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The widespread application of fertilizers has greatly influenced many processes and properties of agroecosystems, and agricultural fertilization is expected to increase even further in the future. To date, most research on fertilizer impacts has used short-term studies, which may be unrepresentative of long-term responses, thus hindering our capacity to predict long-term impacts. Here, we examined the effects of long-term fertilizer addition on key ecosystem properties in a long-term grassland experiment (Palace Leas Hay Meadow) in which farmyard manure (FYM) and inorganic fertilizer treatments have been applied consistently for 120 years in order to characterize the experimental site more fully and compare ecosystem responses with those observed at other long-term and short-term experiments. FYM inputs increased soil organic carbon (SOC) stocks, hay yield, nutrient availability and acted as a buffer against soil acidification (>pH 5). In contrast, N-containing inorganic fertilizers strongly acidified the soil (<pH 4.5) and increased surface SOC stocks by increasing the C stored in the coarse (2.8 mm-200 μm) and fine (200–50 μm) fractions. Application of N fertilizers also reduced plant species richness and the abundance of forbs and legumes. Overall, our results were broadly consistent with those observed in other very long-term studies (the Park Grass and Steinach Grassland experiments) in that fertilization effects on plant and soil properties appeared to be driven by differences in both nutrient input and changes to soil pH. We also established that the direction of long-term fertilization effects tended to be comparable with short-term experiments, but that their magnitude differed considerably, particularly where ammonium sulphate-induced acidification had occurred. We therefore conclude that short-term studies are unlikely to possess the required timeframe to accurately predict long-term responses, thus necessitating the use of long-term study sites. Such experiments should be strategically established in regions where future fertilizer use is expected to increase rapidly.
1. Plant-fungal interactions are important for plant community assembly, but quantifying these relationships remains challenging. High throughput sequencing of fungal communities allows us to identify plant-fungal associations at a high level of resolution, but often fails to provide information on taxonomic and functional assignment of fungi. 2. We transplanted seeds of Pinus cembra across an elevational gradient (1850–2250 m a.s.l.) and identified environmental factors and known fungal associates important for seedling establishment and survival. We then applied null model tests to identify taxonomically unassigned fungi associated with pine recruitment. 3. Early seedling establishment was determined by abiotic environmental factors, while seedling survival was predominantly affected by biotic environmental factors (i.e., the abundance of a fungal pathogen known from literature and the distance to adult trees). Null model tests identified known mycorrhizal partners and a large number of unknown operational taxonomic units (OTUs) associated with seedling survival, including saprotrophic and pathogenic species. These results highlight that unknown fungal OTUs, which are usually discarded from analyses, could play a crucial role for plant survival. 4. Synthesis. We conclude that high throughput metabarcoding paired with null model tests, is a valuable approach for identifying hidden plant-fungal associations within large and complex DNA metabarcoding datasets. Such an approach can be an important tool in illuminating the black box of plant-microbe interactions, and thus understanding ecosystem dynamics.
Establishing and maintaining protected areas (PAs) is a key action in delivering post-2020 biodiversity targets. PAs often need to meet multiple objectives, ranging from biodiversity protection to ecosystem service provision and climate change mitigation, but available land and conservation funding is limited. Therefore, optimizing resources by selecting the most beneficial PAs is vital. Here, we advocate for a flexible and transparent approach to selecting PAs based on multiple objectives, and illustrate this with a decision support tool on a global scale. The tool allows weighting and prioritization of different conservation objectives according to user-specified preferences as well as real-time comparison of the outcome. Applying the tool across 1,346 terrestrial PAs, we demonstrate that decision makers frequently face trade-offs among conflicting objectives, e.g., between species protection and ecosystem integrity. Nevertheless, we show that transparent decision support tools can reveal synergies and trade-offs associated with PA selection, thereby helping to illuminate and resolve land-use conflicts embedded in divergent societal and political demands and values.
The establishment and maintenance of protected areas(PAs) is viewed as a key action in delivering post-2020 biodiversity targets. PAs often need to meet a multitude of objectives, ranging from biodiversity protection to ecosystem service provision and climate change mitigation. As available land and conservation funding are limited, optimizing resources by selecting the most beneficial PAs is vital. Here we present a decision support tool that enables a flexible approach to PA selection on a global scale, allowing different conservation objectives to be weighted and prioritized according to user-specified preferences. We apply the tool across 1347 terrestrial PAs and highlight frequent trade-offs among different objectives, e.g., between biodiversity protection and ecosystem integrity. These results indicate that decision makers must usually decide among conflicting objectives. To assist this our decision support tool provides an explicitly value-based approach that can help resolve such conflicts by considering divergent societal and political demands and values.
The establishment and maintenance of protected areas (PAs) is viewed as a key action in delivering post-2020 biodiversity targets. PAs often need to meet multiple objectives, ranging from biodiversity protection to ecosystem service provision and climate change mitigation, but available land and conservation funding is limited. Therefore, optimizing resources by selecting the most beneficial PAs is vital. Here, we advocate for a flexible and transparent approach to selecting protected areas based on multiple objectives, and illustrate this with a decision support tool on a global scale. The tool allows weighting and prioritization of different conservation objectives according to user-specified preferences, as well as real-time comparison of the selected areas that result from such different priorities. We apply the tool across 1347 terrestrial PAs and highlight frequent trade-offs among different objectives, e.g., between species protection and ecosystem integrity. Outputs indicate that decision makers frequently face trade-offs among conflicting objectives. Nevertheless, we show that transparent decision-support tools can reveal synergies and trade-offs associated with PA selection, thereby helping to illuminate and resolve land-use conflicts embedded in divergent societal and political demands and values.