Intermediate coupling between aboveground and belowground biomass maximises the persistence of grasslands

Aboveground and belowground biomass compartments of vegetation fulfil different functions and they are coupled by complex interactions. These compartments exchange water, carbon and nutrients and the belowground biomass compartment has the capacity to buffer vegetation dynamics when aboveground biomass is removed by disturbances such as herbivory or fire. However, despite their importance, root-shoot interactions are often ignored in more heuristic vegetation models. Here, we present a simple two-compartment grassland model that couples aboveground and belowground biomass. In this model, the growth of belowground biomass is influenced by aboveground biomass and the growth of aboveground biomass is influenced by belowground biomass. We used the model to explore how the dynamics of a grassland ecosystem are influenced by fire and grazing. We show that the grassland system is most persistent at intermediate levels of aboveground-belowground coupling. In this situation, the system can sustain more extreme fire or grazing regimes than in the case of strong coupling. In contrast, the productivity of the system is maximised at high levels of coupling. Our analysis suggests that the yield of a grassland ecosystem is maximised when coupling is strong, however, the intensity of disturbance that can be sustained increases dramatically when coupling is intermediate. Hence, the model predicts that intermediate coupling should be selected for as it maximises the chances of persistence in disturbance driven ecosystems

Rainfall or Price Variability: What Determines Rangeland Management Decisions? A Simulation-Optimization Approach to South African Savannas

Savannas cover the greater part of Africa and Australia and almost half of South America and contribute to the livelihoods of more than 350 million people. With the intensification of land use during the second half of the 20th century, savannas have become increasingly degraded through bush encroachment as a consequence of increased grazing pressure. Research on rangeland dynamics, however, provides contradicting answers with regard to the causes and possible remedies of bush encroachment. In this paper we present results from an application of a simulation-optimization model to the case of extensive rangeland management in South Africa. Our model differs from previous approaches in that it explicitly accounts for the influence of stochastic prices and rainfall on economically optimal management decisions. By showing the implications of neglecting price variation and stochasticity in rangeland models we provide new insights with regard to the determinants of bush encroachment and rangeland managers' economic utility. We demonstrate that, in the case of South Africa, optimal rangeland management is likely to lead to bush encroachment that eventually makes livestock holding unprofitable. Yet, we identify the costs of fire management to be a limiting factor for managers to counteract bush encroachment and explore the impact of policy measures to reduce fire control costs on the ecological and economic sustainability of livestock holding.Equilibrium, bio-economic modeling, grassland management, sustainable strategies, stochastic conditions, Livestock Production/Industries, Q57,

Музично-етнографічні польові дослідження (на прикладі обстеження історичної Хотинщини)

The author of the article researches the approaches of musical-ethnographic work, its methods and goals, as well as the choice of the specific territory and its exploration defined by them. The author comments on his intention to examine the area of Khotyn, which now is a part of Chernivtsy region; explains the methods of examining the territory. Pluses and minuses of the existing song collections dedicated to the given district are under consideration in this article. In conclusion short information about Northern Bessarabia and its population is given

Global ecosystems and fire: multi-model assessment of fire-induced tree cover and carbon storage reduction

In this study, we use simulations from seven global vegetation models to provide the first multi‐model estimate of fire impacts on global tree cover and the carbon cycle under current climate and anthropogenic land use conditions, averaged for the years 2001‐2012. Fire reduces the tree covered area and vegetation carbon storage by 10%. Regionally the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to zero. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire‐induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state‐of‐the‐art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems

A social-ecological approach to identify and quantify biodiversity tipping points in South America’s seasonal dry ecosystems

