Integrating biodiversity conservation in wider landscape management: Necessity, implementation and evaluation

Kleijn, David; Biesmeijer, Koos; Klaassen, Raymond H. G.; Oerlemans, N.; Raemakers, Ivo; Scheper, Jeroen; Vet, L.E.M.

doi:10.1016/bs.aecr.2020.08.004

Cited by 18 publications

(5 citation statements)

References 129 publications

(146 reference statements)

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“…Policy makers might use the results of automated wildflower monitoring for spatial planning activities and biodiversity conservation [88]. In the long run, we envisage that AI-enabled monitoring solutions will be tied to more coarse-grained ecological indicators [89].…”

Section: Discussionmentioning

confidence: 99%

Data-centric AI approach for automated wildflower monitoring

Schouten,

Michielsen,

Gravendeel

2024

Preprint

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Both researchers and policy makers are in need of standards and tools that help understanding and assessing natural capital. Wildflowers are a major component of our natural capital; they play an essential role in ecosystems, improve soil health, supply food and medicines, and curb climate change. In this paper, we present the Eindhoven Wildflower Dataset (EWD) as well as a PyTorch object detection model that is able to identify and count wildflowers. EWD, collected over two entire flowering seasons and expert annotated, contains 2002 top-view images of flowering plants captured ‘in the wild’ in five different landscape types (roadsides, urban green spaces, cropland, weed-rich grassland, marshland). It holds a total of 65571annotations for 160 species belonging to 31 different families of flowering plants and serves as a reference dataset for automating wildflower monitoring. To ensure consistent annotations, we define specific floral count units (largely based on inflorescences) and provide extensive annotation guidelines. With a 0.82 mAP (@IoU > 0.50) score the presented baseline model, trained with a balanced subset of EWD, is to the best of our knowledge superior in its class. Our approach empowers automated quantification of wildflower richness and abundance and encourages the development of standards for AI-based wildflower monitoring. The annotated EWD dataset is publicly available on the DataverseNL research data repository, and the code to train and run the baseline model is supplied as supplementary material.

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Section: Discussionmentioning

confidence: 99%

Data-centric AI approach for automated wildflower monitoring

Schouten,

Michielsen,

Gravendeel

2024

Preprint

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“…Our need for food production is expected to increase greatly in the coming decades; consequently, preserving agricultural practices that potentially support high levels of farmland biodiversity might become increasingly difficult. Yet, conservation initiatives of agricultural areas connected to PA networks should be promoted (Kleijn et al, 2020). Post‐2020 policy targets call for identifying favorable landscape elements and setting aside a proportion of agricultural land to enhance biodiversity (Šálek et al, 2018; Tarjuelo et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Spatial heterogeneity and temporal stability characterize future climatic refugia in Mediterranean Europe

Doxa

Kamarianakis

Mazaris

2022

Global Change Biology

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Identifying areas where species that are most exposed to climate change will occur in forthcoming decades has become a priority for conservation planning (Carroll et al., 2017;Heller et al., 2015;Wang et al., 2018). The dynamically shifting climate is a major parameter driving the global redistribution of biodiversity, and is expected to force many species out of current reserves, jeopardizing the efficiency of the present, static network of protected areas (PAs), which were established to halt biodiversity declines (Araújo

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“…In 2018, a landscape‐level conservation initiative was started in the Geuldal area of south Limburg, the Netherlands (Figure 2) in an effort to improve existing semi‐natural habitats for wild bees (https://boshommellandschap-geuldal.nl/; for a general approach, see Kleijn et al., 2020). Approximately 60% of the area is covered by intensive agricultural land use (arable fields and permanent pastures, with almost no mass‐flowering crops except for a small proportion of fruit orchards), but up to 13% consists of natural grasslands that are protected under the EU Natura 2000 conservation network, including 2% calcareous grasslands, a habitat that harbours high levels of biodiversity.…”

Section: Methodsmentioning

confidence: 99%

“…We use these highly spatiotemporally replicated data to address three In 2018, a landscape-level conservation initiative was started in the Geuldal area of south Limburg, the Netherlands (Figure 2) in an effort to improve existing semi-natural habitats for wild bees (bosho mmell andsc hap-geuld al. nl; for a general approach, see Kleijn et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Bees go up, flowers go down: Increased resource limitation from late spring to summer in agricultural landscapes

Bishop,

Fijen,

Raemakers

et al. 2024

Journal of Applied Ecology

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The loss of floral resources is a leading cause of wild bee decline in agricultural landscapes, but little is known about the temporal aspects of floral resource limitation for both social and solitary bees. Understanding when floral resources are most needed is crucial for the optimal design of pollinator conservation measures. We surveyed bees and flowers in 160 semi‐natural habitat patches multiple times per year (May–July) for 5 years. We identified the seasonality of floral resources and wild bees and examined inter‐ and intra‐annual patterns of floral resource limitation at both local and landscape scales. Floral resource availability varied across years but generally peaked in late May, after which it declined and remained low through July. Bumblebee and solitary bee abundances increased across the season, leading to stronger floral resource limitation for both groups later in the season. Bumblebee abundance was marginally positively associated with the cumulative amount of landscape‐scale floral resources as well as the floral resources of the previous year. Solitary bee abundance was only predicted by local‐scale floral resources. Synthesis and applications: Our results indicate that agri‐environmental management should target the provision of summer floral resources for both social and solitary bees. Local‐scale enhancement of floral resources can likely benefit solitary bees, but bumblebees probably require the management of floral resources at the landscape scale. Increasing the floral resources and the flowering period of herbaceous habitats that cover large proportions of the landscape, such as pastures, has the greatest potential to improve summer floral resources for bees.

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Integrating biodiversity conservation in wider landscape management: Necessity, implementation and evaluation

Cited by 18 publications

References 129 publications

Data-centric AI approach for automated wildflower monitoring

Data-centric AI approach for automated wildflower monitoring

Spatial heterogeneity and temporal stability characterize future climatic refugia in Mediterranean Europe

Bees go up, flowers go down: Increased resource limitation from late spring to summer in agricultural landscapes

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