Larval dispersal in a changing ocean with an emphasis on upwelling regions

Dispersal of benthic species in the sea is mediated primarily through small, vulnerable larvae that must survive minutes to months as members of the plankton community while being transported by strong, dynamic currents. As climate change alters ocean conditions, the dispersal of these larvae will be affected, with pervasive ecological and evolutionary consequences. We review the impacts of oceanic changes on larval transport, physiology, and behavior. We then discuss the implications for population connectivity and recruitment and evaluate life history strategies that will affect susceptibility to the effects of climate change on their dispersal patterns, with implications for understanding selective regimes in a future ocean. We find that physical oceanographic changes will impact dispersal by transporting larvae in different directions or inhibiting their movements while changing environmental factors, such as temperature, pH, salinity, oxygen, ultraviolet radiation, and turbidity, will affect the survival of larvae and alter their behavior. Reduced dispersal distance may make local adaptation more likely in well-connected populations with high genetic variation while reduced dispersal success will lower recruitment with implications for fishery stocks. Increased dispersal may spur adaptation by increasing genetic diversity among previously disconnected populations as well as increasing the likelihood of range expansions. We hypothesize that species with planktotrophic (feeding), calcifying, or weakly swimming larvae with specialized adult habitats will be most affected by climate change. We also propose that the adaptive value of retentive larval behaviors may decrease where transport trajectories follow changing climate envelopes and increase where transport trajectories drive larvae toward increasingly unsuitable conditions. Our holistic framework, combined with knowledge of regional ocean conditions and larval traits, can be used to produce powerful predictions of expected impacts on larval dispersal as well as the consequences for connectivity, range expansion, or recruitment. Based on our findings, we recommend that future studies take a holistic view of dispersal incorporating biological and oceanographic impacts of climate change rather than solely focusing on oceanography or physiology. Genetic and paleontological techniques can be used to examine evolutionary impacts of altered dispersal in a future ocean, while museum collections and expedition records can inform modern-day range shifts

