear segregated, we found evidence of divergent strain evolution. Our tool can be used to infer the risks of strain introduction across fragmented habitats in population management, particularly through practices such as wildlife corridor constructions and translocations.

A mechanistic approach to weighting edge-effects in landscape connectivity assessments

ContextUnderstanding landscape functional connectivity is critical for nature conservation in fragmented landscapes. Spatially explicit graph-theoretical approaches to assessing landscape connectivity have provided a promising framework for capturing functional components driving connectivity at the landscape scale. However, existing weighting schemes used to parameterise functional connectivity in graph theory-based methods are limited with respect to their ability to capture patch-level characteristics relevant to habitat use such as edge-effects.ObjectivesWe set out to develop a new approach to weighting habitat connectivity as a function of edge-effects exerted by non-habitat patches through better delineation of edge-interior habitat transitions at the patch-level and parameterization of intra-patch movement cost at the landscape scale.MethodsWe leverage the use of raster surfaces and area-weighted exponential kernels to operationalize a mechanistic approach to computing spatially explicit edge surfaces. We integrate map algebra, graph theory and landscape resistance methods to capture connectivity for a range of species specialisms on the edge-interior spectrum. We implement our method through a set of functions in the R statistical environment.ResultThrough a real-world case study, we demonstrate that our approach, drawing on these behaviours, outperforms competing metrics when evaluating potential functional connectivity in a typically fragmented agricultural landscape. We highlight options for the optimal parameterization of graph-theoretical models.ConclusionOur method offers increased flexibility, being tuneable for interior-edge habitat transitions. This therefore represents a key opportunity that can help to re-align the fields of landscape ecology and conservation biology by reconciling patch-versus-landscape methodological stances.

Population genomic structure of a widespread, urban-dwelling mammal: The eastern grey squirrel (Sciurus carolinensis).

Urbanization is a persistent and widespread driver of global environmental change, potentially shaping evolutionary processes due to genetic drift and reduced gene flow in cities induced by habitat fragmentation and small population sizes. We tested this prediction for the eastern grey squirrel (Sciurus carolinensis), a common and conspicuous forest-dwelling rodent, by obtaining 44K SNPs using reduced representation sequencing (ddRAD) for 403 individuals sampled across the species' native range in eastern North America. We observed moderate levels of genetic diversity, low levels of inbreeding, and only a modest signal of isolation-by-distance. Clustering and migration analyses show that estimated levels of migration and genetic connectivity were higher than expected across cities and forested areas, specifically within the eastern portion of the species' range dominated by urbanization, and genetic connectivity was less than expected within the western range where the landscape is fragmented by agriculture. Landscape genetic methods revealed greater gene flow among individual squirrels in forested regions, which likely provide abundant food and shelter for squirrels. Although gene flow appears to be higher in areas with more tree cover, only slight discontinuities in gene flow suggest eastern grey squirrels have maintained connected populations across urban areas in all but the most heavily fragmented agricultural landscapes. Our results suggest urbanization shapes biological evolution in wildlife species depending strongly on the composition and habitability of the landscape matrix surrounding urban areas.

Use of Satellite-based Leaf Area Index Data for Monitoring Green Gram Crop Growth in Ikombe-Katangi Area, Machakos County, Kenya

Monitoring the conditions under which crops grow and estimating their yield is essential to the process of economic development in any nation. The traditional approaches to crop monitoring are labor- and resource-intensive, as well as limited in their ability to cover expansive geographic regions. Since remote sensing data is integrated with ground measurements, it has been used as a potentially useful tool to extract biophysical variables such as leaf area index (LAI), biomass, and phenology. Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes such as yield. The potential of satellite-based LAI for monitoring crop growth was investigated and compared to field measurements. This was the objective of his study. High resolution leaf area index was retrieved from Landsat 8 OLI imageries, and ground leaf area index measurements were taken in Ikombe-Katangi Machakos in Machakos county. An equation based on regression was developed to estimate the leaf area index using the normalized difference vegetation index that was derived from Landsat-8 OLI (NDVI). According to the findings, the derived LAI exhibited strong linear relationships with the leaf area index that was measured on the ground for green gram crops, with RMSE values being 0.09846, and R2 values of 0.9249. The overall findings shed light on the viability of using multispectral data, in estimating leaf area index in a very fragmented agricultural landscape, such as that found in the Ikombe and Katangi areas of Machakos, Kenya. Therefore, accurate crop monitoring on a large scale can be accomplished through remote sensing data in conjunction with LAI measurements taken from the ground. The implementation of the leaf area index is subject to the environmental condition which requires to be investigated as the use of remote sensing data is advocated for. The advancement in the use of satellite data for extraction LAI data is key towards transforming crop production within Kenya ad Africa at large. Scarce resources for crop growth monitoring can now be augmented with open-source satellite datasets such as Landsat 8 in LAI estimation.

