Pesticides affect a diverse range of non-target species and may be linked to global biodiversity loss. The magnitude of this hazard remains only partially understood. We present a synthesis of pesticide (insecticide, herbicide and fungicide) impacts on multiple non-target organisms across trophic levels based on 20,212 effect sizes from 1,705 studies. For non-target plants, animals (invertebrate and vertebrates) and microorganisms (bacteria and fungi), we show negative responses of the growth, reproduction, behaviour and other physiological biomarkers within terrestrial and aquatic systems. Pesticides formulated for specific taxa negatively affected non-target groups, e.g. insecticidal neonicotinoids affecting amphibians. Negative effects were more pronounced in temperate than tropical regions but were consistent between aquatic and terrestrial environments, even after correcting for field-realistic terrestrial and environmentally relevant exposure scenarios. Our results question the sustainability of current pesticide use and support the need for enhanced risk assessments to reduce risks to biodiversity and ecosystems.

Comparable data is essential to understand biodiversity patterns. While assemblage or community inventorying requires comprehensive sampling, monitoring focuses on as few components as possible to detect changes. Quantifying species, their evolutionary history, and the way they interact requires studying changes in taxonomic (TD), phylogenetic (PD) and functional diversity (FD). Here we propose a method for the optimization of sampling protocols for inventorying and monitoring assemblages or communities across these three diversity dimensions taking sampling costs into account. We used Iberian spiders and Amazonian bats as two case-studies. The optimal combination of methods for inventorying and monitoring required optimizing the accumulation curve of α-diversity and minimizing the difference between sampled and estimated β-diversity (bias), respectively. For Iberian spiders, the optimal combination for TD, PD and FD allowed sampling at least 50% of estimated diversity with 24 person-hours of fieldwork. The optimal combination of six person-hours allowed reaching a bias below 8% for all dimensions. For Amazonian bats, surveying all the 12 sites with mist-nets and 0 or 1 acoustic recorders was the optimal combination for almost all diversity types, resulting in >89% of the diversity and <10% bias with roughly a third of the cost. Only for phylogenetic α-diversity, the best solution was less clear and involved surveying both with mist nets and acoustic recorders. The widespread use of optimized and standardized sampling protocols and regular repetition in time will radically improve global inventory and monitoring of biodiversity. We strongly advocate for the global adoption of sampling protocols for both inventory and monitoring of taxonomic, phylogenetic and functional diversity.

Agricultural Biodiversity dynamics has been evaluated by social metabolism or by landscape structure-function analysis. In this study, by using ELIA modeling, we used both methods in combination to understand how the interplay between social metabolism and landscape structure-function can affect biodiversity pattern distribution. We used energy reinvestment (E) as an indicator of social metabolism and landscape heterogeneity (Le) as an indicator of landscape structure-function. We propose a research hypothesis to analyze biodiversity patterns considering four different clusters identified based on high or low E or Le. As cluster 1, we defined E as high and Le as low and associated natural ecosystems to it. These ecosystems are expected to contain high species abundance but low richness. As cluster 2, both E and Le were defined as high and semi-natural ecosystems were associated to it, where nature friendly farm system developed. In these ecosystems, high species abundance and richness are expected. Cluster 3 with low E and Le was associated intensive farmland, which is due to the simplification of the landscape. Here, low energy reinvestment and landscape heterogeneity confirm that ecosystem services related to biodiversity have been drastically reduced. Lastly, cluster 4 with low E but high Le refers to intensive mosaics of farmland and pasture. In this cluster, the biodiversity richness index is high due to spatial landscape diversity, but the biodiversity abundance index is low due to the lack of energy reinvestment. We evaluate the proposed hypothesis for biodiversity analysis in the Qazvin province, emphasizing the interplay between energy availability and landscape heterogeneity in shaping ecological communities. This study highlights the importance of understanding biodiversity patterns at spatial scale and emphasizes the need for interdisciplinary research to address conservation and sustainability challenges. Our approach would be very useful where there is lack of biodiversity data.

1. Recent biodiversity declines require action across sectors such as agriculture. The situation is particularly acute for arthropods, a species-rich taxon providing important ecosystem services. To counteract the negative consequences of agricultural intensification, creating a less hostile agricultural ‘matrix’ through growing crop mixtures can reduce harm for arthropods without yield losses.
2. While grassland biodiversity experiments showed positive plant biodiversity effects on arthropods, experiments manipulating crop diversity and agrochemical input used to study arthropods are lacking.
3. Here, we experimentally manipulated crop diversity (1–3 species, fallows), crop species (wheat, faba bean, linseed and oilseed rape) and agrochemical input (high vs. low) and studied responses of arthropod biodiversity. We tested whether arthropod responses were affected by crop diversity, mixtures and management. Additionally, we measured crop biomass.
4. Crop biomass increased with crop diversity under high-input management, while under low management intensity, biomass was highest in two-species mixtures.
5. Increasing crop diversity positively affected arthropod abundance and diversity, under both low- and high-input management. Crop mixtures containing faba bean, linseed or oilseed rape had particularly high arthropod diversity.
6. Mass-flowering crops attracted more arthropods than legumes or cereals. Integrating intercropping into agricultural systems could increase flower visits by insects up to 1.5 million per hectare, thus likely also supporting pollination and pest-control ecosystem services.
7. Flower visitor network complexity increased in mixtures containing linseed and faba bean and under low-input management.
8. Intercropping can counteract insect declines in farmland by creating beneficial matrix habitat without compromising crop yield.