Differentiating geological fertility derived vegetation zones in Kruger National Park,South Africa,using Landsat and MODIS imagery

Spatial technologies present possibilities for producing frequently updated and accurate habitat maps, which are important in biodiversity conservation. Assemblages of vegetation are equivalent to habitats. This study examined the use of satellite imagery in vegetation differentiation in South Africa's Kruger National Park (KNP). A vegetation classification scheme based on dominant tree species but also related to the park's geology was tested, the geology generally consisting of high and low fertility lithology. Currently available multispectral satellite imagery is broadly either of high spatial but low temporal resolution or low spatial but high temporal resolution. Landsat TM/ETM+ and MODIS images were used to represent these broad categories. Rain season dates were selected as the period when discrimination between key habitats in KNP is most likely to be successful. Principal Component Analysis enhanced vegetated areas on the Landsat images, while NDVI vegetation enhancement was employed on the MODIS image. The images were classified into six field sampling derived classes depicting a vegetation density and phenology gradient, with high (about 89%) indicative classification accuracy. The results indicate that, using image processing procedures that enhance vegetation density, image classification can be used to map the park's vegetation at the high versus low geological fertility zone level, to accuracies above 80% on high spatial resolution imagery and slightly lower accuracy on lower spatial resolution imagery. Rainfall just prior to the image date influences herbaceous vegetation and, therefore, success at image scene vegetation mapping, while cloud cover limits image availability. Small scale habitat differentiation using multispectral satellite imagery for large protected savanna areas appears feasible, indicating the potential for use of remote sensing in savanna habitat monitoring. However, factors affecting successful habitat mapping need to be considered. Therefore, adoption of remote sensing in vegetation mapping and monitoring for large protected savanna areas merits consideration by conservation agencies.     

High‐resolution riparian vegetation mapping to prioritize conservation and restoration in an impaired desert river

In highly impaired watersheds, it is critical to identify both areas with desirable habitat as conservation zones and impaired areas with the highest likelihood of improvement as restoration zones. We present how detailed riparian vegetation mapping can be used to prioritize conservation and restoration sites within a riparian and instream habitat restoration program targeting 3 native fish species on the San Rafael River, a desert river in southeastern Utah, United States. We classified vegetation using a combination of object‐based image analysis (OBIA) on high‐resolution (0.5 m), multispectral, satellite imagery with oblique aerial photography and field‐based data collection. The OBIA approach is objective, repeatable, and applicable to large areas. The overall accuracy of the classification was 80% (Cohen's κ = 0.77). We used this high‐resolution vegetation classification alongside existing data on habitat condition and aquatic species' distributions to identify reaches' conservation value and restoration potential to guide management actions. Specifically, cottonwood (Populus fremontii) and tamarisk (Tamarix ramosissima) density layers helped to establish broad restoration and conservation reach classes. The high‐resolution vegetation mapping precisely identified individual cottonwood trees and tamarisk thickets, which were used to determine specific locations for restoration activities such as beaver dam analogue structures in cottonwood restoration areas, or strategic tamarisk removal in high‐density tamarisk sites. The site prioritization method presented here is effective for planning large‐scale river restoration and is transferable to other desert river systems elsewhere in the world.     

Assessing the impacts of agricultural intensification on biodiversity: a British perspective

Agricultural intensification is best considered as the level of human appropriation of terrestrial net primary production. The global value is set to increase from 30%, increasing pressures on biodiversity. The pressures can be classified in terms of spatial scale, i.e. land cover, landscape management and crop management. Different lowland agricultural landscapes in Great Britain show differences among these pressures when habitat diversity and nutrient surplus are used as indicators. Eutrophication of plants was correlated to N surplus, and species richness of plants correlated with broad habitat diversity. Bird species diversity only correlated with habitat diversity when the diversity of different agricultural habitats was taken into account. The pressures of agricultural change may be reduced by minimizing loss of large habitats, minimizing permanent loss of agricultural land, maintaining habitat diversity in agricultural landscapes in order to provide ecosystem services, and minimizing pollution from nutrients and pesticides from the crops themselves. While these pressures could potentially be quantified using an internationally consistent set of indicators, their impacts would need to be assessed using a much larger number of locally applicable biodiversity indicators.     

