Germany lacks a standardised and coordinated farmland biodiversity monitoring framework. However, several independent and heterogeneous programmes generate datasets for farmland-related biodiversity assessments. These programmes have limitations, including limited taxonomic coverage, lack of long-term trends, and insufficient data on agricultural land use/management, affecting the early detection of declining ecosystem conditions. MonViA, launched in 2019, is a nationwide, representative, and science-based framework that addresses key gaps in farmland biodiversity monitoring, aiming to develop monitoring concepts and indicators for agri-environmental policy for Germany. MonViA complements existing monitoring programmes with its farmland focus, national-level indicators on habitat, species, and genetic diversity, and modular design. The chapter outlines key steps towards an integrated farmland biodiversity monitoring in Germany through improved survey designs, novel survey methods, and advancements in data management, access, and integration. Strengthening farmland biodiversity monitoring and integrating it into policy and practice is urgently needed for supporting effective conservation and land-management decisions.
Land degradation has severely impacted economies and the environment, pushing global efforts to prevent, reduce and reverse the phenomenon. The Land Degradation Neutrality (LDN) initiative adopted in 2015 under the United Nations Convention to Combat Desertification (UNCCD) framework, has been key to balancing land degradation with sustainable land management and restoration. Significant literature exists on enhancing the success of LDN initiatives, however, limited work has been done on the role of land governance towards achieving LDN outcomes, particularly in river catchment areas, such as the Zambezi basin. The study examines the link between land governance and LDN to establish relations for a sustainable river basin. It employs a systematic literature review, covering the basin’s eight riparian states, examining land degradation through a governance lens. The results show complex challenges relating to policy overlaps, ineffective enforcement of the law, financial and human resources constraints, and land tenure insecurity. Emerging issues relate to the need for policies that provide alternative means of livelihoods so as to relieve pressure on the natural resource base. The paper makes a case for effective land governance through harmonising policies, enhancing land tenure security, ensuring multi stakeholder participation in land use planning and land restoration in support of LDN in the Zambezi basin.
Monitoring biodiversity in agricultural landscapes is essential for addressing global biodiversity decline. Emerging remote sensing and artificial intelligence technologies enable large-scale, low-impact and cost-effective assessments. This chapter provides a concise review of remote sensing applications for farmland biodiversity monitoring, structured around the Essential Biodiversity Variable framework. We examine plants, animals, fungi and microorganisms, and their habitats in productive and semi-natural areas. Spaceborne and airborne sensors effectively assess agroecosystem structure and functioning, and detect plants and large mammals with distinct spectral or phenological traits. UAV imagery and camera traps offer higher resolution for monitoring smaller or cryptic species. Remote sensing also supports indirect methods, such as species distribution modeling, to infer the occurrence of species that are difficult to detect. Key challenges include technological, methodological and operational limitations. Integrating multi-platform data, establishing shared benchmarks, and broadening taxonomic coverage, supported by policy incentives and farmer-friendly technologies, are essential for scaling biodiversity monitoring and promoting sustainable strategies that reconcile conservation with agricultural production.
Aphantasia, the strong diminution or complete absence of mental imagery, challenges long-standing views of imagery as central to cognition. Competing accounts variously explain the phenomenon as a failure of sensory reactivation or as unconscious mental imagery. Here, we propose a new framework, the integration model of aphantasia, which argues that reactivated sensory information must undergo multi-stage integration to yield imagery experience. Against unconscious imagery accounts, we argue that the neural activations observed in aphantasics are not imagery but sensory precursors: rudimentary sensory codes that lack perceptual status. Only when sensory precursors are locally integrated do they become perceptual representations, and only when these are further integrated with interoceptive signals do they give rise to conscious imagery experience. We present the integration model as a dual-stream framework that unifies recent attention- and interoception-based accounts, situate it within existing theories of mental imagery and aphantasia, and highlight its clinical relevance. In doing so, we reframe the debate on unconscious imagery and draw attention to the role of multi-stage integration as a key mechanism underlying mental imagery and its absence across different subtypes of aphantasia.