ropical dry forests and savannas harbour unique biodiversity and provide critical ES, yet they are under severe pressure globally. We need to improve our understanding of how and when this pressure provokes tipping points in biodiversity and the associated social-ecological systems. We propose an approach to investigate how drivers leading to natural vegetation decline trigger biodiversity tipping and illustrate it using the example of the Dry Diagonal in South America, an understudied deforestation frontier. The Dry Diagonal represents the largest continuous area of dry forests and savannas in South America, extending over three million km² across Argentina, Bolivia, Brazil, and Paraguay. Natural vegetation in the Dry Diagonal has been undergoing large-scale transformations for the past 30 years due to massive agricultural expansion and intensification. Many signs indicate that natural vegetation decline has reached critical levels. Major research gaps prevail, however, in our understanding of how these transformations affect the unique and rich biodiversity of the Dry Diagonal, and how this affects the ecological integrity and the provisioning of ES that are critical both for local livelihoods and commercial agriculture.Fil: Thonicke, Kirsten. Institute for Climate Impact Research ; AlemaniaFil: Langerwisch, Fanny. Institute for Climate Impact Research ; Alemania. Czech University of Life Sciences Prague; República ChecaFil: Baumann, Matthias. Humboldt Universität zu Berlin; Alemania. Technische Universitat Carolo Wilhelmina Zu Braunschweig.; AlemaniaFil: Leitão, Pedro J.. Humboldt Universität zu Berlin; Alemania. Technische Universitat Carolo Wilhelmina Zu Braunschweig.; AlemaniaFil: Václavík, Tomáš. Helmholtz Centre for Environmental Research; Alemania. Palacký University Olomouc; República ChecaFil: Alencar, Anne. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Simões, Margareth. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); BrasilFil: Scheiter, Simon. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); Brasil. Universidade Federal do Rio de Janeiro; BrasilFil: Langan, Liam. Senckenberg Biodiversity and Climate Research Centre; AlemaniaFil: Bustamante, Mercedes. Universidade do Brasília; BrasilFil: Gasparri, Nestor Ignacio. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Ecología Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaFil: Hirota, Marina. Universidade Federal de Santa Catarina; Brasil. Universidade Estadual de Campinas; BrasilFil: Börner, Jan. Universitat Bonn; AlemaniaFil: Rajao, Raoni. Universidade Federal de Minas Gerais; BrasilFil: Soares Filho, Britaldo. Universidade Federal de Minas Gerais; BrasilFil: Yanosky, Alberto. Consejo Nacional de Ciencia y Tecnología; ParaguayFil: Ochoa Quinteiro, José Manuel. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt; ColombiaFil: Seghezzo, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; ArgentinaFil: Conti, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: de la Vega Leiner, Anne Cristina. Universität Greifswald; Alemani

Challenges and opportunities in land surface modelling of savanna ecosystems

The savanna complex is a highly diverse global biome that occurs within the seasonally dry tropical to sub-tropical equatorial latitudes and are structurally and functionally distinct from grasslands and forests. Savannas are open-canopy environments that encompass a broad demographic continuum, often characterised by a changing dominance between C3-tree and C4-grass vegetation, where frequent environmental disturbances such as fire modulates the balance between ephemeral and perennial life forms. Climate change is projected to result in significant changes to the savanna floristic structure, with increases to woody biomass expected through CO2 fertilisation in mesic savannas and increased tree mortality expected through increased rainfall interannual variability in xeric savannas. The complex interaction between vegetation and climate that occurs in savannas has traditionally challenged terrestrial biosphere models (TBMs), which aim to simulate the interaction between the atmosphere and the land surface to predict responses of vegetation to changing in environmental forcing. In this review, we examine whether TBMs are able to adequately represent savanna fluxes and what implications potential deficiencies may have for climate change projection scenarios that rely on these models. We start by highlighting the defining characteristic traits and behaviours of savannas, how these differ across continents and how this information is (or is not) represented in the structural framework of many TBMs. We highlight three dynamic processes that we believe directly affect the water use and productivity of the savanna system: phenology, root-water access and fire dynamics. Following this, we discuss how these processes are represented in many current-generation TBMs and whether they are suitable for simulating savanna fluxes.Finally, we give an overview of how eddy-covariance observations in combination with other data sources can be used in model benchmarking and intercomparison frameworks to diagnose the performance of TBMs in this environment and formulate road maps for future development. Our investigation reveals that many TBMs systematically misrepresent phenology, the effects of fire and root-water access (if they are considered at all) and that these should be critical areas for future development. Furthermore, such processes must not be static (i.e. prescribed behaviour) but be capable of responding to the changing environmental conditions in order to emulate the dynamic behaviour of savannas. Without such developments, however, TBMs will have limited predictive capability in making the critical projections needed to understand how savannas will respond to future global change

Linking scales and disciplines : an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management

CITATION: Berger, C. et al. 2019. Linking scales and disciplines : an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management. Climatic Change, 156:139–150, doi:10.1007/s10584-019-02544-0.The original publication is available at https://www.springer.com/journal/10584Southern Africa is particularly sensitive to climate change, due to both ecological and socioeconomic factors, with rural land users among the most vulnerable groups. The provision of information to support climate-relevant decision-making requires an understanding of the projected impacts of change and complex feedbacks within the local ecosystems, as well as local demands on ecosystem services. In this paper, we address the limitation of current approaches for developing management relevant socio-ecological information on the projected impacts of climate change and human activities.We emphasise the need for linking disciplines and approaches by expounding the methodology followed in our two consecutive projects. These projects combine disciplines and levels of measurements from the leaf level (ecophysiology) to the local landscape level (flux measurements) and from the local household level (socio-economic surveys) to the regional level (remote sensing), feeding into a variety of models at multiple scales. Interdisciplinary, multi-scaled, and integrated socio-ecological approaches, as proposed here, are needed to compliment reductionist and linear, scalespecific approaches. Decision support systems are used to integrate and communicate the data and models to the local decision-makers.https://link.springer.com/article/10.1007/s10584-019-02544-0Publisher's versio

