Aggregation of soil and climate input data can underestimate simulated biomass loss and nitrate leaching under climate change

Predicting areas of severe biomass loss and increased N leaching risk under climate change is critical for applying appropriate adaptation measures to support more sustainable agricultural systems. The frequency of annual severe biomass loss for winter wheat and its coincidence with an increase in N leaching in a temperate region in Germany was estimated including the error from using soil and climate input data at coarser spatial scales, using the soil-crop model CoupModel. We ran the model for a reference period (1980-2010) and used climate data predicted by four climate model(s) for the Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5. The annual median biomass estimations showed that for the period 2070-2100, under the RCP8.5 scenario, the entire region would suffer from severe biomass loss almost every year. Annual incidence of severe biomass loss and increased N leaching was predicted to increase from RCP4.5 to the 8.5 scenario. During 2070-2100 for RCP8.5, in more than half of the years an area of 95% of the region was projected to suffer from both severe biomass loss and increased N leaching. The SPEI3 predicted a range of 32 (P3 RCP4.5) to 55% (P3 RCP8.5) of the severe biomass loss episodes simulated in the climate change scenarios. The simulations predicted more severe biomass losses than by the SPEI index which indicates that soil water deficits are important in determining crop losses in future climate scenarios. There was a risk of overestimating the area where "no severe biomass loss + increased N leaching" occurred when using coarser aggregated input data. In contrast, underestimation of situations where "severe biomass loss + increased N leaching" occurred when using coarser aggregated input data. Larger annual differences in biomass estimations compared to the finest resolution of input data occurred when aggregating climate input data rather than soil data. The differences were even larger when aggregating both soil and climate input data. In half of the region, biomass could be erroneously estimated in a single year by more than 40% if using soil and climate coarser input data. The results suggest that a higher spatial resolution of especially climate input data would be needed to predict reliably annual estimates of severe biomass loss and N leaching under climate change scenarios

The Gaussian-Linear Hidden Markov model: a Python package

We propose the Gaussian-Linear Hidden Markov model (GLHMM), a generalisation of different types of HMMs commonly used in neuroscience. In short, the GLHMM is a general framework where linear regression is used to flexibly parameterise the Gaussian state distribution, thereby accommodating a wide range of uses -including unsupervised, encoding and decoding models. GLHMM is implemented as a Python toolbox with an emphasis on statistical testing and out-of-sample prediction -i.e. aimed at finding and characterising brain-behaviour associations. The toolbox uses a stochastic variational inference approach, enabling it to handle large data sets at reasonable computational time. Overall, the approach can be applied to several data modalities, including animal recordings or non-brain data, and applied over a broad range of experimental paradigms. For demonstration, we show examples with fMRI, electrocorticography, magnetoencephalo-graphy and pupillometry.Comment: 22 pages, 7 figures, 1 tabl

Scotland’s biodiversity progress to 2020 Aichi Targets:Conserving genetic diversity- development of a national approach for addressing Aichi Biodiversity Target 13 that includes wild species

Aichi Target 13 (T13) focuses on the conservation of genetic diversity. •Major challenges in implementing T13 are that the type of genetic diversity to conserve is not clearly defined, and that key issues in genetic conservation vary across different sectors (e.g., forestry vs agriculture vs other species of socio-economic importance). •In Scotland and the UK more widely, baseline mechanisms are well established for assessing and reporting on genetic diversity in species of agricultural importance (e.g., rare livestock breeds, crop wild relatives), and a methodology has been established for ornamental plants. •A new UK Strategy for Forest Genetics Resources was launched in 2019, creating a framework for linking forest trees into T13 reporting. •However, there is no clear strategy to deal with ‘other species of socio-economic importance’ in Scotland, the UK or indeed elsewhere, and addressing this gap is the major focus of this report. •There is a lack of guidance for identifying focal species of socio-economic importance, and no clear mechanism for addressing T13 for these species once they have been identified. •To address this, we have identified a set of criteria for defining terrestrial and freshwater species of socio-economic importance in Scotland, and selected an initial list of 26 species. •The criteria applied were: -National conservation priority wild species. -Species of national cultural importance. -Species providing key ecosystem services. -Species of importance for wild harvesting (food and medicine). -Economically important game species. •We then developed a simple, readily applicable scorecard method for assessing risks to the conservation of genetic diversity in these species. •The scorecard approach is not dependent on prior genetic knowledge, and instead uses structured expert opinion assessments of whether: -Demographic declines are likely to lead to loss of genetic diversity (genetic erosion). -Hybridisation is likely to lead to undesirable replacement of genetic diversity. -Restrictions to regeneration/turnover are likely to impede evolutionary change. •For plant species where seed-banking is a viable mechanism for holding genetic resources ex situ,we also report on the representativeness of these ex situ collections. •Overall, this scorecard provides a mechanism for incorporating ‘other species of socio-economic importance’ into T13 actions and reporting. •Furthermore, its application is not restricted to Aichi T13 as the approach is designed as a generic scorecard for genetic diversity. It is thus relevant to post-2020 CBD targets focusing on genetic diversity. •Future priorities include: -Extension to other species of socio-economic, commercial and cultural importance (with the inclusion of marine species being a particularly high priority). -Harmonising genetic conservation strategies between sectors (drawing on commonalities), whilst minimising disruption of existing well-established methodologies within sectors. -Greater incorporation of genomic data into monitoring genetic diversity (particularly in the agricultural and forestry sectors where data availability is potentially high)

Land cover change and carbon emissions over 100 years in an African biodiversity hotspot

