Ecological Applications Research Articles

Multifunctional benefits of organic farming and hedgerow density

AbstractResearch Highlight discussing: Boinot, S., Alignier, A., Aviron, S., Bertrand, C., Cheviron, N., Comment, G., Jeavons, E., Le Lann, C., Mondy, S., Mougin, C., Précigout, P.‐A., Ricono, C., Robert, C., Saias, G., Vandenkoornhuyse, P., & Mony, C. (2024). Organic farming and semi‐natural habitats for multifunctional agriculture: A case study in hedgerow landscapes of Brittany. Journal of Applied Ecology, https://doi.org/10.1111/1365‐2664.14825. Agricultural expansion and intensification are key drivers of biodiversity loss. In search of more sustainable ways to produce food and conserve biodiversity in agricultural landscapes, the benefit of multiple local‐ and landscape‐scale measures, and their interaction, has received a lot of attention. Yet to date few studies take on a holistic view and simultaneously evaluate multiple functions and benefits provided by these measures. In their multifunctionality assessment, Boinot et al. (2024) show that adoption of organic management as local‐scale measure in winter cereals have positive effects on biodiversity, ecosystem functioning and economic performance, in spite of the significantly reduced food production. More hedgerow density resulted in some benefits, but clear interactions between organic management and hedgerow density in the wider landscape were rare and depended on the evaluated spatial scale. We argue that more taxa, functions and focus crops should be included in future studies. In addition, assessing a broader gradient in hedgerow density, and including other types of semi‐natural habitat such as road verges, small forest patches and semi‐natural grasslands, can lead to identification of more clear synergies between local‐ and landscape‐scale measures. In sum, the study of Boinot et al. (2024) provides a compelling example of assessing how local and landscape‐scale measures mediate multiple benefits.

Hybrid delineation of landforms: Case of Bystrytsia-Pidbuzka drainage basin

Landforms, together with geological substrate and geomorphic processes, are essential for delineation of geoecosystems, which are indispensable for environmental management at landscape and regional levels. Geomorphometry is a geographic information system technology, which affords quantitative land-surface analysis and landform delineation using digital elevation models (DEM). Application of geomorphometry has been fostered by the emergence of free global high-resolution DEMs. However, automated geomorphometric extraction of landforms for flat areas, like wide river valleys, may be problematic owing to insufficient accuracy of the DEM. We selected the Bystrytsia-Pidbuzka drainage basin of 500 km², which has a transitional location between low flysch External Carpathian Mountains and wavy denudation-alluvial plains of the Fore-Carpathian Upland in Lviv Oblast (Ukraine), to test a hybrid methodology of landform delineation – manual and automated geomorphometric. We considered regional landforms as orotectonic units (morphostructures), which are used to characterize ecoregions, and morpholithotopes as the smallest local landforms – mesorelief elements together with surface deposits and current geomorphic processes. FABDEM V1-2 with a resolution of 30*30 m was used as a primary geodataset of elevation data. Ecoregions and morpholithotopes of wide flat valley bottoms were delineated via manual interpretation of the DEM and ancillary data, while the morpholithotopes of the hilly and mountainous interfluves were delineated automatedly using three topographic variables: topographic position index, slope, and flow accumulation. Within the study area, we singled-out six microecoregions and four mesoecoregions: Upper Dnister Depression, Upper Dnister Upland, Marginal Beskydy, and Dnister Beskydy. There are 21 classes of morpholithotopes distinguished belonging to flat valley bottoms with alluvial and lacustrine deposits, narrow valleys and big gullies, as well as lower concave and upper convex slopes formed by colluvial and eluvial-colluvial deposits respectively, divided into four inclination categories, and two lithological groups. Each morpholithotope class is attributed with a drainage status and probable current geomorphic processes. The obtained dataset is oriented on further ecological application. Key words: morpholithotope; ecoregion; geomorphometry; FABDEM; the Carpathian Mountains; the Fore-Carpathian Upland.