Modeling the multi-functionality of African savanna landscapes under global change

Various recent publications have indicated that accelerated global change and its negative impacts on terrestrial ecosystems in Southern Africa urgently demand quantitative assessment and modelling of a range of ecosystem services on which rural communities depend. Information is needed on how these Ecosystem Services (ES) can be enhanced through sustainable land management interventions and enabling policies. Yet, it has also been claimed that, to date, the required system analyses, data and tools to quantify important interactions between biophysical and socio-economic components, their resilience and ability to contribute to livelihood needs do not exist. We disagree, but acknowledge that building an appropriate integrative modelling framework for assessing the multi-functionality of savanna landscapes is challenging. Yet, in this Letter-to-the-Editor, we show that a number of suitable modelling components and required data already exist and can be mobilized and integrated with emerging data and tools to provide answers to problem-driven questions posed by stakeholders on land management and policy issues.German Federal Ministry of Education and Researchhttps://onlinelibrary.wiley.com/journal/1099145xhj2022Zoology and Entomolog

Biome classification influences the projected rate of future biome transitions

<p><strong>Aim. </strong>Biome classification schemes are widely used to map biogeographic patterns of vegetation formations on large spatial scales. Future climate change will influence  biome patterns, and vegetation models can be used to assess the susceptibility of biomes to experience  transitions. However, biome classification is not unique, and various classification schemes and biome maps exist. Here, we aimed to assess how the choice of biome classification schemes influences current and projected future biome patterns.</p> <p><strong>Location.</strong> Africa, Australia, Tropical Asia</p> <p><strong>Time period.</strong> 2000-2099</p> <p><strong>Major taxa studied.</strong> Tropical vegetation</p> <p><strong>Methods.</strong> We used adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation in the study region. We classified vegetation into biomes using (1) a classification scheme based on the cover of  functional types, (2) a cluster analysis based on the cover of  functional types, and (3) a cluster analysis based on trait patterns simulated by the aDGVM2. We compared the resulting biome maps to multiple observation-based biome maps and quantified differences in projected biome changes under the RCP8.5 scenario for the different classification schemes.</p> <p><strong>Results.</strong> As expected, biome patterns were strongly related to the scheme used for biome classification. The highest data-model agreement was derived for a cluster analysis using 21 simulated traits. Traits related to size were most important for classification. Considering all classification schemes, the area projected to undergo biome transitions under climate change varied between 16.5% and 32.1%. Despite this variability, different schemes consistently showed that grassland and savanna areas are most susceptible to climate change, whereas tropical forests and deserts are stable. Our results demonstrate that traits simulated by aDGVM2 are appropriate to delimit biomes.</p> <p><strong>Main conclusions. </strong> Studies projecting biome patterns and transitions under current and future climate should consider applying different biome classification schemes to avoid biases in such projections caused by biome classification schemes.</p&gt

Biome classification influences the projected rate of future biome transitions

<p><strong>Aim. </strong>Biome classification schemes are widely used to map biogeographic patterns of vegetation formations on large spatial scales. Future climate change will influence vegetation dynamics, and vegetation models can be used to assess the susceptibility of biomes to experience biome transitions. However, biome classification is not unique, and various classification schemes and biome maps exist. Here, we aimed to assess how the choice of biome classification schemes influences modeled rates of future biome changes.</p> <p><strong>Location. </strong>Africa, Australia, Tropical Asia <strong>Time period. </strong>2000-2099</p> <p><strong>Major taxa studied. </strong>Tropical vegetation</p> <p><strong>Methods. </strong>We used aDGVM2 to simulate vegetation in the study region. We classified vegetation into biomes using (1) a classification scheme based on the cover of different functional types, (2) a cluster analysis based on the cover of different functional types, and (3) a cluster analysis based on trait patterns simulated by the aDGVM2. We compared the resulting biome maps to multiple observation-based biome and land cover products and quantified differences in projected biome changes under the RCP8.5 scenario for the different classification schemes. </p> <p><strong>Results. </strong>As expected, biome patterns were strongly related to the scheme used for biome classification. The highest data-model agreement was derived for a cluster analysis using simulated trait patterns. The area projected to undergo biome transitions under climate change varied between 16.5% and 32.1% for different classification schemes. Despite this variability, different schemes consistently showed that grassland and savanna areas are most susceptible to climate change, whereas tropical forests and deserts are stable. Our results demonstrate that traits simulated by aDGVM2 are appropriate to delimit biomes. </p> <p><strong>Main conclusions. </strong>Studies projecting biome transitions under climate change should consider applying different biome classification schemes to avoid biases in such projections caused by biome classification schemes.</p> <p> </p&gt