Agricultural expansion has resulted in both land use and land cover change (LULCC) across the tropics. However, the spatial and temporal patterns of such change and their resulting impacts are poorly understood, particularly for the pre-satellite era. Here we quantify the LULCC history across the 33.9 million ha watershed of Tanzania's Eastern Arc Mountains, using geo-referenced and digitised historical land cover maps (dated 1908, 1923, 1949 and 2000). Our time series from this biodiversity hotspot shows that forest and savanna area both declined, by 74% (2.8 million ha) and 10% (2.9 million ha), respectively, between 1908 and 2000. This vegetation was replaced by a five-fold increase in cropland, from 1.2 million ha to 6.7 million ha. This LULCC implies a committed release of 0.9 Pg C (95% CI: 0.4-1.5) across the watershed for the same period, equivalent to 0.3 Mg C ha(-1) yr(-1) . This is at least three-fold higher than previous estimates from global models for the same study area. We then used the LULCC data from before and after protected area creation, as well as from areas where no protection was established, to analyse the effectiveness of legal protection on land cover change despite the underlying spatial variation in protected areas. We found that, between 1949 and 2000, forest expanded within legally protected areas, resulting in carbon uptake of 4.8 (3.8-5.7) Mg C ha(-1) , compared to a committed loss of 11.9 (7.2-16.6) Mg C ha(-1) within areas lacking such protection. Furthermore, for nine protected areas where LULCC data is available prior to and following establishment, we show that protection reduces deforestation rates by 150% relative to unprotected portions of the watershed. Our results highlight that considerable LULCC occurred prior to the satellite era, thus other data sources are required to better understand long-term land cover trends in the tropics. This article is protected by copyright. All rights reserved

Inequitable gains and losses from conservation in a global biodiversity hotspot

A billion rural people live near tropical forests. Urban populations need them for water, energy and timber. Global society benefits from climate regulation and knowledge embodied in tropical biodiversity. Ecosystem service valuations can incentivise conservation, but determining costs and benefits across multiple stakeholders and interacting services is complex and rarely attempted. We report on a 10-year study, unprecedented in detail and scope, to determine the monetary value implications of conserving forests and woodlands in Tanzania’s Eastern Arc Mountains. Across plausible ranges of carbon price, agricultural yield and discount rate, conservation delivers net global benefits (+US$8.2B present value, 20-year central estimate). Crucially, however, net outcomes diverge widely across stakeholder groups. International stakeholders gain most from conservation (+US$10.1B), while local-rural communities bear substantial net costs (-US$1.9B), with greater inequities for more biologically important forests. Other Tanzanian stakeholders experience conflicting incentives: tourism, drinking water and climate regulation encourage conservation (+US$72M); logging, fuelwood and management costs encourage depletion (-US$148M). Substantial global investment in disaggregating and mitigating local costs (e.g., through boosting smallholder yields) is essential to equitably balance conservation and development objectives

Protecting tropical forests from the rapid expansion of rubber using carbon payments

Expansion of Hevea brasiliensis rubber plantations is a resurgent driver of deforestation, carbon emissions, and biodiversity loss in Southeast Asia. Southeast Asian rubber extent is massive, equivalent to 67% of oil palm, with rapid further expansion predicted. Results-based carbon finance could dis-incentivise forest conversion to rubber, but efficacy will be limited unless payments match, or at least approach, the costs of avoided deforestation. These include opportunity costs (timber and rubber profits), plus carbon finance scheme setup (transaction) and implementation costs. Using comprehensive Cambodian forest data, exploring scenarios of selective logging and conversion, and assuming land-use choice is based on net present value, we find that carbon prices of $30-$51 per tCO2are needed to break even against costs, higher than those currently paid on carbon markets or through carbon funds. To defend forests from rubber, either carbon prices must be increased, or other strategies are needed, such as corporate zero-deforestation pledges, and governmental regulation and enforcement of forest protection

Effects of climate and atmospheric nitrogen deposition on early to mid-term stage litter decomposition across biomes

Litter decomposition is a key process for carbon and nutrient cycling in terrestrial ecosystems and is mainly controlled by environmental conditions, substrate quantity and quality as well as microbial community abundance and composition. In particular, the effects of climate and atmospheric nitrogen (N) deposition on litter decomposition and its temporal dynamics are of significant importance, since their effects might change over the course of the decomposition process. Within the TeaComposition initiative, we incubated Green and Rooibos teas at 524 sites across nine biomes. We assessed how macroclimate and atmospheric inorganic N deposition under current and predicted scenarios (RCP 2.6, RCP 8.5) might affect litter mass loss measured after 3 and 12 months. Our study shows that the early to mid-term mass loss at the global scale was affected predominantly by litter quality (explaining 73% and 62% of the total variance after 3 and 12 months, respectively) followed by climate and N deposition. The effects of climate were not litter-specific and became increasingly significant as decomposition progressed, with MAP explaining 2% and MAT 4% of the variation after 12 months of incubation. The effect of N deposition was litter-specific, and significant only for 12-month decomposition of Rooibos tea at the global scale. However, in the temperate biome where atmospheric N deposition rates are relatively high, the 12-month mass loss of Green and Rooibos teas decreased significantly with increasing N deposition, explaining 9.5% and 1.1% of the variance, respectively. The expected changes in macroclimate and N deposition at the global scale by the end of this century are estimated to increase the 12-month mass loss of easily decomposable litter by 1.1-3.5% and of the more stable substrates by 3.8-10.6%, relative to current mass loss. In contrast, expected changes in atmospheric N deposition will decrease the mid-term mass loss of high-quality litter by 1.4-2.2% and that of low-quality litter by 0.9-1.5% in the temperate biome. Our results suggest that projected increases in N deposition may have the capacity to dampen the climate-driven increases in litter decomposition depending on the biome and decomposition stage of substrate