Bag of tricks for long-tail visual recognition of animal species in camera-trap images

Camera traps are a method for monitoring wildlife and they collect a large number of pictures. The number of images collected of each species usually follows a long-tail distribution, i.e., a few classes have a large number of instances, while a lot of species have just a small percentage. Although in most cases these rare species are the ones of interest to ecologists, they are often neglected when using deep-learning models because these models require a large number of images for the training. In this work, a simple and effective framework called Square-Root Sampling Branch (SSB) is proposed, which combines two classification branches that are trained using square-root sampling and instance sampling to improve long-tail visual recognition, and this is compared to state-of-the-art methods for handling this task: square-root sampling, class-balanced focal loss, and balanced group softmax. To achieve a more general conclusion, the methods for handling long-tail visual recognition were systematically evaluated in four families of computer vision models (ResNet, MobileNetV3, EfficientNetV2, and Swin Transformer) and four camera-trap datasets with different characteristics. Initially, a robust baseline with the most recent training tricks was prepared and, then, the methods for improving long-tail recognition were applied. Our experiments show that square-root sampling was the method that most improved the performance for minority classes by around 15%; however, this was at the cost of reducing the majority classes' accuracy by at least 3%. Our proposed framework (SSB) demonstrated itself to be competitive with the other methods and achieved the best or the second-best results for most of the cases for the tail classes; but, unlike the square-root sampling, the loss in the performance of the head classes was minimal, thus achieving the best trade-off among all the evaluated methods. Our experiments also show that Swin Transformer can achieve high performance for rare classes without applying any additional method for handling imbalance, and attains an overall accuracy of 88.76% for the WCS dataset and 94.97% for Snapshot Serengeti using a location-based training/test partition. Despite the improvement in the tail classes' performance, our experiments highlight the need for better methods for handling long-tail visual recognition in camera-trap images, since state-of-the-art approaches achieve poor performance, especially in classes with just a few training instances.     

Intercropping in high input agriculture supports arthropod diversity without risking significant yield losses

Arthropod diversity of different taxonomic groups and ecosystem services are declining, yet current measures to counteract losses are often restricted to small areas of land or field margins, particularly in agricultural systems. At the same time, large areas of land will be required to feed a growing global population. Intercropping has been proposed as a potential solution to maximize both biodiversity and yield at large scale, but experimental evidence is scarce.In a three-year field experiment, we manipulated crop diversity and management intensity in a cereal-legume intercropping experiment in Germany, where 50% of wheat was replaced by faba beans. We measured arthropod abundance and diversity of different functional groups (pollinators, natural enemies, herbivores) and crop yield.We found that increasing crop diversity increased abundance and diversity of arthropods. Notably, pollinator and natural enemy abundances increased in intercropped systems. Low management intensity generally had positive effects on arthropod abundance and especially on pollinator diversity, indicating benefits of reduced inputs of fertilizers and herbicides. While wheat yield was higher in monocultures and for high management intensity, total grain yield of the intercrop (indicated by land equivalent ratio) was higher in mixtures. We found that trade-offs were stronger between arthropod diversity and wheat yield than between arthropod abundance and wheat yield. Specialist wheat herbivores and generalist herbivores were more abundant at higher wheat yields. Conversely, pollinator and natural enemy diversity were negatively associated with wheat yield.Our results show that diversification can promote both higher yields and greater diversity of arthropods. Intercropping can thus be an opportunity to support biodiversity without risking significant yield losses.     

