Mechanistic modeling of environmental drivers of woolly mammoth carrying capacity declines on St. Paul Island.

On St. Paul Island, a remnant of the Bering Land Bridge, woolly mammoths persisted until 5,600 yr BP with no known predators or competitors, providing a natural system for studying hypothesized environmental drivers of extinction. These include overheating due to rising temperatures, starvation, and drought. Here, we test these hypotheses using Niche Mapper and LPJ-GUESS to mechanistically estimate mammoth metabolic rates and dietary and freshwater requirements and, from these, estimate variations in island carrying capacity on St. Paul for the last 17,000 yr. Population carrying capacity may have been several hundred individuals at the time of initial isolation from the mainland. Adult mammoths could have fasted for two to three months, indicating a necessary ability to access snow-buried forage. During the Holocene, vegetation net primary productivity increased, but shrinking island area overrode increased net primary productivity (NPP), lowering carrying capacity to ~100 individuals. NPP and freshwater availability alternated as critical limiting factors for this island population during the environmental changes of the late Pleistocene and Holocene. Only two or three individuals could have been sustained by the freshwater surplus in crater lakes (up to 18 individuals under the most optimistic parameter sensitivity experiments), suggesting that the St. Paul mammoth population was highly dependent on coastal freshwater sources. The simulations are consistent with the available proxy data, while highlighting the need to retrieve new paleohydrological proxy records from the coastal lagoons to test model predictions. More broadly, these findings reinforce the vulnerability of island megaherbivore populations to resource limitation and extinction.

Simulating the recent impacts of multiple biotic disturbances on forest carbon cycling across the United States.

Biotic disturbances (BDs, for example, insects, pathogens, and wildlife herbivory) substantially affect boreal and temperate forest ecosystems globally. However, accurate impact assessments comprising larger spatial scales are lacking to date although these are critically needed given the expected disturbance intensification under a warming climate. Hence, our quantitative knowledge on current and future BD impacts, for example, on forest carbon (C) cycling, is strongly limited. We extended a dynamic global vegetation model to simulate ecosystem response to prescribed tree mortality and defoliation due to multiple biotic agents across United States forests during the period 1997-2015, and quantified the BD-induced vegetation C loss, that is, C fluxes from live vegetation to dead organic matter pools. Annual disturbance fractions separated by BD type (tree mortality and defoliation) and agent (bark beetles, defoliator insects, other insects, pathogens, and other biotic agents) were calculated at 0.5° resolution from aerial-surveyed data and applied within the model. Simulated BD-induced C fluxes totaled 251.6 Mt C (annual mean: 13.2 Mt C year-1 , SD ±7.3 Mt C year-1 between years) across the study domain, to which tree mortality contributed 95% and defoliation 5%. Among BD agents, bark beetles caused most C fluxes (61%), and total insect-induced C fluxes were about five times larger compared to non-insect agents, for example, pathogens and wildlife. Our findings further demonstrate that BD-induced C cycle impacts (i) displayed high spatio-temporal variability, (ii) were dominated by different agents across BD types and regions, and (iii) were comparable in magnitude to fire-induced impacts. This study provides the first ecosystem model-based assessment of BD-induced impacts on forest C cycling at the continental scale and going beyond single agent-host systems, thus allowing for comparisons across regions, BD types, and agents. Ultimately, a perspective on the potential and limitations of a more process-based incorporation of multiple BDs in ecosystem models is offered.

Modelling changes in vegetation productivity and carbon balance under future climate scenarios in southeastern Australia.