Sixty years of ecology with impact

Abstract Journal of Applied Ecology celebrates its 60th birthday in 2024. In this Editorial, we explore how the journal's role has changed since its launch and investigate whether the articles we publish are achieving real‐world impact. We designed and ran an author survey for all authors who have published with us between 2017 and 2021. Authors were asked if their publication achieved real‐world impact, and if so, how they achieved it. Forty four percent of respondents achieved real‐world impact with their article, primarily citing engagement with key stakeholders as the reason for this impact. We also assessed our impact on online policy documentation, comparing this to our citations in the published scientific literature. We are the most highly cited British Ecological Society journal for policy mentions with over 2800 citations in total. We also found a weak correlation between policy citations and citations in academic literature, which highlights the fact that article with relatively few academic citations can have large real‐world impact. Synthesis and applications. Whilst these results are encouraging, there are significant challenges involved in achieving and measuring impact scale. To help address some of these, we launch here a suite of new author services to help our authors achieve real‐world impact with their work. This includes offering plain language summaries and the opportunity to present findings to British Ecological Society's stakeholder community.

Identification of a Soybean Volatile Attractive for Riptortus pedestris Using Reverse Chemical Ecology Approach.

The bean bug Riptortus pedestris is a major soybean pest and a cause of the stay-green symptoms. However, the molecular mechanisms underlying its olfaction-mediated host-seeking behavior remain unclear. In this study, we compared the antennae transcriptomes of starved and nonstarved adult R. pedestris, identifying four differentially expressed odorant receptor (OR) genes. Among these, RpedOR13 showed a strong response to the host volatile 2-phenylethanol (2-PE) in Xenopus oocyte assays, while electroantennography and behavioral tests confirmed 2-PE as an effective attractant. Next, phylogenetic analysis identified RpedOR72b as a paralog of RpedOR13, with subsequent Xenopus oocyte assays confirming its specific response to 2-PE. Additionally, RNA interference experiments highlighted the crucial role of RpedOR72b in detecting 2-PE. Taken together, these findings provide new insights into the molecular mechanisms of host-seeking behavior in R. pedestris and highlight the successful application of reverse chemical ecology in OR-based screening of bioactive compounds.

Solar Power Supply for Sensor Applications in the Field: A Guide for Environmental Scientists

The move toward sophisticated sensor networks in ecological applications requires a substantial amount of energy. Energy storage solutions based simply on batteries are often not sufficient to cover the energy needs, so a standalone power supply using solar energy harvesting is generally required. However, designing an appropriate solar power supply without oversizing and avoiding output power disruption all year long is not a trivial task. This paper provides a set of guidelines as well as useful information and advice for environmental researchers and other non-experts to select the right components when designing their own autonomous solar power supply for a range between 10 mW and 10 W. The design steps are compiled into a comprehensive document, free of irrelevant information yet still presenting a general overview of the solar power supply design process, in order to make this task more accessible and understandable for non-experts. The methodology for simple initial dimensioning was carried out and applied to a real-life use case by using the estimated or measured daily consumption combined with free meteorological data of the deployment site provided by various websites. Next, an hourly simulation completed the first sizing. A year of experimental results validated the methodology.