Hierarchical extraction of cropland boundaries using Sentinel-2 time-series data in fragmented agricultural landscapes

Accurately extracting cropland field parcels from satellite data in fragmented agricultural landscapes presents huge challenges due to small field sizes and irregular field shapes. Here, we developed a hierarchical framework for agricultural field boundary extraction from Sentinel-2 satellites based on the concept of the degree in field fragmentation. Our framework focused on three tasks, including a core task for agricultural field extraction and two auxiliary tasks to address special situations in two different fragmented regions, namely the Plains-Basin scenes with relatively regular shapes and diverse crop types, and the Plateau-Hilly scenes, with irregular shapes and small field sizes. First, the field boundaries were delineated using a modified Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net), and post-processed using morphological filtering and the Douglas-Peucker algorithm (DPA). A rule-based decision fusion strategy assisted with a classification method was developed to address the under-segmentation problem in the Plains-Basin scenes, and an adaptive generalized buffer algorithm (AGBA) was used to address the incomplete-segmentation problem in the Plateau-Hilly scenes. We tested the proposed methods in Yangming County, Heilongjiang Province of China, using time series Sentinel-2 imageries with 10-m spatial resolution. Compared with the results from using R2U-Net or a single-temporal image alone, the fragmented field boundaries show an outstanding performance, with an overall accuracy of 86.42% and 84.15% and F1 score of 0.85 and 0.83 in Plains-Basin scenes and Plateau-Hilly scenes, respectively. Our proposed methods provide a feasible solution to finely extract field boundaries in highly fragmented and heterogeneous agricultural landscapes.

Detecting the long-term spatiotemporal crop phenology changes in a highly fragmented agricultural landscape

Trends in crop phenometrics reveal the influence of climate variability and change on crop growth and development. However, the trends are less clear in fragmented tropical smallholder landscapes, because there is high spatial and temporal variability in crop phenology. Frequent historical and high spatial resolution (≤30 m) Earth observations are needed to track changes in crop phenology in fragmented landscapes but are often unavailable. The spatial–temporal gap can be closed by integrating infrequent high spatial resolution Earth observations with low spatial/high temporal resolution observations through data fusion. We fused 30 m resolution Landsat and 250 m resolution MODIS imagery to investigate trends in crop phenology from 2000 to 2020 in a fragmented agricultural landscape of Ethiopia. We used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) that had recently been modified for application in fragmented agricultural landscapes. We used the non-parametric Mann–Kendall test for crop phenology trend analysis. Crop phenology based on Landsat–MODIS fusion was compared to MODIS-based crop phenology without fusion. We found data fusion yielded a smaller magnitude of changes in the start of season (SOS: -0.2-day-y−1) and end of season (EOS: -0.50-day-y−1) compared to MODIS SOS (-0.5-day-y−1) and EOS (1.38-day-y−1) due to MODIS-related mixing with the surrounding natural vegetation in the fragmented agricultural landscape. EOS showed a faster rate of change compared to SOS over the 21-years. The Landsat and MODIS fusion captured spatial variation in the timing and magnitude of change specific to crops and their growing environment, which has implications for adaptation strategies. Our results highlight the importance of long-term data fusion to improve the spatial dimension of crop phenology time series analysis. Integrating time series land cover maps into the data fusion processing chain could further improve long-term data fusion for crop phenology trend analysis.

Dryland agricultural expansion leads to lower content and higher variability of soil inorganic carbon in topsoil

Soil inorganic carbon (SIC) is of key importance to the global carbon cycle and mitigating climate change. However, decades of agricultural expansion in the drylands have resulted in vast and fragmented agricultural landscapes and have potentially affected SIC. Previous studies investigating the impact of agricultural expansion on SIC have primarily focused on cultivation impacts, while ignoring edge effects from agricultural land boundaries. Here, we combined cultivation impacts with edge effects and fully evaluated the impacts of agricultural expansion on SIC. We conducted spatial statistical and buffer analyses using a SIC distribution map, which was developed using 388 sample points in 0 – 20 cm topsoil over the Sangong River watershed in drylands of China. We found that decades-long agricultural expansion resulted in lower content and higher variability of SIC in topsoil. Specifically, cultivation decreased SIC content by 28.57% (1.64 g kg−1) and increased SIC variability by 95.71% (0.67 g kg−1) in agricultural lands. Edge effects altered SIC content by − 1.95–11.51% (−0.11 to 0.54 g kg−1) and increased SIC variability by 39.14% (0.34 g kg−1) at edges. In addition, we found that fewer sampling points in areas with high SIC variability (e.g., agricultural lands and edges) decreased the estimation accuracy. Our study highlights the need for SIC conservation during dryland agricultural expansion. Also, it suggests that as many sampling points as possible should be set in high variability areas, which is especially useful for accurately estimating SIC distribution in drylands.