Australia, characterized by extensive and heterogeneous terrestrial ecosystems, plays a critical role in the global carbon cycle and in efforts to mitigate climate change. Prior research has quantified vegetation productivity and carbon balance within the Australian context over preceding decades. Nonetheless, the responses of vegetation and carbon dynamics to the evolving phenomena of climate change and escalating concentrations of atmospheric carbon dioxide remain ambiguous within the Australian landscape. Here, we used LPJ-GUESS model to assess the impacts of climate change on Gross Primary Productivity (GPP) and Net Biome Productivity (NBP) of carbon for the state of New South Wales (NSW) in southeastern Australia. LPJ-GUESS simulations were driven by an ensemble of 27 global climate models under different emission scenarios. We investigated the change of GPP for different vegetation types and whether NSW ecosystems will be a net sink or source of carbon under climate change. We found that LPJ-GUESS successfully simulated GPP for the period 2003-2021, demonstrating a comparative performance with GPP derived from upscaled eddy covariance fluxes (R2 = 0.58, nRMSE = 14.2 %). The simulated NBP showed a larger interannual variation compared with flux data and other inversion products but could capture the timing of rainfall-driven carbon sink and source variations in 2015-2020. GPP would increase by 10.3-19.5 % under a medium emission scenario and 19.7-46.8 % under a high emission scenario. The mean probability of NSW acting as a carbon sink in the future showed a small decrease with a large uncertainty with >8 of the 27 climate models indicating an increased potential for carbon sink. These findings emphasize the significance of emission scenarios in shaping future carbon dynamics but also highlight considerable uncertainties stemming from different climate projections. Our study represents a baseline for understanding natural ecosystem dynamics and their key role in governing land carbon uptake and storage in Australia.

Latitude or altitude as the future refugium? A case for the future of forests in Asia Minor and its surroundings.

At the current juncture with climate change, centennial projections of species distributions in biodiversity hotspots, using dynamic vegetation models may provide vital insight into conservation efforts. This study aims to answer: (1) if climate change progresses under a business-as-usual scenario of anthropogenic emissions for this century, how may the forest ranges be affected? (2) will there be potential regional extinctions of the taxa simulated? (3) may any site emerge as a potential refugium? STUDY AREA: Anatolian Peninsula and its surroundings, longitudes 24-50° E, latitudes 33-46° N. Time Period: 1961-2100. Major Taxa Studied: 25 woody species and a C3 grass-type. METHOD: Keeping a spatial window large enough to track potential changes in the vegetation range and composition especially in the mountain ranges within the study area, we parameterized a process-based regional-to-global dynamic vegetation model (LPJ-GUESS v 4.1), forced it with ERA5-Land reanalysis for the historical period, and five different bias-corrected centennial global circulation model (GCM) datasets under SSP5-8.5, and simulated the dynamic responses of key forest species. Bivariate spatio-temporal maps from the simulation results were constructed for final analysis. RESULTS: A significant increase in woody taxa biomass for the majority of our study area, towards the end of the century was simulated, where temperate taxa with high tolerance for drought and a wider range of temperatures took dominance. The mountain ranges in our study area stood out as critical potential refugia for cold favoring species. There were no regional extinctions of taxa, however, important changes in areal dominance and potential future forest composition were simulated. MAIN CONCLUSIONS: Our simulation results suggest a high potential for future forest cover in our study region by the end of the century under a high emissions scenario, sans human presence, with important changes in vegetation composition, including encroachment of grasslands ecosystems by woody taxa.

Movement drives population dynamics of one of the most mobile ungulates on Earth: Insights from a mechanistic model.

Long-distance movements are hypothesized to positively influence population size and stability of mobile species. We tested this hypothesis with a novel modeling approach in which moving herbivores interact with the environment created by a dynamic global vegetation model using highly mobile Mongolian gazelles in the eastern Mongolian grasslands as a case study. Gazelle population dynamics were modeled from 1901 to 2018 under two scenarios, one allowing free movement and one restricting movement. Gazelles were 2.2 times more abundant when they could move freely and were extirpated in 71% of the study area when mobility was restricted. Mobility resulted in greater population increases during times of abundant forage and smaller population decreases during drought. Reduced thermoregulatory costs associated with climate change, combined with an increase in vegetation biomass, increased gazelle abundance. Since high abundances often resulted in overgrazing and, thus, extirpation when movement was restricted, mobility had an important role in maintaining higher densities. The novel modeling approach shows how accounting for not just herbivore but also plant ecophysiology can improve our understanding of the population dynamics of highly mobile herbivores, in particular when examining the effects of habitat and climate change. Since the model simulates herbivores based on general physiological mechanisms that apply across large herbivores and the vegetation model can be applied globally, it is possible to adapt the model to other large-herbivore systems.