Responses and adaptation strategies of terrestrial ecosystems to climate change

Terrestrial ecosystems are likely to be affected by climate change, as climate change-induced shift of water and heat stresses patterns will have significant impacts on species composition, habitat distribution, and ecosystem functions, and thereby weaken the terrestrial carbon (C) sink and threaten global food security and biofuel production. This thesis investigates the responses of terrestrial ecosystems to climate change and is structured in four main chapters.;The first chapter of the thesis is directed towards the impacts of snow variation on ecosystem phenology. Variations in seasonal snowfall regulate regional and global climatic systems and vegetation growth by changing energy budgets of the lower atmosphere and land surface. We investigated the effects of snow on the start of growing season (SGS) of temperate vegetation in China. Across the entire temperate region in China, the winter snow depth increased at a rate of 0.15 cm•yr-1 (p=0.07) during the period 1982-1998, and decreased at a rate of 0.36 cm•yr-1 (p=0.09) during the period 1998-2005. Correspondingly, the SGS advanced at a rate of 0.68 d•yr-1 (p\u3c0.01) during 1982 to 1998, and delayed at a rate of 2.13 d•yr-1 (p=0.07) during 1998 to 2005, against a warming trend throughout the entire study period of 1982-2005. Spring air temperature strongly regulated the SGS of both deciduous broad-leaf and coniferous forests; whilst the winter snow had a greater impact on the SGS of grassland and shrubs. Snow depth variation combined with air temperature contributed to the variability in the SGS of grassland and shrubs, as snow acted as an insulator and modulated the underground thermal conditions. Additionally, differences were seen between the impacts of winter snow depth and spring snow depth on the SGS; as snow depths increased, the effect associated went from delaying SGS to advancing SGS. The observed thresholds for these effects were snow depths of 6.8 cm (winter) and 4.0 cm (spring). The results of this study suggest that the response of the vegetation\u27s SGS to seasonal snow change may be attributed to the coupling effects of air temperature and snow depth associated with the soil thermal conditions.;The second chapter further addresses snow impacts on terrestrial ecosystem with focus on regional carbon exchange between atmosphere and biosphere. Winter snow has been suggested to regulate terrestrial carbon (C) cycling by modifying micro-climate, but the impacts of snow cover change on the annual C budget at the large scale are poorly understood. Our aim is to quantify the C balance under changing snow depth. Here, we used site-based eddy covariance flux data to investigate the relationship between snow cover depth and ecosystem respiration (Reco) during winter. We then used the Biome-BGC model to estimate the effect of reductions in winter snow cover on C balance of Northern forests in non-permafrost region. According to site observations, winter net ecosystem C exchange (NEE) ranged from 0.028-1.53 gC•m-2•day-1, accounting for 44 +/- 123% of the annual C budget. Model simulation showed that over the past 30 years, snow driven change in winter C fluxes reduced non-growing season CO2 emissions, enhancing the annual C sink of northern forests. Over the entire study area, simulated winter ecosystem respiration (Reco) significantly decreased by 0.33 gC•m-2•day -1•yr-1 in response to decreasing snow cover depth, which accounts for approximately 25% of the simulated annual C sink trend from 1982 to 2009. Soil temperature was primarily controlled by snow cover rather than by air temperature as snow served as an insulator to prevent chilling impacts. A shallow snow cover has less insulation potential, causing colder soil temperatures and potentially lower respiration rates. Both eddy covariance analysis and model-simulated results showed that both Reco and NEE were significantly and positively correlated with variation in soil temperature controlled by variation in snow depth. Overall, our results highlight that a decrease in winter snow cover restrains global warming through emitting less C to the atmosphere.;The third chapter focused on assessing drought\u27s impact on global terrestrial ecosystems. Drought can affect the structure, composition and function of terrestrial ecosystems, yet the drought impacts and post-drought recovery potential of different land cover types have not been extensively studied at a global scale. Here, we evaluated drought impacts on gross primary productivity (GPP), evapotranspiration (ET), and water use efficiency (WUE) of different global terrestrial ecosystems, as well as the drought-resilience of each ecosystem type during the period of 2000 to 2011. We found the rainfall and soil moisture during drought period were dramatically lower than these in non-drought period, while air temperatures were higher than normal during drought period with amplitudes varied by land cover types. The length of recovery days (LRD) presented an evident gradient of high (\u3e 60 days) in mid- latitude region and low (\u3c 60 days) in low (tropical area) and high (boreal area) latitude regions. As average GPP increased, the LRD showed a significantly decreasing trend among different land covers (R 2=0.53, p\u3c0.0001). Moreover, the most dramatic reduction of the drought-induced GPP was found in the mid-latitude region of north Hemisphere (48% reduction), followed by the low-latitude region of south Hemisphere (13% reduction). In contrast, a slightly enhanced GPP (10%) was showed in the tropical region under drought impact. Additionally, the highest drought-induced reduction of ET was found in the Mediterranean area, followed by Africa. The water use efficiency, however, showed a pattern of decreasing in the north Hemisphere and increasing in the south Hemisphere.;The last chapter compared the differences of performance in trading water for carbon in planted forest and natural forest, with specific focus on China. Planted forests have been widely established in China as an essential approach to improving the ecological environment and mitigating climate change. Large-scale forest planting programs, however, are rarely examined in the context of tradeoffs between carbon sequestration and water yield between planted and natural forests. We reconstructed evapotranspiration (ET) and gross primary production (GPP) data based on remote-sensing and ground observational data, and investigated the differences between natural and planted forests, in order to evaluate the suitability of tree-planting activity in different climate regions where the afforestation and reforestation programs have been extensively implemented during the past three decades in China. While the differences changed with latitude (and region), we found that, on average, planted forests consumed 5.79% (29.13mm) more water but sequestered 1.05% (-12.02 gC m-2 yr -1) less carbon than naturally generated forests, while the amplitudes of discrepancies varied with latitude. It is suggested that the most suitable lands in China for afforestation should be located in the moist south subtropical region (SSTP), followed by the mid-subtropical region (MSTP), to attain a high carbon sequestration potential while maintain a relatively low impact on regional water balance. The high hydrological impact zone, including the north subtropical region (NSTP), warm temperate region (WTEM), and temperate region (TEM) should be cautiously evaluated for future afforestation due to water yield reductions associated with plantations