Machine vision approach for monitoring and quantifying fish school migration

The precise monitoring and quantification of fish migration are crucial for enhancing agricultural productivity and promoting environmental conservation. However, conducting these tasks in natural environments presents challenges due to the subtle characteristics of fish and the inherent complexities in detection. This study addresses these challenges by introducing DVE-YOLO (Dynamic Vision Enhanced YOLO), a novel framework based on the YOLOv8 architecture, complemented by a tailored sample allocation strategy and a dedicated loss function. Operating on dual-frame input, DVE-YOLO integrates deep features from consecutive images to create composite anchor boxes from adjacent frames. This design enables DVE-YOLO to capture dynamic object features, reveal correlations of detected objects across frames, and facilitate efficient tracking and detection. Furthermore, this research proposes an innovative method for identifying fish migration through fish counting, documenting both the migration area and the duration of fish presence for subsequent analysis. Evaluation on an extensive fish migration dataset demonstrates that DVE-YOLO outperforms YOLOv8 and other mainstream detection algorithms, showcasing superior detection accuracy with higher AP50 and AP50−95 metrics. In terms of counting accuracy, DVE-YOLO achieves a lower Mean Squared Error (MSE) compared to YOLOv8+BoTSORT and YOLOv8+ByteTrack, indicating improved counting performance. Additionally, DVE-YOLO exhibits enhanced precision in identifying fish migration in contrast to YOLOv8+BoTSORT and YOLOv8+ByteTrack. Ultimately, these machine learning methods holds promising prospects for ecological applications.

DEBEcoMod: A dynamic energy budget R tool to predict life-history traits of marine organisms across time and space

DEBEcoMod is an open-source R script designed to apply Dynamic Energy Budget (DEB) theory to predict life-history traits of marine organisms under various environmental and anthropogenic stressors. It presents a novel approach to overcoming the computational and scale limitations of previous DEB applications, enabling the generation of spatially explicit outputs. DEBEcoMod is intended to predict traits such as maximum size, reproductive output, and life-history traits across different temporal and spatial scales. It utilises parameters from the AddMyPet database for various species and environmental time series to simulate the past, present, and future performance of organisms. The tool also includes a module for spatio-temporal representation, producing clear and accessible maps for stakeholders. The document highlights DEBEcoMod's application in invasion biology, marine spatial planning, integrated multi-trophic aquaculture, and marine ecology, drawing on published examples of spatial applications to demonstrate its versatility and potential in ecological research and adaptive management. Furthermore, the code has been cross-validated with the official DEBtool to ensure its accuracy and reliability. DEBEcoMod is available for download on GitHub, enhancing its accessibility and utility for a wide range of ecological and conservation applications.

Uncovering the arsenal of class II bacteriocins in salivarius streptococci

Facing the antibiotic resistance crisis, bacteriocins are considered as a promising alternative to treat bacterial infections. In the human commensal Streptococcus salivarius, the production of unmodified bacteriocins (or salivaricins) is directly controlled at the transcriptional level by quorum-sensing. To discover hidden bacteriocins, we harnessed here the unique molecular signatures of salivaricins not yet used in available computational pipelines and performed genome mining followed by orthogonal reconstitution and expression. From 100 genomes of S. salivarius, we identified more than 50 bacteriocin candidates clustered into 21 groups. Strain-based analysis of bacteriocin combinations revealed significant diversity, reflecting the plasticity of seven independent loci. Activity tests showed both narrow and broad-spectrum bacteriocins with overlapping activities against a wide panel of Gram-positive bacteria, including notorious multidrug-resistant pathogens. Overall, this work provides a search-to-test generic pipeline for bacteriocin discovery with high impact for bacterial ecology and broad applications in the food and biomedical fields.

Allelopathic activity of cyanobacteria isolated from Lake Tuzkol.