Species Enriched Grass–Clover Pastures Show Distinct Carabid Assemblages and Enhance Endangered Species of Carabid Beetles (Coleoptera: Carabidae) Compared to Continuous Maize

There is an urgent global need for the ecological intensification of agricultural systems to reduce negative impacts on the environment while meeting the rising demand for agricultural products. Enriching grasslands with floral species is a tool to promote diversity and the associated services at higher trophic levels, and ultimately, to enhance the agricultural landscape matrix. Here, we studied an organic pastures-based dairy production system with plant species enhanced grass–clover pastures with respect to the effect on the activity density, functional traits, carabid assemblages, and species richness of carabid beetles. To understand the effect of land management on carabid beetles, we studied two types of grass–clover pastures with low and relatively high plant diversities in an integrated crop–livestock rotational grazing system (ICLS). As a comparison, organic permanent grasslands and conventionally managed maize were studied. We installed pitfall traps for three weeks in early summer, and for two weeks in autumn. In total, 11,347 carabid beetles of 66 species were caught. Grass–clover pastures did not differ in activity density, functional traits, habitat guilds, or species richness, but conventional maize did show a higher activity density in autumn and a higher proportion of eurytopic species and mobile species compared to grass–clover pastures. On grass–clover pastures, we found more endangered species, Carabus beetles, and a distinct carabid assemblage compared to maize. However, we attribute the lack of an effect of increased plant diversity of the grass–clover pastures on carabid species richness and functional traits to the intensive grazing regime, which resulted in the compositional and structural homogeneity of vegetation. Still, the presence of specialized and endangered species indicated the potential for organically managed grass–clover pastures to promote dispersal through an otherwise depleted and fragmented agricultural landscape. By increasing crop diversity in ICLS, more resources for foraging and nesting are created; therefore, organically managed grass–clover pastures add to the multi-functionality of agricultural landscapes.

Habitat use by the endangered spotted-tailed quoll in a fragmented landscape

The spotted-tailed quoll (Dasyurus maculatus) is an endangered mesopredator endemic to Australia. It is generally considered a forest-dependent species associated with large, intact forested habitats. In Australia’s mainland, quoll research has typically been conducted in contiguous forest, and consequently, the species’ presumed forest-dependency might reflect sampling bias rather than preferred habitat niche. Recent studies have revealed that quolls also persist in fragmented agricultural landscapes, raising questions about their true habitat requirements and preferences. In this study, we investigated quoll habitat use within a fragmented agricultural landscape in mainland Australia. We deployed 42 lured camera traps to determine quoll habitat preferences across four broad vegetation types (open grassland, grassy woodland, dry sclerophyll forest, and wet sclerophyll forest) based on quoll activity and occupancy. Quolls were detected in all vegetation types, and quoll activity indicated a preference for dry sclerophyll forest and grassy woodlands, although this preference varied depending on the time of year. Our results suggest that quoll habitat use in mainland Australia is more flexible than previously assumed, and we recommend further research on factors that may influence habitat preference such as prey availability and seasonal behavior. Understanding the factors that drive habitat use by quolls outside of contiguous forested landscapes will inform and improve conservation and management strategies to ensure critical habitat for the species is protected and retained in an increasingly fragmented landscape.

Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region.

