An investigation of the evidence of benefits from climate compatible development

Climate change is likely to have profound effects on developing countries both through the climate impacts experienced, but also through the policies, programmes and projects adopted to address climate change. Climate change mitigation (actions taken to reduce the extent of climate change), adaptation (actions taken to ameliorate the impacts), and on-going development are all critical to reduce current and future losses associated with climate change, and to harness gains. In the context of limited resources to invest in climate change, policies, programmes, or projects that deliver ‘triple wins’ (i.e. generating climate adaptation, mitigation and development benefits) – also known as climate compatible development – are increasingly discussed by bilateral and multilateral donors. Yet there remains an absence of empirical evidence of the benefits and costs of triple win policies. The purpose of this paper is therefore to assess evidence of ‘triple wins’ on the ground, and the feasibility of triple wins that do not generate negative impacts. We describe the theoretical linkages that exist between adaptation, mitigation and development, as well as the trade-offs and synergies that might exist between them. Using four developing country studies, we make a simple assessment of the extent of climate compatible development policy in practice through the lens of ‘no-regrets’, ‘low regrets’ and ‘with regrets’ decision making. The lack of evidence of either policy or practice of triple wins significantly limits the capacity of donors to identify, monitor or evaluate ‘triple wins at this point in time. We recommend a more strategic assessment of the distributional and financial implications of 'triple wins' policies

Chemical Competition between Microscopic Stages of Macrocystis pyrifera and Five Native Kelp Species: Does Giant Kelp Always Lose?

The giant kelp Macrocystis pyrifera is often considered competitively dominant to other kelp species due to its high productivity. However, on the microscopic level, previous studies found that Macrocystis can be inferior to other kelp species through microscopic interspecies chemical competition. Recruitment failure can be caused by neighboring kelps because there is no species specificity in the stereochemistry of the signaling chemical used during reproduction to initiate spermatozoid release; therefore, Macrocystis spermatozoid release is pre-empted by that of its competitors. To date, this interaction has been tested between Macrocystis and only one other kelp taxon, Pterygophora. To test whether Macrocystis is always chemically outcompeted microscopically, I investigated the competitive outcome, by tracking sporophyte production, between Macrocystis and five native kelps using laboratory studies. Tests with Pterygophora californica and Ecklonia arborea showed asymmetric results indicating that Macrocystis was the inferior kelp. Studies using Alaria marginata and Egregia menziesii found symmetric results where both competing species did poorly in the presence of Macrocystis. Lastly, when Macrocystis was settled with Postelsia palmaeformis, there was no significant difference in sporophyte production between polycultures and monocultures for either species. These results indicate that the competitively superior species will vary depending on the specific species interaction

Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models

© Copyright © 2020 Elmahdy, Ali, Mohamed, Howari, Abouleish and Simonet. Mangrove forests are acting as a green lung for the coastal cities of the United Arab Emirates, providing a habitat for wildlife, storing blue carbon in sediment and protecting shoreline. Thus, the first step toward conservation and a better understanding of the ecological setting of mangroves is mapping and monitoring mangrove extent over multiple spatial scales. This study aims to develop a novel low-cost remote sensing approach for spatiotemporal mapping and monitoring mangrove forest extent in the northern part of the United Arab Emirates. The approach was developed based on random forest (RF), Kernel logistic regression (KLR), and Naive Bayes Tree machine learning algorithms which use multitemporal Landsat images. Our results of accuracy metrics include accuracy, precision, and recall, F1 score revealed that RF outperformed the KLR and NB with an F1 score of more than 0.90. Each pair of produced mangrove maps (1990–2000, 2000–2010, 2010–2019, and 1990–2019) was used to image difference algorithm to monitor mangrove extent by applying a threshold ranges from +1 to −1. Our results are of great importance to the ecological and research community. The new maps presented in this study will be a good reference and a useful source for the coastal management organization

Kelp bed variability and fish population dynamics in Kachemak Bay, Alaska

Thesis (M.S.) University of Alaska Fairbanks, 2004Understanding interactions between kelp beds and fishes is essential because anthropogenic changes and natural variability in these beds (composition, density, and distribution) may affect available habitat for fishes. In Alaska, little is known about the annual and seasonal variability of macroalgal cover in kelp beds and corresponding changes in associated fish populations. This study investigated natural variability using monthly SCUBA surveys in Kachemak Bay, Alaska from May 2002 to September 2003. Ten shallow (approximately 7m water depth) nearshore kelp beds with varying degrees of macroalgal cover were surveyed visually for fishes and kelp, and measurements of environmental variables were collected. These kelp beds had a persistent, perennial-dominated understory with sporadic, sparse populations of annual canopy kelp. Understory and canopy kelps had affinities with greater bottom structure, and annual kelp density was greatest during periods with higher temperatures. Hexagrammids, especially kelp greenlings, existed year-round in the more structurally complex beds and were typically more abundant during periods with higher temperatures, and at sites with denser annual kelp populations. Most other fishes were transient and generally present only during summer months. Monthly changes in kelp and fish communities reflected a strong seasonal component

Center for Research on Sustainable Forests 2020 Annual Report

FY20 saw exciting changes in CRSF with several new initiatives launched, while progress continues on many other ongoing efforts. In particular, FY20 saw the start of two National Science Foundation funded and CRSF-led research projects. The first is the INSPIRES project, a multi-year research collaboration between Maine, New Hampshire, and Vermont focused on harnessing Big Data to better understand and forecast the region’s forest given current as well as future uncertainties. The other effort was a successful Phase 3 reboot of the National Science Foundation Industry-University Collaborative Research Center, Center for Advanced Forestry System (CAFS), for which I have served as Director since 2016. CAFS provides direct connections among several additional universities across the United States, including North Carolina State University, Oregon State University, Purdue University, University of Georgia, University of Idaho, and University of Washington, as well as to forest industry partners. Phase 3 of CAFS will be a five-year effort and, I hope, will lead to the successful graduation of the IUCRC