Allelopathy plays a crucial role in shaping phytoplankton community dynamics and diversity. It influences competition between photoautotrophs for resources and affects species succession in aquatic ecosystems. This article evaluates the allelopathic activity of cyanobacteria isolated from Lake Tuzkol, Kazakhstan. The allelopathic effects, including growth inhibition or stimulation, were assessed by adding cell-free filtrates of the cyanobacteria Oscillatoria sp. SS-9, Nostoc sp. CZS 2205, Synechococcus sp. SS-5, or Anabaena sp. SS-8 to the tested microalgae. The most pronounced effect was observed with Nostoc sp. CZS 2205, which reduced the cell counts of Chlorella vulgaris SP BB-2 and Chlamydomonas reinhardtii CC-124 by 60% and 57%, respectively, and Scenedesmus quadricauda CC-1 and Ankistrodesmus falcatus CB-2 by 55% and 53%, respectively. Synechococcus sp. SS-5, in contrast, stimulated the growth of Chlorella vulgaris SP BB-2 and Scenedesmus quadricauda CC-1 by 11% and 8.8%, respectively. Filtrates from Oscillatoria sp. SS-9 and Anabaena sp. SS-8 had no significant impact on microalgal growth. Considering the significant allelopathic activity of Nostoc sp. CZS 2205, an agar diffusion test using biomass extracts confirmed a significant inhibitory effect, with the biomass extracts showing stronger activity than cell-free filtrates. A detailed analysis of the cell-free filtrates revealed eight compounds, including methyl heptadecanoate, methyl eicosanoate, tricosane, methyl octadeca-9,12-dienoate, methyl 13,16-octadecadienoate, and 2,2-dimethoxybutane. Additionally, nostocyclamide, a cyclic peptide, was identified in the biomass extract. The detection of nostocyclamide emphasizes its ecological importance and potential evolutionary role. These results enhance our understanding of the role of cyanobacteria in aquatic ecosystems and lay the groundwork for future research into their ecological applications.

Microfluidic colorimetric biosensor for rapid and sensitive detection of Shewanella oneidensis MR‑1

Extracellular respiratory bacteria (ERB) assume a significant role in biological and environmental remediation, so the rapid detection of ERB is essential to monitor its biological repair process. This study developed a microfluidic colorimetric biosensor for rapid and sensitive detection of ERB model strain Shewanella oneidensis MR-1. In the process of separation and enrichment of bacteria based on immune magnetic nanoparticles (MNPs), the biological signal is significantly amplified by the excellent peroxidase activity of flower-like CoOOH (FL-CoOOH). Firstly, immune MNPs, samples and immune FL-CoOOH were mixed and incubated within the microfluidic chip’s channel. Magnetic capture in the separation chamber yielded MNP-bacteria-CoOOH complex. Then, 3, 3′, 5, 5′-tetramethylbenzidine (TMB) substrate was added to make the FL-CoOOH nanozyme catalyze the formation of yellow product. Finally, the target bacteria in the microplate were quantitatively detected. Based on the excellent simulated enzyme activity and stability of FL-CoOOH, the detection can be completed within 45 min after the introduction of the microfluidic chip. The detection was 5×101∼5×106 CFU/mL. Compared with the previously reported colorimetric sensors for MR-1, the sensitivity was increased by 99.54 % to 23 CFU/mL. This study uniquely integrates optical sensors and microfluidic technology for rapid ERB detection with enhanced portability. The design of biosensor can facilitate further investigations into extracellular respiration mechanisms and rapid ecological environment restoration applications.

Benefit of weeds for crop-plant mycobiota in agroecosystems: Integrating ecological demonstration and management applicability

Agricultural intensification reduces not only biodiversity in agroecosystems but also key ecosystem functions such as soil fertility. By reintroducing biological diversity in fields, weeds may enhance soil biological fertility through their influence on crop microbiota. However, letting weeds grow in crop fields will depend on weed competitiveness, farmers’ perception and acceptance, and on crop management, which influences the occurrence and the abundance of weeds in the field. This study assessed the use of eight weed species to modify wheat plant root endospheric mycobiota, and the applicability of using these plants for mycobiota enrichment in the field. By combining controlled lab experiments and field studies, we demonstrated that weeds act either as a refuge for a high diversity of fungi or as a vector for transferring fungi to the crop, particularly symbionts. Weeds differed in their competitive effect on wheat growth and only three species significantly reduced wheat growth. Interviews with farmers’ revealed that weed species were better known and more appreciated in crop fields by organic farmers than by conventional farmers. Floristic surveys confirmed that both weed occurrence and cover were higher in organic fields than in conventional fields. A multicriteria analysis showed that Trifolium repens and V. persica had the highest potential for promoting wheat plant mycobiota. Among the weed species tested, these two are worth considering as auxiliaries to improve soil biological fertility in crop fields. Their use with the goal of selecting appropriate crop mycobiota should be relatively easier in organic farms where weeds are better accepted, whereas their use in conventional farms would require raising farmers’ awareness of the benefits of weeds for soil fertility.