Accurate and rapid crop type mapping is critical for agricultural sustainability. The growing trend of cloud-based geospatial platforms provides rapid processing tools and cloud storage for remote sensing data. In particular, a variety of remote sensing applications have made use of publicly accessible data from the Sentinel missions of the European Space Agency (ESA). However, few studies have employed these data to evaluate the effectiveness of Sentinel-1, and Sentinel-2 spectral bands and Machine Learning (ML) techniques in challenging highly heterogeneous and fragmented agricultural landscapes using the Google Earth Engine (GEE) cloud computing platform. This work aims to map, accurately and early, the crop types in a highly heterogeneous and fragmented agricultural region of the Tadla Irrigated Perimeter (TIP) as a case study using the high spatiotemporal resolution of Sentinel-1, Sentinel-2, and a Random Forest (RF) classifier implemented on GEE. More specifically, five experiments were performed to assess the optical band reflectance values, vegetation indices, and SAR backscattering coefficients on the accuracy of crop classification. Besides, two scenarios were used to assess the monthly temporal windows on classification accuracy. The findings of this study show that the fusion of Sentinel-1 and Sentinel-2 data can accurately produce the early crop mapping of the studied area with an Overall Accuracy (OA) reaching 95.02%. The scenarios prove that the monthly time series perform better in terms of classification accuracy than single monthly windows images. Red-edge and shortwave infrared bands can improve the accuracy of crop classification by 1.72% when compared to only using traditional bands (i.e., visible and near-infrared bands). The inclusion of two common vegetation indices (The Normalized Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI)) and Sentinel-1 backscattering coefficients to the crop classification enhanced the overall classification accuracy by 0.02% and 2.94%, respectively, compared to using the Sentinel-2 reflectance bands alone. The monthly windows analysis indicated that the improvement in the accuracy of crop classification is the greatest when the March images are accessible, with an OA higher than 80%.

Sexual segregation occurs in bats within fragmented remnant woodlands in an agricultural landscape.

Species‐specific responses to landscape configuration and landscape composition have been studied extensively. However, little work has been done to compare intraspecific differences in habitat preferences. Bats have potential as good bioindicator taxa in woodland habitats. Therefore, studying sex differences in responses to woodland and the wider landscape can allow us to gain insight into the relative importance of these habitats for both bats and other taxa. In this study, we aimed to test the predictions that (i) habitat type and connectivity will influence the probability of recording female bats in woodlands and (ii) sex differences in response to habitat type and connectivity will be species‐specific. Bat capture data was collected in 206 woodlands over 3 years in England. The probability of detecting females relative to males was modeled in response to a range of woodland characteristics and landscape metrics for six bat species. We recorded sex differences in responses to landscape features in three species. We found a higher probability of capturing female Myotis nattereri in woodlands that were surrounded by a higher proportion of improved grasslands, whereas female Myotis mystacinus were less likely to be recorded in woodlands surrounded by semi‐natural vegetation. Female Plecotus auritus were more likely to be recorded in isolated woodlands with less connectivity to other woodlands and where agriculture dominated the surrounding landscape. Our findings indicate that sexual segregation occurs across several UK bat species in response to landscape connectivity and composition. Sexual segregation in response to landscape characteristics in bats should therefore be an important consideration in the management of fragmented agricultural landscapes.

Traits influence reptile responses to fire in a fragmented agricultural landscape

ContextHabitat loss and fragmentation can interact with other threats, including altered fire regimes, and responses to these effects can be mediated by functional traits.ObjectivesTo determine how richness and abundance of reptile trait groups respond to habitat fragmentation, patch isolation and fire.MethodsWe surveyed reptiles in 30 sites over 3 years. Sites in remnant patches in farmland were adjacent to a conservation park with either recently burnt or long-unburnt habitat. The remnant patches were stratified by distance from the reserve. Sites were spatially paired, and we experimentally burnt one of each pair in farmland. Trait groups included size, reproduction, habitat position, diet, and activity period.ResultsNone of the trait groups benefited from experimental burns, while the burns reduced abundance of viviparous, small, and above-ground species. Species richness was lower in isolated sites than in sites close to the conservation park, while generalist trait groups appeared unaffected by patch isolation. Large-sized reptiles had higher abundance in remnants. There was not more rapid colonisation of burnt sites near recently burnt conservation park. Instead, low initial abundance may have been caused by fire in combination with drought, with high rainfall during the study allowing recovery and spill-over into adjacent remnants.ConclusionsLandscape structure appears to interact with natural fires, restoration burns and longer-term climatic trends to influence the abundance and distribution of reptiles. Traits mediate responses, enabling us to formulate a set of testable mechanistic hypotheses, which illustrates a pathway to generalisation and prediction.

Patch quality and habitat fragmentation shape the foraging patterns of a specialist folivore.

