In the past three decades, our global climate has been experiencing unprecedented warming. This warming has and will continue to significantly influence the structure and function of forest ecosystems. While studies have been conducted to explore the possible responses of forest landscapes to future climate change, the representative concentration pathways (RCPs) scenarios under the framework of the Coupled Model Intercomparison Project Phase 5 (CMIP5) have not been widely used in quantitative modeling research of forest landscapes. We used LANDIS-II, a forest dynamic landscape model, coupled with a forest ecosystem process model (PnET-II), to simulate spatial interactions and ecological succession processes under RCP scenarios, RCP2.6, RCP4.5 and RCP8.5, respectively. We also modeled a control scenario of extrapolating current climate conditions to examine changes in distribution and aboveground biomass (AGB) among five different forest types for the period of 2010-2100 in Taihe County in southern China, where subtropical coniferous plantations dominate. The results of the simulation show that climate change will significantly influence forest distribution and AGB. (i) Evergreen broad-leaved forests will expand into Chinese fir and Chinese weeping cypress forests. The area percentages of evergreen broad-leaved forests under RCP2.6, RCP4.5, RCP8.5 and the control scenarios account for 18.25%, 18.71%, 18.85% and 17.46% of total forest area, respectively. (ii) The total AGB under RCP4.5 will reach its highest level by the year 2100. Compared with the control scenarios, the total AGB under RCP2.6, RCP4.5 and RCP8.5 increases by 24.1%, 64.2% and 29.8%, respectively. (iii) The forest total AGB increases rapidly at first and then decreases slowly on the temporal dimension. (iv) Even though the fluctuation patterns of total AGB will remain consistent under various future climatic scenarios, there will be certain responsive differences among various forest types.

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The future of wild tigers is dire, and the Global Tiger Initiative's (GTI) goal of doubling tiger population size by the next year of the tiger in 2022 will be challenging. The GTI has identified 20 tiger conservation landscapes (TCL) within which recovery actions will be needed to achieve these goals. The Amur tiger conservation landscape offers the best hope for tiger recovery in China where all other subspecies have most likely become extirpated. To prioritize recovery planning within this TCL, we used tiger occurrence data from adjacent areas of the Russian Far East to develop two empirical models of potential habitat that were then averaged with an expert-based habitat suitability model to identify potential tiger habitat in the Changbaishan ecosystem in Northeast China. We assessed the connectivity of tiger habitat patches using least-cost path analysis calibrated against known tiger movements in the Russian Far East to identify priority tiger conservation areas (TCAs). Using a habitat-based population estimation approach, we predicted that a potential of 98 (83-112) adult tigers could occupy all TCAs in the Changbaishan ecosystem. By combining information about habitat quality, connectivity and potential population size, we identified the three best TCAs totaling over 25 000 km 2 of potential habitat that could hold 79 (63-82) adult tigers. Strong recovery actions are needed to restore potential tiger habitat to promote recovery of Amur tigers in China, including restoring ungulate populations, increasing tiger survival through improved anti-poaching activities, landuse planning that reduces human access and agricultural lands in and adjacent to key TCAs, and maintaining connectivity both within and across international boundaries. Our approach will be useful in other TCLs to prioritize recovery actions to restore worldwide tiger populations.Restoring tiger conservation landscapes M. Hebblewhite et al. 580Animal Conservation 15 (2012) 579-592