The effect of land type diversity and spatial heterogeneity on farmland birds in Norway

The decline in farmland birds observed throughout Europe during recent decades has attracted much attention. Agricultural intensification or land abandonment are commonly forwarded as key drivers. Several countries have established agri-environmental schemes (AES) to counter these negative trends among farmland birds. This paper reports a study of the relationship between land use and bird species in the agricultural landscape of Norway. The main objective was to investigate the effect of spatial heterogeneity and diversity of land use on total richness and abundance of farmland birds at a national level.Monitoring the distribution and abundance of birds is part of the Norwegian monitoring programme for agricultural landscapes. The monitoring programme is based on mapping of 1 × 1 km squares distributed across the entire agricultural landscape. Within these squares permanent observation points are established for bird monitoring. Detailed interpretation of aerial photographs provides the land classification. We tested the relationship between landscape metrics at different levels of land type detail and species richness and abundance of farmland and non-farmland birds.There was a positive relationship between species richness and abundance of farmland birds and agricultural area. For non-farmland birds the relationship was negative. Spatial heterogeneity of land use was a significant positive factor for both farmland and non-farmland species. High land type diversity was positive for farmland bird richness, but negative for abundance. Non-farmland bird richness was not affected by land type diversity, but abundance had a negative response.The results presented in this paper highlight the importance of a spatial heterogeneous landscape. However, we also found that land type diversity could negatively affect the abundance of both farmland and non-farmland birds. Our findings suggest a need for different management approaches depending on whether the aim is increased species richness or abundance. Achieving both aims with the same means might be difficult. We thus suggest a need for land use analyses before proper management strategies can be implemented.     

Census Parcels Cropping System Classification from Multitemporal Remote Imagery: A Proposed Universal Methodology

A procedure named CROPCLASS was developed to semi-automate census parcel crop assessment in any agricultural area using multitemporal remote images. For each area, CROPCLASS consists of a) a definition of census parcels through vector files in all of the images; b) the extraction of spectral bands (SB) and key vegetation index (VI) average values for each parcel and image; c) the conformation of a matrix data (MD) of the extracted information; d) the classification of MD decision trees (DT) and Structured Query Language (SQL) crop predictive model definition also based on preliminary land-use ground-truth work in a reduced number of parcels; and e) the implementation of predictive models to classify unidentified parcels land uses. The software named CROPCLASS-2.0 was developed to semi-automatically perform the described procedure in an economically feasible manner. The CROPCLASS methodology was validated using seven GeoEye-1 satellite images that were taken over the LaVentilla area (Southern Spain) from April to October 2010 at 3- to 4-week intervals. The studied region was visited every 3 weeks, identifying 12 crops and others land uses in 311 parcels. The DT training models for each cropping system were assessed at a 95% to 100% overall accuracy (OA) for each crop within its corresponding cropping systems. The DT training models that were used to directly identify the individual crops were assessed with 80.7% OA, with a user accuracy of approximately 80% or higher for most crops. Generally, the DT model accuracy was similar using the seven images that were taken at approximately one-month intervals or a set of three images that were taken during early spring, summer and autumn, or set of two images that were taken at about 2 to 3 months interval. The classification of the unidentified parcels for the individual crops was achieved with an OA of 79.5%.     

Butterfly diversity at the ecotone between agricultural and semi‐natural habitats across a climatic gradient

Aim Understanding the response of species to ecotones and habitat edges is essential to designing conservation management, especially in mosaic agricultural landscapes. This study examines how species diversity and composition change with distance from semi‐natural habitats, over ecotones into agricultural fields, and how within‐site patterns of community transition change across a climatic gradient and differ between crop types. Location A total of 19 sites in Israel where semi‐natural habitats border agricultural fields (wheat fields or olive groves) distributed along a sharp climatic gradient ranging between 100 and 800 mm mean annual rainfall. Methods  We performed butterfly surveys in 2006. We analysed species richness (α‐diversity), diversity, community nestedness and species turnover (β‐diversity) within sites and between sites (γ‐diversity). We also assessed where species of conservation concern occurred. Results In wheat sites, richness and diversity declined abruptly from ecotones to fields and remained homogenously poor throughout the fields, regardless of climate. In olive sites, despite the sharp structural boundary, richness and diversity remained high from the semi‐natural habitat to the grove margins and then declined gradually into groves. Species of conservation concern occurred across all habitats at olive sites, but none were found inside wheat fields or at their ecotones. The contrast in community structure between semi‐natural habitats and fields was affected by both climate and field type. Irrigation in arid regions did not augment species diversity. Main conclusions Our results indicate that consideration of crop type, within a climatic context, should receive high priority in biodiversity conservation in agricultural areas. In ‘hostile’ crops, such as wheat, we suggest favouring a combination of high‐intensity management and wide margins over less intensive management without margins, which may merely aid generalist butterfly species. The scarcity of butterflies in arid irrigated fields suggests a need to carefully assess the effects of irrigation and agrochemicals on species’ communities.     