Process-Informed Neural Networks: A Hybrid Modelling Approach to Improve Predictive Performance and Inference of Neural Networks in Ecology and Beyond.

Despite deep learning being state of the art for data-driven model predictions, its application in ecology is currently subject to two important constraints: (i) deep-learning methods are powerful in data-rich regimes, but in ecology data are typically sparse; and (ii) deep-learning models are black-box methods and inferring the processes they represent are non-trivial to elicit. Process-based (= mechanistic) models are not constrained by data sparsity or unclear processes and are thus important for building up our ecological knowledge and transfer to applications. In this work, we combine process-based models and neural networks into process-informed neural networks (PINNs), which incorporate the process knowledge directly into the neural network structure. In a systematic evaluation of spatial and temporal prediction tasks for C-fluxes in temperate forests, we show the ability of five different types of PINNs (i) to outperform process-based models and neural networks, especially in data-sparse regimes with high-transfer task and (ii) to inform on mis- or undetected processes.

Functional ecology of plant communities as a guide for vegetation management

Plant functional traits determine how individual plants cope with varying environmental conditions, and extensive literature supports their role in influencing ecosystem properties. The study of plant functional traits could help the prediction of species responses to disturbances and environmental change, thus providing invaluable knowledge to refine conservation strategies of species and vegetation. However, applications of functional ecology for vegetation management are still underrepresented.The articles in this Special Issue of FLORA offer cutting-edge insights on the variation of plant traits and ecological strategies in response to environmental drivers, such as anthropogenic disturbance, climate and land use change. The results demonstrate how trait-based approaches can be used for the prediction of climate and land use change impacts on vulnerable sets of species or on biogeochemical cycles, reinforcing the concept that functional plant ecology can help identify the most effective procedures for vegetation management, such as the choice of mowing intensity in grasslands or planning vegetation recovery after wildfires.Our knowledge of plant trait coordination at the community level is still undefined, especially concerning the integration of above- and belowground traits. This represents a missed opportunity for vegetation management, especially considering that a complete understanding of the dynamics of the entire ecosystem is crucial for complete understanding of ecosystem properties. Since plant functional traits can represent a straightforward tool for monitoring changes in ecosystem structure and functions, their use could support vegetation monitoring as required by different programs of nature conservation. It would be useful to embrace a perspective that integrates plant traits with systematic conservation planning, ensuring effective and sustainable conservation efforts.

Shedding light on pollination deficits: Cueing into plant spectral reflectance signatures to monitor pollination delivery across landscapes

AbstractPollination underlies plant yield, health and reproductive success in agricultural and natural systems worldwide. It is therefore concerning that declining animal pollinator populations compounded by growing demands for food are leading to rising pollination deficits, with globally significant economic and environmental implications.Despite this urgent issue, accurate and scalable tools to quantify and track pollination across useful spatiotemporal scales are lacking. Here, we propose to shed new light on pollination deficits, looking to remote sensing platforms as a transformative mapping and monitoring tool and a solution for pollinator conservation and crop management.Providing a synthesis of our current understanding of pollination‐triggered floral senescence and underlying ultrastructural and metabolic changes, we propose how spectral reflectance technologies could be applied to accurately detect pollination events in real‐time and at the landscape scale.Synthesis and applications: We highlight where research efforts can be targeted to produce scalable methods for identifying field‐relevant bioindicators of pollination. We provide guidance on how spectral imaging accompanied by machine learning and coupled with autonomous operation technologies will enable applications to detect pollination delivery across complex landscapes. Ultimately, such an ecological application will transform our quantitative understanding of pollination services and, by directly linking plant yields and health, will reveal pollination deficits at high resolution to help mitigate risks to food security and ecosystem functioning.

