Search

While deforestation and forest degradation have gained attention in recent years not least at the UN climate negotiations, a third "de", i.e., defaunation, has to a great extent been overlooked. Human-induced faunal loss does not only reduce tree species diversity, but also significantly erodes key ecosystem services and functions and further disadvantages local communities. In this article, we analyze these impacts, and the associated multi-level governance gaps, through a case study of Nigeria's Cross River State and make suggestions for more encompassing conservation approaches that take defaunation into account. To this end, we analyze the interplay between current forest governance and REDD+ in Cross River State and local hunting of forest fauna. Drawing on Ostrom's social-ecological systems framework and a mixed-methods approach, we identify shortcomings and gaps of international and domestic forest governance, for instance, the ongoing expansion of agriculture in forest areas, a lack of collective action on forest fauna conservation at the local level, as well as conflicts amongst key actors at the sub-national level. Current REDD+ governance in Cross River State largely fails to address fauna loss and local hunting practices, but also affect allocation and access of environmental benefits and burdens for local people. [ABSTRACT FROM AUTHOR]

Forests are major carbon stores on a global scale but there are significant uncertainties about changes in carbon flux through time and the relative contributions of drivers such as land use, climate and atmospheric CO. We used the dynamic vegetation model LPJ-GUESS to test the relative influence of CO increase, temperature increase and management on carbon storage in living biomass in an unmanaged European temperate deciduous forest. The model agreed well with living biomass reconstructed from forest surveys and maximum biomass values from other studies. High-resolution climate data from both historical records and general circulation models were used to force the model and was manipulated for some simulations to allow relative contributions of individual drivers to be assessed. Release from management was the major driver of carbon storage for most of the historical period, whereas CO took over as the most important driver in the last 20 years. Relatively, little of the observed historical increase in carbon stocks was attributable to increased temperature. Future simulations using IPCC RCP4.5 and RCP8.5 scenarios indicated that carbon stocks could increase by as much as 3 kg C m by the end of the century, which is likely to be driven by CO increase. This study suggests that unmanaged semi-natural woodland in Europe can be a major potential carbon sink that has been previously underestimated. Increasing the area of unmanaged forest would provide carbon sink services during recovery from timber extraction, while long-term protection would ensure carbon stocks are maintained. [ABSTRACT FROM AUTHOR]

Plant functional response groups (PFGs) are now widely established as a tool to investigate plant—environment relationships. Different statistical methods to form PFGs are used in the literature. One way is to derive emergent groups by classifying species based on correlation of biological attributes and subjecting these groups to tests of response to environmental variables. Another way is to search for associations of occurrence data, environmental variables and trait data simultaneously. The fourth-corner method is one way to assess the relationships between single traits and habitat factors. We extended this statistical method to a generally applicable procedure for the generation of plant functional response groups by developing new randomization procedures for presence/absence data of plant communities. Previous PFG groupings used either predefined groups or emergent groups i.e. classifications based on correlations of biological attributes (Lavorel et al Trends Ecol Evol 12:474–478, 1997), of the global species pool and assessed their functional response. However, since not all PFGs might form emergent groups or may be known by experts, we used a permutation procedure to optimise functional grouping. We tested the method using an artificial test data set of virtual plants occurring in different disturbance treatments. Direct trait-treatment relationships as well as more complex associations are incorporated in the test data. Trait combinations responding to environmental variables could be clearly distinguished from non-responding combinations. The results are compared with the method suggested by Pillar (J Veg Sci 10:631–640) for the identification of plant functional groups. After exploring the statistical properties using an artificial data set, the method is applied to experimental data of a greenhouse experiment on the assemblage of plant communities. Four plant functional response groups are formed with regard to differences in soil fertility on the basis of the traits canopy height and spacer length. [ABSTRACT FROM AUTHOR]

Carbon storage dynamics in vegetation and soil are determined by the balance of carbon influx and turnover. Estimates of these opposing fluxes differ markedly among different empirical datasets and models leading to uncertainty and divergent trends. To trace the origin of such discrepancies through time and across major biomes and climatic regions, we used a model-data fusion framework. The framework emulates carbon cycling and its component processes in a global dynamic ecosystem model, LPJ-GUESS, and preserves the model-simulated pools and fluxes in space and time. Thus, it allows us to replace simulated carbon influx and turnover with estimates derived from empirical data, bringing together the strength of the model in representing processes, with the richness of observational data informing the estimations. The resulting vegetation and soil carbon storage and global land carbon fluxes were compared to independent empirical datasets. Results show model-data agreement comparable to, or even better than, the agreement between independent empirical datasets. This suggests that only marginal improvement in land carbon cycle simulations can be gained from comparisons of models with current-generation datasets on vegetation and soil carbon. Consequently, we recommend that model skill should be assessed relative to reference data uncertainty in future model evaluation studies. [ABSTRACT FROM AUTHOR]