Temporal Feature Selection with Symbolic Regression

Building and discovering useful features when constructing machine learning models is the central task for the machine learning practitioner. Good features are useful not only in increasing the predictive power of a model but also in illuminating the underlying drivers of a target variable. In this research we propose a novel feature learning technique in which Symbolic regression is endowed with a ``Range Terminal\u27\u27 that allows it to explore functions of the aggregate of variables over time. We test the Range Terminal on a synthetic data set and a real world data in which we predict seasonal greenness using satellite derived temperature and snow data over a portion of the Arctic. On the synthetic data set we find Symbolic regression with the Range Terminal outperforms standard Symbolic regression and Lasso regression. On the Arctic data set we find it outperforms standard Symbolic regression, fails to beat the Lasso regression, but finds useful features describing the interaction between Land Surface Temperature, Snow, and seasonal vegetative growth in the Arctic

Change Detection Process and Techniques

Land use / land cover changes studies have become very interesting over the past decades through using remote sensing because of the availability of a suite of sensors operating at various imaging scales and scope of using various techniques as well as considered the good ways for effective monitoring and accurate land use /land cover changes. This paper looks into the following aspects related to the remote sensing technology, change detection process and techniques for land cover changes, and factor affecting change detection techniques and considerations. Keywords: Remote Sensing, Land Use / Land Cover, Change Detectio

Triennial Report: 2006-2008

Triennial Report Purpose [Page] 2 The Geographic Information Science Center of Excellence [Page] 4 Three Years in Review [Page] 5 SDSU Faculty [Page] 6-11 EROS Faculty [Page] 12-16 Post-Doctoral Researchers [Page] 17-26 GSE Ph.D. program [Page] 27 Ph.D. Students [Page] 28-39 Center Scholars Program [Page] 40 Masters Students [Page] 41 Geospatial Analysts [Page] 42 Administrative Staff [Page] 43 Center Alumni [Page] 44 Research Funding [Page] 45-46 Ph.D. Student Scholarship Grants [Page] 47 Computing Resources [Page] 48 Looking Forward [Page] 49 Appendix I Faculty publications 2006-2008 [Page] 50-58 Appendix II Cool faculty research and locations [Page] 60-65 Appendix III GIScCE birthplace map [Page] 66 Appendix IV Telephone and email contact information [Page] 67-68 Appendix V How to get to the GIScCE [Page] 6

Economic Evaluation of Climate Change Impacts and Adaptation in Italy

The paper deals with the social and economic dimensions of climate change impacts and adaptation in Italy. The ultimate aim of the paper is to provide policy makers and experts with a conceptual framework, as well as methodological and operational tools for dealing with climate change impacts and adaptation from an economic perspective. In order to do so, first a conceptual and theoretical framework of the economic assessment of climate change impacts is presented and the state of the art about impact assessment studies is briefly analysed. Then, the Italian case is taken into account, by underlying the main impacts and adaptation challenges that are likely to be implied by climate change in the next decades. The analysis of the Italian case is particularly addressed through the description of the methodology and results of two case studies. The first one, dealing mainly with impact assessment, is carried out at the national level and is part of a EC funded project on Weather Impacts on Natural, Social and Economic Systems (WISE). The second one is carried out at the local level and focuses on sea level rise impacts and adaptation in a plane south of Rome. The two case studies allow to propose simple and flexible methodologies for the economic impact assessment and the economic valuation of adaptation strategies.Climate change, Economic impact assessment, Adaptation, Cost benefit analysis

Current marine pressures and mechanisms driving changes in marine habitats

Human activities and the resultant pressures they place on the marine environment have been widely demonstrated to contribute to habitat degradation, therefore, their identification and quantification is an essential step towards any meaningful restoration effort. The overall scope of MERCES Deliverable 1.2 is to review current knowledge regarding the major marine pressures placed upon marine ecosystems in EU waters and the mechanisms by which they impact habitats in order to determine potential restoration pathways. An understanding of their geographical distribution is critical for any local assessment of degradation, as well as for planning conservation and restoration actions. This information would ideally be in the form of maps, which: (a) compile single or multiple activities and pressures over broad scales, integrating and visualizing available data and allowing direct identification of aggregations as well as gaps and (b) may be overlaid with habitat maps (or any other map layer containing additional information), thus combining different data levels and producing new information to be used for example when implementing EU policies. The deliverable also documents typical example habitat case studies, the prominent impacts and consequences of activities and pressures towards the identification of possible restoration or mitigation actions. Finally the deliverable discusses pressures, assessments, marine spatial planning and blue growth potential. Activities and pressures are used in a strict sense, where marine activities are undertaken to satisfy the needs of societal drivers (e.g. aquaculture or tourism) and pressures are considered to be the mechanism through which an activity has an actual or potential effect on any part of the ecosystem (e.g. for demersal trawling activity, one pressure would be abrasion of the seabed). Habitats are addressed using a nested approach from large-scale geological features (e.g. shallow soft bottoms) to species-characterised habitats (e.g. Posidonia meadows) because of the way they are referred to in current policy documents which lack standard and precise definitions