MATHEMATICAL METHODS OF STATISTICS IN BIOLOGICAL RESEARCH

The article examines mathematical statistical methods used in biological research to process data and detect patterns within complex biological systems. Key methods such as correlation analysis, regression analysis, analysis of variance (ANOVA), cluster analysis, and hypothesis testing are discussed. Specific examples of their application in various biological fields, including genetics, ecology, and biomedical research, are provided. These methods play a critical role in the accurate analysis of large datasets, allowing researchers to evaluate the impact of different factors on biological processes, model interactions between variables, and make reliable predictions.The proper application of statistical methods significantly enhances the accuracy of scientific conclusions and reduces the likelihood of misinterpretation of data. This is especially relevant in modern biology, where large-scale data collection, such as genomic sequencing and environmental monitoring, requires sophisticated analytical tools. The review of recent publications emphasizes the importance of statistical literacy among researchers, as incorrect use of statistical tests can lead to invalid conclusions. This highlights the need for improved training in statistical methods to ensure the reproducibility and reliability of scientific findings.Additionally, the article discusses future research prospects, particularly the integration of traditional statistical methods with modern machine learning tools. This integration offers new opportunities for the automated processing and analysis of biological data, enabling the discovery of previously unknown relationships within biological systems. Machine learning techniques, when combined with statistical methods, hold great potential for advancing the fields of genomics, proteomics, and ecological modeling, providing a more in-depth understanding of biological phenomena.

Performance enhancement, life cycle assessment, and feature analysis of wheat starch-based NaCl-binder as a sustainable alternative to OPC mortar

The primary aim of this investigation is to advance the properties of an innovative and sustainable wheat starch-based NaCl-binder (WSB) material. The scope encompasses the material's ecological and structural applications and potential for paving usage. The creation of hydrophobic and environmentally friendly WSB samples under microwave curing conditions was achieved by using a salt mortar with varying sizes of salt grains, water, and wheat starch as a binding agent. This study investigated the effects of curing time, water and starch amounts, and salt particle size on strength, modulus of rupture, and modulus of elasticity. Additionally, XRD, XRF, and SEM analyses were carried out to assess the chemical characteristics of WSB and the bonding between its particles. The mix design with the highest compressive strength, which consisted of 1 wt% starch, 8 min of microwave treatment, and 10 wt% water, attained a strength of 25.27 MPa. Furthermore, coconut oil was employed to effectively make the samples water-resistant. Following different guidelines, WSB could be categorized as structural concrete and utilized for structural and paving bricks. A vital aspect of this study is the exceptional environmental performance of WSB as a structural material, highlighting its originality and importance.

Deep learning revealed the distribution and evolution patterns for invertible promoters across bacterial lineages.

Invertible promoters (invertons) are crucial regulatory elements in bacteria, facilitating gene expression changes under stress. Despite their importance, their prevalence and the range of regulated gene functions are largely unknown. We introduced DeepInverton, a deep learning model that identifies invertons across a broad phylogenetic spectrum without using sequencing reads. By analyzing 68 733 bacterial genomes and 9382 metagenomes, we have uncovered over 200 000 nonredundant invertons and have also highlighted their abundance in pathogens. Additionally, we identified a post-Cambrian Explosion increase of invertons, paralleling species diversification. Furthermore, we revealed that invertons regulate diverse functions, including antimicrobial resistance and biofilm formation, underscoring their role in environmental adaptation. Notably, the majority of inverton identifications by DeepInverton have been confirmed by the in vitro experiments. The comprehensive inverton profiles have deepened our understanding of invertons at pan-genome and pan-metagenome scales, enabling a broad spectrum of applications in microbial ecology and synthetic biology.

Insights into phylogenetic relationships and gene rearrangements: complete mitogenomes of two sympatric species in the genus Rana (Anura, Ranidae).