Effects of polyculture and monoculture farming in oil palm smallholdings on tropical fruit‐feeding butterfly diversity

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Indicators for the on-farm assessment of crop cultivar and livestock breed diversity: a survey-based participatory approach

Agrobiodiversity plays a fundamental role in guaranteeing food security. However, still little is known about the diversity within crop and livestock species: the genetic diversity. In this paper we present a set of indicators of crop accession and breed diversity for different farm types at farm-level, which may potentially supply a useful tool to assess and monitor farming system agrobiodiversity in a feasible and relatively affordable way. A generic questionnaire was developed to capture the information on crops and livestock in 12 European case study regions and in Uganda by 203 on-farm interviews. Through a participatory approach, which involved a number of stakeholders, eight potential indicators were selected and tested. Five of them are recommended as potentially useful indicators for agrobiodiversity monitoring per farm: (1) crop-species richness (up to 16 crop species), (2) crop-cultivar diversity (up to 15 crop cultivars, 1–2 on average), (3) type of crop accessions (landraces accounted for 3 % of all crop cultivars in Europe, 31 % in Uganda), (4) livestock-species diversity (up to 5 livestock species), and (5) breed diversity (up to five cattle and eight sheep breeds, on average 1–2).We demonstrated that the selected indicators are able to detect differences between farms, regions and dominant farm types. Given the present rate of agrobiodiversity loss and the dramatic effects that this may have on food production and food security, extensive monitoring is urgent. A consistent survey of crop cultivars and livestock breeds on-farm will detect losses and help to improve strategies for the management and conservation of on-farm genetic resources.     

Classification of vegetational diversity in managed boreal forests in eastern Finland

Diversity of vegetation in managed forests is studied. A classification based on forest stand structure, the abundances of vegetation species and variations in these abundances is developed and diversity indices are calculated for the classes to describe the diversity of the vegetation within the classes. The classes were formed using detrended correspondence analysis (DCA), global nonmetric multidimensional scaling (GNMDS) and TWINSPAN classification. Discriminant analysis was used to determine the environmental variables differentiating between the classes, and Duncan's multiple range test was used to examine the ability of the diversity measures to distinguish the classes. Beta diversity was estimated with Økland's method based on DCA ordination of the sample plots. The results point to fertility and the successional stage of the stand as the main factors affecting species diversity, in addition to which soil type, the number of tree species, crown cover, basal area and certain variables describing the management of the stand were relevant to the classification. The most distinct diversity indices were the reciprocal of Simpson, Pielou's J' and species richness, while the highest alpha diversity was found in young stands with a low crown cover on herb-rich or mesic forest sites. Beta diversity was quite high, its highest values of all for the whole data being recorded along the fertility gradient.     

Fine hyperspectral classification of rice varieties based on self-attention mechanism

The accurate identification of rice varieties using rapid and nondestructive hyperspectral technology is of practical significance for rice cultivation and agricultural production. This paper proposes a convolutional neural network classification model based on a self-attention mechanism (self-attention-1D-CNN) to improve accuracy in distinguishing between crop species in fields using canopy spectral information. After experimental materials were planted in the research area, portable equipment was used to collect the canopy hyperspectral data for rice during the booting stage. Five preprocessing methods and three extraction methods were used to process the data. A comparison of the classification accuracy of different classification models showed that the self-attention-1D-CNN proposed in this study achieved the best classification with an accuracy of 99.93%. The research demonstrated the feasibility of using hyperspectral technology for the fine classification of rice varieties, and the feasibility of using the CNN model as a potential classification method for near-ground crop monitoring and classification.     