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We analyzed the changes of net primary productivity (NPP) and net ecosystem productivity (NEP) of Quercus spp. forest and Robinia pseudoacacia plantation under different future climate scenarios in Shaanxi Province during 2015-2100, using the process-based dynamic vegetation model-LPJ-GUESS. The results showed that compared with the benchmark period (1961-1990), NPP of Quercus spp. forest and R. pseudoacacia plantation in northern Shaanxi would decrease by 4.9%-29.5% and 22.5%-56.2% respectively, while that in Guanzhong and southern Shaanxi would increase by 13.0%-49.0% and 21.3%-62.9% respectively in the future. The NPP of Quercus spp. forest and R. pseudoacacia plantation under the RCP8.5 scenario was the highest, followed by that under the RCP4.5 and RCP2.6 scenarios. Those two types of forest would be carbon sink in three subregions in the future. Quercus spp. forest would have stronger carbon sink function in nor-thern Shaanxi and Guanzhong, while R. pseudoacacia plantation would have stronger carbon sink function in Southern Shaanxi. Under different RCP scenarios, the NEP variation range of R. pseu-doacacia plantation was greater than that of Quercus spp. forest in three subregions.

Potential land use adjustment for future climate change adaptation in revegetated regions.

To adapt to future climate change, appropriate land use patterns are desired. Potential natural vegetation (PNV) emphasizing the dominant role of climate can provide a useful baseline to guide the potential land use adjustment. This work is particularly important for the revegetated regions with intensive human perturbation. However, it has received little attention. This study chose China's Loess Plateau, a typical revegetated region, as an example study area to generate the PNV patterns with high spatial resolution over 2071-2100 with a process-based dynamic vegetation model (LPJ-GUESS), and further investigated the potential land use adjustment through comparing the simulated and observed land use patterns. Compared with 1981-2010, the projected PNV over 2071-2100 would have less forest and more steppe because of drier climate. Subsequently, 25.3-55.0% of the observed forests and 79.3-91.9% of the observed grasslands in 2010 can be kept over 2071-2100, and the rest of the existing forested area and grassland were expected to be more suitable for steppes and forests, respectively. To meet the request of China's Grain for Green Project, 60.9-84.8% of the existing steep farmland could be converted to grassland and the other for forest. Our results highlight the importance in adjusting the existing vegetation pattern to adapt to climate change. The research approach is extendable and provides a framework to evaluate the sustainability of the existing land use pattern under future climate.

[Dynamic change of Pinus tabuliformis forest productivity and its response to future climate change in Shaanxi Province, China.] / 陕西省油松林生产力动态及对未来气候变化的响应.

This study analyzed the dynamics of net primary productivity (NPP) of Pinus tabuliformis forest under future climate scenarios in Shaanxi Province during 2015-2100, using a dynamic vegetation model (LPJ-GUESS). The results showed that in the 2015-2100 period, annual mean temperature of this region would significantly increase by 0.12, 0.23 and 0.54 ℃·10 a-1 under RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively, while the annual precipitation would have no significant change under climate scenarios except RCP4.5, under which it would significantly increase by 14.36 mm·10 a-1. Compared with the NPP of P. tabuliformis forest in the historical period (1961-1990), it would increase by 1.6%-29.6% in the future period, and the enhancement could reach 45.4% at the end of this century (2071-2100) under RCP8.5 scenario. The NPP under the RCP8.5 scenario was the highest, followed by the RCP4.5 and RCP2.6 scenarios. During 2015-2100, the NPP in the northern Shaanxi region would significantly decrease with the rate of 41.00 and 21.00 g C·m-2·10 a-1 under the RCP2.6 and RCP4.5 scenarios, respectively, implying that this area has the potentiality to be carbon source.

Drivers of dissolved organic carbon export in a subarctic catchment: Importance of microbial decomposition, sorption-desorption, peatland and lateral flow.

