ALBERT

Description: Combining a climatic envelope modeling technique with more than two centuries (1800-2009) of distribution records has revealed the effects of a changing climate on the egg-laying monotreme, the platypus, Ornithorhynchus anatinus . We show that the main factor associated with platypus occurrence switched from aquatic habitat availability (estimated by rainfall) to thermal tolerances (estimated by annual maximum temperature) in the 1960's. This correlates directly with the change in the annual maximum temperature anomaly from cooler to warmer conditions in southeastern Australia. Modeling of platypus habitat under emission scenarios (A1B, A2, B1 and B2) revealed large decreases (〉 30%) in thermally suitable habitat by 2070. This reduction, compounded by increasing demands for water for agriculture and potable use, suggests that there is real cause for concern over the future status of this species, and highlights the need for restoration of thermal refugia within the platypus’ modeled range.

Description: Intergovernmental Panel on Climate Change (IPCC) Tier 1 methodologies commonly underpin project-scale carbon accounting for changes in land use and management and are used in frameworks for Life Cycle Assessment and carbon footprinting of food and energy crops. These methodologies were intended for use at large spatial scales. This can introduce error in predictions at finer spatial scales. There is an urgent need for development and implementation of higher tier methodologies that can be applied at fine spatial scales (e.g. farm/project/plantation) for food and bioenergy crop greenhouse gas (GHG) accounting to facilitate decision making in the land-based sectors. Higher tier methods have been defined by IPCC and must be well evaluated and operate across a range of domains (e.g. climate region, soil type, crop type, topography), and must account for land use transitions and management changes being implemented. Furthermore, the data required to calibrate and drive the models used at higher tiers need to be available and applicable at fine spatial resolution, covering the meteorological, soil, cropping system and management domains, with quantified uncertainties. Testing the reliability of the models will require data either from sites with repeated measurements or from chronosequences. We review current global capability for estimating changes in soil carbon at fine spatial scales and present a vision for a framework capable of quantifying land use change and management impacts on soil carbon, which could be used for addressing issues such as bioenergy and biofuel sustainability, food security, forest protection, and direct/indirect impacts of land use change. The aim of this framework is to provide a globally accepted standard of carbon measurement and modelling appropriate for GHG accounting that could be applied at project to national scales (allowing outputs to be scaled up to a country level), to address the impacts of land use and land management change on soil carbon.

Description: Interannual variability in biosphere-atmosphere exchange of CO 2 is driven by a diverse range of biotic and abiotic factors. Replicating this variability thus represents the ‘acid test’ for terrestrial biosphere models. Although such models are commonly used to project responses to both normal and anomalous variability in climate, they are rarely tested explicitly against inter-annual variability in observations. Herein, using standardized data from the North American Carbon Program, we assess the performance of 16 terrestrial biosphere models and 3 remote sensing products against long-term measurements of biosphere-atmosphere CO 2 exchange made with eddy-covariance flux towers at 11 forested sites in North America. Instead of focusing on model-data agreement we take a systematic, variability-oriented approach and show that although the models tend to reproduce the mean magnitude of the observed annual flux variability, they fail to reproduce the timing. Large biases in modeled annual means are evident for all models. Observed interannual variability is found to commonly be on the order of magnitude of the mean fluxes. None of the models consistently reproduce observed interannual variability within measurement uncertainty. Underrepresentation of variability in spring phenology, soil thaw and snowpack melting, and difficulties in reproducing the lagged response to extreme climatic events are identified as systematic errors, common to all models included in this study.

Description: Interannual variability in biosphere-atmosphere exchange of CO 2 is driven by a diverse range of biotic and abiotic factors. Replicating this variability thus represents the ‘acid test’ for terrestrial biosphere models. Although such models are commonly used to project responses to both normal and anomalous variability in climate, they are rarely tested explicitly against inter-annual variability in observations. Here, using standardized data from the North American Carbon Program, we assess the performance of 16 terrestrial biosphere models and 3 remote sensing products against long-term measurements of biosphere-atmosphere CO 2 exchange made with eddy-covariance flux towers at 11 forested sites in North America. Instead of focusing on model-data agreement we take a systematic, variability-oriented, approach and show that although the models tend to reproduce the mean magnitude of the observed annual flux variability, they fail to reproduce the timing. Large biases in modeled annual means are evident for all models. Observed interannual variability is found to commonly be on the order of magnitude of the mean fluxes. None of the models consistently reproduce observed interannual variability within measurement uncertainty. Underrepresentation of variability in spring phenology, soil thaw and snowpack melting, and difficulties in reproducing the lagged response to extreme climatic events are identified as systematic errors, common to all models included in this study.