Effects of Agricultural Intensification on the Assemblage of Leaf-Nosed Bats (Phyllostomidae) in a Coffee Landscape in Chiapas,Mexico

The agricultural matrix surrounding forested areas serves critical functions as dispersal corridors and alternate habitat for wildlife. Agricultural intensification, however, can reduce the conservation value of these areas. To evaluate the effects of agroecosystem management on bat assemblages, we studied the abundance and diversity of leaf-nosed bats (family: Phyllostomidae) in southwestern Chiapas, Mexico, a landscape dominated by shade coffee agroforestry. During 2104 mist-net hour (MNH), we captured 3167 bats of 27 phyllostomid species. Total species richness in each land-use type varied from 24 species in forest fragments to 22 species in commercial shade polycultures. Although the cumulative observed species richness showed little change in response to management intensity, the number of bats captured per MNH declined significantly in the more intensively managed (i.e., low-shade monocultures) plantations. Intensively managed coffee plantations had lower phyllostomid diversity and species similarity, and had lower proportions of nectarivorous and animalivorous bats. Among frugivores, the proportion of large (>25 g) frugivores captured increased with management intensity. Recapture frequency was significantly higher than expected in forest fragments, and lower than expected in more intensively managed coffee. Our results suggest that less intensively managed coffee agroforests can serve as valuable feeding and commuting areas for most leaf-nosed bats, and that maintaining forest fragments in agricultural landscapes contributes to bat diversity. Declines in populations of gleaning insectivores, however, could compromise natural suppression of insect pests in these agricultural areas.     

Diversity and composition of tropical butterflies along an Afromontane agricultural gradient in the Jimma Highlands,Ethiopia

Afromontane landscapes are typically characterized by a mosaic of smallholder farms and the biodiversity impacts of these practices will vary in accordance to local management and landscape context. Here, we assess how tropical butterfly diversity is maintained across an agricultural landscape in the Jimma Highlands of Ethiopia. We used transect surveys to sample understory butterfly communities within degraded natural forest, semi‐managed coffee forest (SMCF), exotic timber plantations, open woodland, croplands and pasture. Surveys were conducted in 29 one‐hectare plots and repeated five times between January and June 2013. We found that natural forest supports higher butterfly diversity than all agricultural plots (measured with Hill's numbers). SMCF and timber plantations retain relatively high abundance and diversity, but these metrics drop off sharply in open woodland, cropland and pasture. SMCF and timber plantations share the majority of their species with natural forest and support an equivalent abundance of forest‐dependent species, with no increase in widespread species. There was some incongruence in the responses of families and sub‐families, notably that Lycaenidae are strongly associated with open woodland and pasture. Adult butterflies clearly utilize forested agricultural practices such as SMCF and timber plantations, but species diversity declines steeply with distance from natural forest suggesting that earlier life‐stages may depend on host plants and/or microclimatic conditions that are lost under agricultural management. From a management perspective, the protection of natural forest remains a priority for tropical butterfly conservation, but understanding functioning of the wider landscape mosaic is important as SMCF and timber plantations may act as habitat corridors that facilitate movement between forest fragments.     