Research on use of foraging patches has focused on why herbivores visit or quit patches, yet little is known about visits to patches over time. Food quality, as reflected by higher nutritional quality and lower plant defenses, and physical patch characteristics, which offer protection from predators and weather, affect patch use and hence should influence their revisitation. Due to the potentially high costs of moving between patches, fragmented habitats are predicted to complicate foraging decisions of many animals. We aimed to determine how food quality, shelter availability and habitat fragmentation influence tree reuse by a specialist folivore, the koala, in a fragmented agricultural landscape. We GPS-tracked 23 koalas in northern New South Wales, Australia and collated number of revisits, average residence time, and average time-to-return to each tree. We measured tree characteristics including food quality (foliar nitrogen and toxic formylated phloroglucinol compounds, FPCs concentrations), tree size, and tree connectedness. We also modeled the costs of locomotion between trees. Koalas re-visited isolated trees with high leaf nitrogen disproportionately often. They spent longer time in trees with high leaf nitrogen, and in large trees used for shelter. They took longer to return to trees with low leaf nitrogen. Tree connectivity reduced travel costs between patches, being either individual or groups of trees. FPC levels had no detectable effect on patch revisitation. We conclude that food quality and shelter drive koala tree re-visits. Scattered, isolated trees with nutrient-rich leaves are valuable resource patches for koalas despite movement costs to reach them.

Adjacent crop type impacts potential pollinator communities and their pollination services in remnants of natural vegetation

AbstractAimPollination plays a crucial role in the conservation of many plant species persisting in fragmented, human‐dominated landscapes. Pollinators are known to be instrumental in maintaining genetic diversity and metapopulation dynamics for many plant species and are important for providing ecological services that are essential in agricultural landscapes where populations of native plants are highly isolated. Numerous studies have explored the value of remnant native vegetation for supporting pollination services to crop species, yet the effect of mass‐flowering crops on the pollinator communities and the pollination services they provide to native plant communities persisting in fragmented landscapes are less well understood. Here, we assess the influence of the presence and phenology of a mass‐flowering crop to pollinator community structure, abundance, and pollen load composition in remnant vegetation in complex agricultural landscapes.LocationSouth‐west Western Australia, Australia.MethodsWe recorded the composition and abundance of insect flower visitors and their pollen loads in isolated remnants of York Gum‐Jam woodlands adjacent to canola (insect‐attracting) or wheat (non‐insect‐attracting) fields over two years.ResultsAll bees were much more sensitive to adjacent crop type (neighbouring canola or wheat) than non‐bee pollinators. Honeybees were the most abundant pollinators in canola fields during peak flowering. Honeybee abundance increased in canola‐adjacent reserves post canola bloom, potentially indicating a movement into reserves as crop flowering waned. Native bees were the most diverse in remnant vegetation. Pollen loads of native bees were more mixed (increased pollen richness and evenness) when sampled next to canola fields compared to wheat fields.Main conclusionThe availability of potential insect pollinators to remnant wildflower communities in agricultural landscapes is context dependent. Whether sampled communities were adjacent to wheat or canola in a landscape significantly impacted the abundance of potential pollinators in certain landscape elements, but not others, and the composition of pollen loads carried by these insects. Results offer novel insights about the influence of landscape context on pollinator communities and the potential pollination services available for the conservation of native plant species in highly fragmented agricultural landscapes.

A Novel Efficient Method for Land Cover Classification in Fragmented Agricultural Landscapes Using Sentinel Satellite Imagery

Updated and accurate land cover maps are essential and crucial for sustainable crop production and efficient land management. However, accurate and efficient land cover mapping is still a challenge for agricultural regions with complicated landscapes. This study proposed a novel spectral-phenological based land cover classification (SPLC) method to identify the land cover for fragmented agricultural landscapes, with less requirement of ground truth data. The SPLC method integrated a pixel-based support vector machine (SVM) algorithm for cropland and various non-cropland classification, and a phenology-based automatic decision tree algorithm for identification of various crop types. It was then tested and applied in two typical case areas (i.e., Jiyuan in the upstream and Yonglian in the downstream) of Hetao Irrigation District (Hetao) in the upper Yellow River basin (YRB), northwest China. The field survey sampling data and the regional visual interpretation maps were jointly used to evaluate the accuracy of land cover classification. Results indicated that stable phenological rules can be established for crop identification even with complex planting patterns, and the SPLC method performed well in land cover mapping in case areas. Four high-accuracy land cover maps were produced for Jiyuan in 2020 and 2021, Yonglian in 2021, and Hetao in 2021. The overall accuracies (OA) can reach 0.90–0.94 based on evaluation with abundant ground truth data, and land cover maps agreed well with the visual interpretation maps in space. Overall, the case application validated the applicability and efficiency of the SPLC method in land cover mapping for regions with fragmented agricultural landscapes, and also implied the potential use in other similar regions.