Mitochondrial genomes (also known as mitogenomes) serve as valuable molecular markers and have found widespread applications in molecular biology and ecology. There is abundant sequence variation in vertebrate mitogenomes, and occasionally, they exhibit gene rearrangements. In this study, two Chinese endemic Rana species, Ranajiemuxiensis and Ranahanluica, were sequenced and analyzed to obtain their complete mitogenomes. The two species were sympatrically distributed in the Zhangjiajie National Forest Park, in Wulingyuan District, Zhangjiajie City, Hunan Province, China. The mitogenome of R.jiemuxiensis was 17,506 bp, while that of R.hanluica was 17,505 bp, each comprising 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), two ribosomal RNA genes (rRNAs), and a non-coding control region (D-loop). The gene content, nucleotide composition, and evolutionary rates of each mitogenome were analyzed and compared with those of congeners. A phylogenetic analysis based on 22 mitogenomes in Rana revealed that the two sympatric species were in two different lineages, indicating that they were genetically separated to a certain extent. Three types of gene rearrangement patterns were identified when examining the gene orders of the 22 Rana mitogenomes. Most of the species shared a second and dominant gene rearrangement pattern that originated from the first ancient pattern. A "tandem duplication - multiple deletion" hypothesis was proposed to explain the evolution of these different gene rearrangement patterns. This study provided valuable data references and enhanced our understanding of the phylogenetic implications and gene rearrangements of Rana species.

Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists.

Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, and a corresponding increase in the volume of data generated. However, data sets are often becoming so sizable that analysing them manually is increasingly burdensome and unrealistic. Fortunately, we have also seen a corresponding rise in computing power and the capability of machine learning algorithms, which offer the possibility of performing some of the analysis required for PAM automatically. Nonetheless, the field of automatic detection of acoustic events is still in its infancy in biology and ecology. In this review, we examine the trends in bioacoustic PAM applications, and their implications for the burgeoning amount of data that needs to be analysed. We explore the different methods of machine learning and other tools for scanning, analysing, and extracting acoustic events automatically from large volumes of recordings. We then provide a step-by-step practical guide for using automatic detection in bioacoustics. One of the biggest challenges for the greater use of automatic detection in bioacoustics is that there is often a gulf in expertise between the biological sciences and the field of machine learning and computer science. Therefore, this review first presents an overview of the requirements for automatic detection in bioacoustics, intended to familiarise those from a computer science background with the needs of the bioacoustics community, followed by an introduction to the key elements of machine learning and artificial intelligence that a biologist needs to understand to incorporate automatic detection into their research. We then provide a practical guide to building an automatic detection pipeline for bioacoustic data, and conclude with a discussion of possible future directions in this field.

Once Common, Long in Decline: Dynamics of Traditional Orchards in a Central European Landscape

Traditional orchards are distinctive features of cultural landscapes in Central Europe. Despite their high level of ecological importance, they are in decline, and comprehensive spatial data over broad extents, which could enable a trend analysis, are lacking. We analysed traditional orchard maps from 1952 to 1967 and a map from 2010, generated via aerial image interpretation, for the state of Hesse (ca. 21,115 km2), which has the second largest share of traditional orchards in Germany. We aimed to (1) quantify long-term orchard dynamics, (2) compare orchard characteristics in terms of topographical, ecological, and socioeconomic factors, and (3) identify key drivers of orchard loss. We found that the number and area of orchards have clearly decreased across Hesse, with varying local and regional patterns. Further, historically old orchards tended to have a larger area, higher shape complexity, and were located closer to settlements, highways, and neighbouring orchards. In contrast, newly established orchards were often found at higher elevations and on steeper slopes. Finally, the three historical orchard hotspots also experienced the most notable losses driven by different factors, namely the expansion of Artificial Surfaces, Residential Buildings, and Agricultural Land. We highlight the importance of such multitemporal spatial data for a wide range of ecological applications, and we encourage the use of novel technologies to support geospatial analyses in the future.