Landscape-scale crop diversity interacts with local management to determine ground beetle diversity

Crop diversification and maintenance of semi-natural habitats (grasslands and field boundaries) are suggested to enhance farmland biodiversity, but the relative importance of these factors remains poorly known. We evaluated how crop diversity and availability of semi-natural grasslands at a landscape-scale interacted with local farming management (three management types from low to high intensity: ley < winter wheat < sugar beet) in their effect on ground beetle assemblages in southern Sweden. Ground beetle diversity increased with crop diversity either independently of local management (Simpson species diversity), or only in the less intensively managed habitats (rarefied species richness). While ground beetle diversity in leys tended to increase with field boundary length, no such relationship was observed in winter wheat or sugar beet fields. In contrast, the landscape proportions of leys and semi-natural grasslands did not affect ground beetle species richness and diversity. We conclude that (a) semi-natural grasslands and leys may not function as source habitats at a landscape-scale if they comprise a low proportion of the total land-use, while (b) increasing crop diversity is correlated to ground beetle richness and diversity in agricultural landscapes dominated by arable land. The beneficial effect of landscape-scale crop diversification on farmland biodiversity may depend on the general level of agricultural intensity of a region.     

Multivariate analyses of Burkholderia species in soil: effect of crop and land use history

The assessment of Burkholderia diversity in agricultural areas is important considering the potential use of this genus for agronomic and environmental applications. Therefore, the aim of this work was to ascertain how plant species and land use management drive the diversity of the genus Burkholderia. In a greenhouse experiment, different crops, i.e., maize, oat, barley, and grass, were planted in pots containing soils with different land use histories, i.e., maize monoculture, crop rotation, and permanent grassland, for three consecutive growth cycles. The diversity of Burkholderia spp. in the rhizosphere soil was assessed by genus-specific PCR-denaturing gradient gel electrophoresis (DGGE) and analyzed by canonical correspondence analysis (CCA). CCA ordination plots showed that previous land use was the main factor affecting the composition of the Burkholderia community. Although most variation in the Burkholderia community structure was observed between the permanent grassland and agricultural areas, differences between the crop rotation and maize monoculture groups were also observed. Plant species affected Burkholderia community structure to a lesser extent than did land use history. Similarities were observed between Burkholderia populations associated with maize and grass, on the one hand, and between those associated with barley and oat, on the other hand. Additionally, CCA ordination plots demonstrated that these two groups (maize/grass versus barley/oat) had a negative correlation. The identification of bands from the DGGE patterns demonstrated that the species correlated with the environmental variables were mainly affiliated with Burkholderia species that are commonly isolated from soil, in particular Burkholderia glathei, B. caledonica, B. hospita, and B. caribiensis.     

Influence of crop type,heterogeneity and woody structure on avian biodiversity in agricultural landscapes

Agriculture is a primary factor underlying world-wide declines in biodiversity. However, different agricultural systems vary in their effects depending on their resemblance to the natural ecosystem, coverage across the landscape, and operational intensity. We combined data from the North American Breeding Bird Survey with remotely sensed measures of crop type and linear woody feature (LWF) density to study how agricultural type, woody structure and crop heterogeneity influenced the avian community at landscape scales across a broad agricultural region of eastern Canada. Specifically, we examined whether 1) avian diversity and abundance differed between arable crop agriculture (e.g., corn, soy) and forage (e.g., hay) and pastoral agriculture, 2) whether increasing the density of LWF enhances avian diversity and abundance, and 3) whether increasing the heterogeneity of arable crop types can reduce negative effects of arable crop amount. Avian diversity was lower in landscapes dominated by arable crop compared to forage agriculture likely due to a stronger negative correlation between arable cropping and the amount of natural land cover. In contrast, total avian abundance did not decline with either agricultural type, suggesting that species tolerant to agriculture are compensating numerically for the loss of non-tolerant species. This indicates that bird diversity may be a more sensitive response than bird abundance to crop cover type in agricultural landscapes. Higher LWF densities had positive effects on the diversity of forest and shrub bird communities as predicted. Higher crop heterogeneity did not reduce the negative effects of high crop amount as expected except for wetland bird abundance. In contrast, greater crop heterogeneity actually strengthened the negative effects of high crop amount on forest bird abundance, shrub-forest edge bird diversity and total bird diversity. We speculate that this was due to negative correlations between crop heterogeneity and the amount of shrub and forest habitat patches in crop-dominated landscapes in our study region. The variable response to crop heterogeneity across guilds suggests that policies aimed at crop diversification may not enhance avian diversity on their own and that management efforts aimed at the retention of natural forest and shrub patches, riparian corridors, and hedge-rows would be more directly beneficial.     