Description: Phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive interactions and feedbacks to the climate system. We conducted an analysis to evaluate the representation of phenology, and the associated seasonality of ecosystem-scale CO 2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis. Model predictions were evaluated using long-term measurements (emphasizing the period 2000-2006) from 10 forested sites within the AmeriFlux and Fluxnet-Canada networks. In deciduous forests, almost all models consistently predicted that the growing season started earlier, and ended later, than was actually observed; biases of 2 weeks or more were typical. For these sites, most models were also unable to explain more than a small fraction of the observed interannual variability in phenological transition dates. Finally, for deciduous forests, misrepresentation of the seasonal cycle resulted in over-prediction of gross ecosystem photosynthesis by +160 ± 145 g C m −2 y −1 during the spring transition period, and +75 ± 130 g C m −2 y −1 during the autumn transition period (13% and 8% annual productivity, respectively) compensating for the tendency of most models to under-predict the magnitude of peak summertime photosynthetic rates. Models did a better job of predicting the seasonality of CO 2 exchange for evergreen forests. These results highlight the need for improved understanding of the environmental controls on vegetation phenology, and incorporation of this knowledge into better phenological models. Existing models are unlikely to predict future responses of phenology to climate change accurately, and therefore will misrepresent the seasonality and interannual variability of key biosphere-atmosphere feedbacks and interactions in coupled global climate models.

Description: Intergovernmental Panel on Climate Change (IPCC) Tier 1 methodologies commonly underpin project-scale carbon accounting for changes in land use and management, and are used in frameworks for Life Cycle Assessment and carbon footprinting of food and energy crops. These methodologies were intended for use at large spatial scales. This can introduce error in predictions at finer spatial scales. There is an urgent need for development and implementation of higher tier methodologies that can be applied at fine spatial scales (e.g. farm/project/plantation) for food and bioenergy crop GHG accounting to facilitate decision making in the land-based sectors. Higher tier methods have been defined by IPCCand must be well evaluated and operate across a range of domains (e.g. climate region, soil type, crop type, topography), and must account for land use transitions and management changes being implemented. Furthermore, the data required to calibrate and drive the models used at higher tiers need to be available and applicable at fine spatial resolution, covering the meteorological, soil, cropping system and management domains, with quantified uncertainties. Testing the reliability of the models will require data either from sites with repeated measurements, or from chronosequences. We review current global capability for estimating changes in soil carbon at fine spatial scales, and present a vision for a framework capable of quantifying land use change and management impacts on soil carbon, which could be used for addressing issues such as bioenergy and biofuel sustainability, food security, forest protection, and direct/indirect impacts of land use change. The aim of this framework is to provide a globally-accepted standard of carbon measurement and modelling appropriate for GHG accounting that could be applied at project to national scales (allowing outputs to be scaled up to a country level), to address the impacts of land use and land management change on soil carbon.

Description: An intensive regional research campaign was conducted by the North American Carbon Program (NACP) in 2007 to study the carbon cycle of the highly productive agricultural regions of the Midwestern United States. Forty-_ve di_erent associated projects were conducted across _ve U.S. agencies over the course of nearly a decade involving hundreds of researchers. One of the primary objectives of the intensive campaign was to investigate the ability of atmospheric inversion techniques to use highly calibrated CO2 mixing ratio data to estimate CO2 ux over the major croplands of the U.S. by comparing the results to an inventory of CO2 uxes. Statistics from densely monitored crop production, consisting primarily of corn and soybeans, provided the backbone of a well studied bottom up inventory ux estimate that was used to evaluate the atmospheric inversion results. Estimates were compared to the inventory from three di_erent inversion systems, representing spatial scales varying from high resolution mesoscale (PSU), to continental (CSU) and global (CarbonTracker), coupled to di_erent transport models and optimization techniques. The inversion-based mean CO2-C sink estimates were generally slightly larger, 8% to 20% for PSU, 10% to 20% for CSU, and 21% for CarbonTracker, but statistically indistinguishable, from the inventory estimate of 135 TgC. While the comparisons show that the MCI region-wide C sink is robust across inversion system and spatial scale, only the continental and mesoscale inversions were able to reproduce the spatial patterns within the region. In general, the results demonstrate that inversions can recover CO2 uxes at sub-regional scales with a relatively high density of CO2 observations and adequate information on atmospheric transport in the region. © 2013 Blackwell Publishing Ltd