Tundra soils account for 50% of global stocks of soil organic carbon (SOC), and it is expected that the amplified climate warming in high latitude could cause loss of this SOC through decomposition. Decomposed SOC could become hydrologically accessible, which increase downstream dissolved organic carbon (DOC) export and subsequent carbon release to the atmosphere, constituting a positive feedback to climate warming. However, DOC export is often neglected in ecosystem models. In this paper, we incorporate processes related to DOC production, mineralization, diffusion, sorption-desorption, and leaching into a customized arctic version of the dynamic ecosystem model LPJ-GUESS in order to mechanistically model catchment DOC export, and to link this flux to other ecosystem processes. The extended LPJ-GUESS is compared to observed DOC export at Stordalen catchment in northern Sweden. Vegetation communities include flood-tolerant graminoids (Eriophorum) and Sphagnum moss, birch forest and dwarf shrub communities. The processes, sorption-desorption and microbial decomposition (DOC production and mineralization) are found to contribute most to the variance in DOC export based on a detailed variance-based Sobol sensitivity analysis (SA) at grid cell-level. Catchment-level SA shows that the highest mean DOC exports come from the Eriophorum peatland (fen). A comparison with observations shows that the model captures the seasonality of DOC fluxes. Two catchment simulations, one without water lateral routing and one without peatland processes, were compared with the catchment simulations with all processes. The comparison showed that the current implementation of catchment lateral flow and peatland processes in LPJ-GUESS are essential to capture catchment-level DOC dynamics and indicate the model is at an appropriate level of complexity to represent the main mechanism of DOC dynamics in soils. The extended model provides a new tool to investigate potential interactions among climate change, vegetation dynamics, soil hydrology and DOC dynamics at both stand-alone to catchment scales.

Comparison between remote sensing and a dynamic vegetation model for estimating terrestrial primary production of Africa.

BACKGROUND: Africa is an important part of the global carbon cycle. It is also a continent facing potential problems due to increasing resource demand in combination with climate change-induced changes in resource supply. Quantifying the pools and fluxes constituting the terrestrial African carbon cycle is a challenge, because of uncertainties in meteorological driver data, lack of validation data, and potentially uncertain representation of important processes in major ecosystems. In this paper, terrestrial primary production estimates derived from remote sensing and a dynamic vegetation model are compared and quantified for major African land cover types. RESULTS: Continental gross primary production estimates derived from remote sensing were higher than corresponding estimates derived from a dynamic vegetation model. However, estimates of continental net primary production from remote sensing were lower than corresponding estimates from the dynamic vegetation model. Variation was found among land cover classes, and the largest differences in gross primary production were found in the evergreen broadleaf forest. Average carbon use efficiency (NPP/GPP) was 0.58 for the vegetation model and 0.46 for the remote sensing method. Validation versus in situ data of aboveground net primary production revealed significant positive relationships for both methods. A combination of the remote sensing method with the dynamic vegetation model did not strongly affect this relationship. CONCLUSION: Observed significant differences in estimated vegetation productivity may have several causes, including model design and temperature sensitivity. Differences in carbon use efficiency reflect underlying model assumptions. Integrating the realistic process representation of dynamic vegetation models with the high resolution observational strength of remote sensing may support realistic estimation of components of the carbon cycle and enhance resource monitoring, providing suitable validation data is available.

Taking a closer look: disentangling effects of functional diversity on ecosystem functions with a trait-based model across hierarchy and time.

Biodiversity and ecosystem functioning (BEF) research has progressed from the detection of relationships to elucidating their drivers and underlying mechanisms. In this context, replacing taxonomic predictors by trait-based measures of functional composition (FC)-bridging functions of species and of ecosystems-is a widely used approach. The inherent challenge of trait-based approaches is the multi-faceted, dynamic and hierarchical nature of trait influence: (i) traits may act via different facets of their distribution in a community, (ii) their influence may change over time and (iii) traits may influence processes at different levels of the natural hierarchy of organization. Here, we made use of the forest ecosystem model 'LPJ-GUESS' parametrized with empirical trait data, which creates output of individual performance, community assembly, stand-level states and processes. To address the three challenges, we resolved the dynamics of the top-level ecosystem function 'annual biomass change' hierarchically into its various component processes (growth, leaf and root turnover, recruitment and mortality) and states (stand structures, water stress) and traced the influence of different facets of FC along this hierarchy in a path analysis. We found an independent influence of functional richness, dissimilarity and identity on ecosystem states and processes and hence biomass change. Biodiversity effects were only positive during early succession and later turned negative. Unexpectedly, resource acquisition (growth, recruitment) and conservation (mortality, turnover) played an equally important role throughout the succession. These results add to a mechanistic understanding of biodiversity effects and place a caveat on simplistic approaches omitting hierarchical levels when analysing BEF relationships. They support the view that BEF relationships experience dramatic shifts over successional time that should be acknowledged in mechanistic theories.