Monitoring an invasive perennial at the landscape scale with remote sensing

Summary   Worldwide, invasive weeds threaten agricultural, natural and urban ecosystems. In Australia's agricultural and grazing regions, invasive species often establish across extensive areas where weed management is hampered by an inability to detect the location and timing of an outbreak. In these vast landscapes, an effective detection and monitoring system is required to delineate the extent of the invasion and identify spatial and temporal factors associated with weed establishment and thickening. In this study, we utilize a time series of remote sensing imagery to detect the spatial and temporal patterns of Prickly Acacia ( Acacia nilotica ) invasion in the Mitchell grass plains of North Queensland. We develop a spectral index from Landsat images which is applied to images from 1989 to 2004, in combination with a classification mask, to identify locations and monitor changes in Prickly Acacia density across 29 000 km² of Mitchell grass plains. The approach identified spectral and temporal signatures consistent with Prickly Acacia infestation on 1.9% of this landscape. Field checking of results confirmed presence of the weed in previously unrecorded locations. The approach may be used to evaluate future spread, or outcomes of management strategies for Prickly Acacia in this landscape and could be employed to detect and monitor invasions in other extensive landscapes.     

Structures determining bryophyte species richness in a managed forest landscape in boreo-nemoral Europe

Forest patches with high biological value are protected as woodland key habitats (WKH), which are identified by the presence of forest structures and indicator species. However, management for conservation needs to consider also managed forests as habitats for species. In this respect, there is a need to set quantitative targets for species and structures at different landscape scales. Due to non-intensive methods of forest management used prior to 1940 in Latvia, it might be expected that large areas of forest have developed structures that can support many species characteristic of natural forests. The aim of the study was to create a model that best described the richness of bryophyte species that are characteristic of natural forests, using forest structures as explanatory factors. The structures and bryophyte communities on living trees and coarse woody debris (CWD) were described in plots along transects blindly placed in areas dominated by State forests under commercial management. Explanatory variables related to tree species composition and tree size explained 54% of the variation in WKH indicator species richness on living trees. The best explanatory factors were maximum diameter of deciduous tree species and CWD. Low richness of total bryophyte and indicator species was found on dead wood, and the amount of variation in bryophyte species richness on CWD explained by explanatory variables was low. The study indicates the importance of deciduous tree substrate in managed forests in maintaining the spatial continuity of epiphytic species diversity. However, the forests in the managed forest landscape did not support high diversity of epixylic species, even in the WKHs, due to low diversity of suitable dead wood substrate.     

A comparison of methods for estimating common vole (Microtus arvalis) abundance in agricultural habitats

Rodent outbreaks cause significant crop damages in agricultural areas worldwide, but routinely monitoring large areas at low cost remains a challenge. The common vole Microtus arvalis has recently colonized the agricultural plains of the northern Iberian Plateau, an area where it has started to produce population outbreaks with important impacts in agriculture, the environment and human health. Vole monitoring has become of prime importance to implement preventive management measures to control populations. In order to find a simple and reliable vole monitoring method to be applied in large areas, we compared abundance estimates derived from three methods: capture–mark–recapture (CMR), single capture events (SCE) and presence/absence of vole activity signs (VAS) during three seasons and on the main agricultural habitats in the study area. We show that an activity index based on the presence of fresh droppings and/or clippings had a similar performance to SCE in a large sample of plots (n = 222) across habitats and seasons. Data obtained with both methods (SCE, VAS) were also well correlated with those obtained with CMR, despite a limited sample size (n = 23 CMR plots). We suggest that the VAS method, which is a cheaper and easier alternative to trapping methods, provides a promising tool for scientists and managers to implement large scale monitoring of common vole in agricultural areas.