Description: Recent studies suggest that species distribution models (SDMs) based on fine-scale climate data may provide markedly different estimates of climate change impacts than coarse-scale models. However, these studies disagree in their conclusions of how scale influences projected species distributions. In rugged terrain, coarse-scale climate grids may not capture topographically-controlled climate variation at the scale that constitutes microhabitat or refugia for some species. While finer-scale data are therefore considered to better reflect climatic conditions experienced by species, there have been few formal analyses of how modeled distributions differ with scale. We modeled distributions for 52 plant species endemic to the California Floristic Province of different life forms and range sizes under recent and future climate across a 2000-fold range of spatial scales (0.008 km 2 – 16 km 2 ). We produced unique current and future climate datasets by separately downscaling 4-km climate models to three finer resolutions based on 800 m, 270 m, and 90 m digital elevation models and deriving bioclimatic predictors from them. As climate data resolution became coarser, SDMs predicted larger habitat area with diminishing spatial congruence between fine- and coarse-scale predictions. These trends were most pronounced at the coarsest resolutions and depended on climate scenario and species’ range size. On average, SDMs projected onto 4-km climate data predicted 42% more stable habitat (the amount of spatial overlap between predicted current and future climatically suitable habitat) compared to 800 m data. We found only modest agreement between areas predicted to be stable by 90-m models generalized to 4-km grids compared to areas classified as stable based on 4 km models, suggesting that some climate refugia captured at finer scales may be missed using coarser-scale data. These differences in projected locations of habitat change may have more serious implications than net habitat area when predictive maps form the basis of conservation decision-making. © 2012 Blackwell Publishing Ltd

Description: Refugia have been suggested as priority sites for conservation under climate change because of their ability to facilitate survival of biota under adverse conditions. Here we review the likely role of refugial habitats in conserving freshwater biota in arid Australian aquatic systems where the major long term climatic influence has been aridification. We introduce a conceptual model that characterises evolutionary refugia and ecological refuges based on our review of the attributes of aquatic habitats and freshwater taxa (fishes and aquatic invertebrates) in arid Australia. We also identify methods of recognizing likely future refugia and approaches to assessing the vulnerability of arid-adapted freshwater biota to a warming and drying climate. Evolutionary refugia in arid areas are characterised as permanent, groundwater-dependent habitats (subterranean aquifers and springs) supporting vicariant relicts and short-range endemics. Ecological refuges can vary across space and time, depending on the dispersal abilities of aquatic taxa and the geographical proximity and hydrological connectivity of aquatic habitats. The most important are the perennial waterbodies (both groundwater and surface water fed) that support obligate aquatic organisms. These species will persist where suitable habitats are available and dispersal pathways are maintained. For very mobile taxa (invertebrates with an aerial dispersal phase) evolutionary refugia may also act as ecological refuges. Evolutionary refugia are likely future refugia because their water source (groundwater) is decoupled from local precipitation. However, their biota is extremely vulnerable to changes in local conditions because population extinction risks cannot be abated by the dispersal of individuals from other sites. Conservation planning must incorporate a high level of protection for aquifers that support refugial sites. Ecological refuges are vulnerable to changes in regional climate because they have little thermal or hydrological buffering. Accordingly, conservation planning must focus on maintaining meta-population processes, especially through dynamic connectivity between aquatic habitats at a landscape scale. © 2013 Blackwell Publishing Ltd

Description: We examined postfire regeneration of chaparral shrubs during an intense drought. This study focused on the demography and physiology of shrub species that resprout from a basal lignotuber following fire. We found significant levels of resprout mortality when intense drought occurred in the year following fire during the period of shrub recovery. Three of the seven sampled resprouting species had the greatest or near greatest levels of mortality ever recorded when compared to previous studies. Most shrub mortality occurred during the drought after individuals had resprouted (i.e. individuals survived fire, resprouted and then subsequently died). Physiological measurements of species with high mortality suggested that resprout stems were highly embolized and xylem hydraulic conductivities were close to zero during the peak of the drought. In addition, lignotubers of two of the three species experiencing high mortality were depleted of starch. Population densities of most shrub species declined after the drought compared with their prefire levels, with the exception of one drought tolerant obligate seeding species. Resprouting shrub species may deplete their carbohydrate reserves during the resprouting process, making them particularly vulnerable to drought because of the need to transpire water to acquire the CO 2 that is used to supply energy to a large respiring root system. Drought appears to interact with fire by altering postfire shrub recovery and altering species abundances and composition of chaparral communities.