Improved assessment of baseline and additionality for forest carbon crediting.

In the California compliance cap-and-trade carbon market, improved forest management (IFM) projects generate carbon credits in the initial reporting period if their initial carbon stocks are greater than a baseline. This baseline is informed by a "common practice" stocking value, which represents the average carbon stocks of surveyed privately owned forests that are classified into the same general forest type by the California Air Resources Board. Recent work has called attention to the need for more ecologically informed common practice carbon stocking values for IFM projects, particularly those in areas with sharp ecological gradients. Current methods for estimating common practice produce biases in baseline carbon values that lead to a clustering of IFM projects in geographical areas and ecosystem types that in fact support much greater forest carbon stocks than reflected in the common practice. This phenomenon compromises additionality, or the increases in carbon sequestration or decreases in carbon emissions that would not have occurred in the absence of carbon crediting. This study seeks to expand upon recent work on this topic and establish unbiased common practice estimates along sharp ecological gradients using methods that do not rely upon discrete forest classification. We generated common practice values for credited IFM projects in the Southern Cascades using a principal components analysis on species composition over an extensive forest inventory to determine the ecological similarity between inventoried forests and IFM project sites. Our findings strengthen the results of recent research suggesting common practice bias and adverse selection. At several sites, even after controlling for private ownership, 100% of the initial carbon stocks could be explained by ecological variables. This result means that improved management did not preserve or increase carbon stocks above what was typical, suggesting that no carbon offsets should have been issued for these sites. This result reveals greater bias than that been found at project sites in this region by research that has used discrete forest categorization.

Land management can contribute to net zero.

The voluntary carbon market needs to embrace changes for the land sector.

Crediting agricultural soil carbon sequestration.

Regional consistency is necessary for carbon credit integrity.

Predicting Plant Invasions Following China's Water Diversion Project.

China's South to North Water Diversion (SNWD) project connects portions of the Yangtze River in the south to the Yellow River system in the north, overcoming biogeographic barriers to water movement. The diversion will supply potable water to over 110 million people and provide multiple other socioeconomic benefits. However, an inadvertent negative impact of this connection includes creation of conduits for species invasions. Alligator weed (Alternanthera philoxeroides), water hyacinth (Eichhornia crassipes), and water lettuce (Pistia stratiotes) are the only aquatic plant species on China's shortlists for special control. These species are mainly invasive in the Yangtze River basin. If these species are able to invade the SNWD and further spread via the SNWD, they have the potential to alter water supply, including water quantity and quality, as well as local ecology and agriculture, threatening the goals of the diversion. Understanding the full potential for these species to invade northern China is critical to early management decisions to avoid costly negative impacts. We used Maxent modeling to evaluate the probability that each of these species might become invasive in the receiving water regions. The models predict that all three species will be able to expand their ranges northward, with alligator weed and water hyacinth having the greatest potential for range expansion. These results suggest the need for prevention, monitoring, and management strategies for these species to reduce the risk and costs of impacts.

Modeled Sea Level Rise Impacts on Coastal Ecosystems at Six Major Estuaries on Florida's Gulf Coast: Implications for Adaptation Planning.

The Sea Level Affecting Marshes Model (SLAMM) was applied at six major estuaries along Florida's Gulf Coast (Pensacola Bay, St. Andrews/Choctawhatchee Bays, Apalachicola Bay, Southern Big Bend, Tampa Bay and Charlotte Harbor) to provide quantitative and spatial information on how coastal ecosystems may change with sea level rise (SLR) and to identify how this information can be used to inform adaption planning. High resolution LiDAR-derived elevation data was utilized under three SLR scenarios: 0.7 m, 1 m and 2 m through the year 2100 and uncertainty analyses were conducted on selected input parameters at three sites. Results indicate that the extent, spatial orientation and relative composition of coastal ecosystems at the study areas may substantially change with SLR. Under the 1 m SLR scenario, total predicted impacts for all study areas indicate that coastal forest (-69,308 ha; -18%), undeveloped dry land (-28,444 ha; -2%) and tidal flat (-25,556 ha; -47%) will likely face the greatest loss in cover by the year 2100. The largest potential gains in cover were predicted for saltmarsh (+32,922 ha; +88%), transitional saltmarsh (+23,645 ha; na) and mangrove forest (+12,583 ha; +40%). The Charlotte Harbor and Tampa Bay study areas were predicted to experience the greatest net loss in coastal wetlands The uncertainty analyses revealed low to moderate changes in results when some numerical SLAMM input parameters were varied highlighting the value of collecting long-term sedimentation, accretion and erosion data to improve SLAMM precision. The changes predicted by SLAMM will affect exposure of adjacent human communities to coastal hazards and ecosystem functions potentially resulting in impacts to property values, infrastructure investment and insurance rates. The results and process presented here can be used as a guide for communities vulnerable to SLR to identify and prioritize adaptation strategies that slow and/or accommodate the changes underway.

Weed risk assessment for aquatic plants: modification of a New Zealand system for the United States.

We tested the accuracy of an invasive aquatic plant risk assessment system in the United States that we modified from a system originally developed by New Zealand's Biosecurity Program. The US system is comprised of 38 questions that address biological, historical, and environmental tolerance traits. Values associated with each response are summed to produce a total score for each species that indicates its risk of invasion. To calibrate and test this risk assessment, we identified 39 aquatic plant species that are major invaders in the continental US, 31 species that have naturalized but have no documented impacts (minor invaders), and 60 that have been introduced but have not established. These species represent 55 families and span all aquatic plant growth forms. We found sufficient information to assess all but three of these species. When the results are compared to the known invasiveness of the species, major invaders are distinguished from minor and non-invaders with 91% accuracy. Using this approach, the US aquatic weed risk assessment correctly identifies major invaders 85%, and non-invaders 98%, of the time. Model validation using an additional 10 non-invaders and 10 invaders resulted in 100% accuracy for the former, and 80% accuracy for the latter group. Accuracy was further improved to an average of 91% for all groups when the 17% of species with scores of 31-39 required further evaluation prior to risk classification. The high accuracy with which we can distinguish non-invaders from harmful invaders suggests that this tool provides a feasible, pro-active system for pre-import screening of aquatic plants in the US, and may have additional utility for prioritizing management efforts of established species.