Diverse sensitivity of winter crops over the growing season to climate and land surface temperature across the rainfed cropland-belt of eastern Australia

© 2017 Elsevier B.V. The rainfed cropland belt in Australia is of great importance to the world grain market but has the highest climate variability of all such regions globally. However, the spatial-temporal impacts of climate variability on crops during different crop growth stages across broadacre farming systems are largely unknown. This study aims to quantify the contributions of climate and Land Surface Temperature (LST) variations to the variability of the Enhanced Vegetation Index (EVI) by using remote sensing methods. The datasets were analyzed at an 8-day time-scale across the rainfed cropland of eastern Australia. First, we found that EVI values were more variable during the crop reproductive growth stages than at any other crop life stage within a calendar year, but nevertheless had the highest correlation with crop grain yield (t ha−1). Second, climate factors and LST during the crop reproductive growth stages showed the largest variability and followed a typical east-west gradient of rainfall and a north-south temperature gradient across the study area during the crop growing season. Last, we identified two critical 8-day periods, beginning on day of the year (DoY) 257 and 289, as the key ‘windows’ of crop growth variation that arose from the variability in climate and LST. Our results show that the sum of the variability of the climate components within these two 8-day ‘windows’ explained >88% of the variability in the EVI, with LST being the dominant factor. This study offers a fresh understanding of the spatial-temporal climate-crop relationships in rainfed cropland and can serve as an early warning system for agricultural adaptation in broadacre rainfed cropping practices in Australia and worldwide

Investigating Changes in Chlorophyll a and Other Water Chemistry Variables in Response to Global Environmental Change

Changes in water quality are influenced by climate change and other anthropogenic stressors. We 1) assessed changes in water quality in lakes in the Laurentian Great Lakes region, and 2) compiled water quality data for lakes across the world to assess global patterns in chlorophyll a. We found that at the regional scale water quality (specifically chlorophyll a, total phosphorus, total nitrogen, and dissolved organic carbon) were influenced by temperature, precipitation, morphology, and the presence of dreissenids. We also compiled chlorophyll a and water chemistry data together with morphometric characteristics for 8557 lakes worldwide. This global dataset will allow researchers to associate global water quality patterns to different pressures such as changes climate or land use

How are Interannual Variations of Land Surface Phenology in the Highland Pastures of Kyrgyzstan Modulated by Terrain, Snow Cover Seasonality, and Climate Oscillations? An Investigation Using Multi-Source Remote Sensing Data

In the semiarid, continental climates of montane Central Asia, with its constant moisture deficit and low relative humidity, agropastoralism constitutes the foundation of the rural economy. In Kyrgyzstan, an impoverished, landlocked republic in Central Asia, herders of the highlands practice vertical transhumance—the annual movement of livestock to higher elevation pastures to take advantage of seasonally available forage resources. Dependency on pasture resource availability during the short mountain growing season makes herds and herders susceptible to changing weather and climate patterns. This dissertation focuses on using remote sensing observations over the highland pastures in Kyrgyzstan to address five interrelated topics: (i) changes in snow cover and its seasonality from 2002 through 2016; (ii) pasture phenology from the perspective of land surface phenology using multi-scale data from 2001 through 2017; (iii) relationships between snow cover seasonality and subsequent land surface phenology; (iv) terrain effects on the snow-phenology interrelations; and (v) the influence of atmospheric teleconnections on modulating the relationships between snow cover seasonality, growing season duration, and pasture phenology. Results indicate that more territory has been experiencing earlier snow onset than earlier snowmelt, and around equivalent areas with longer and shorter duration of snow seasons. Significant shifts toward earlier snow onset (snowmelt) occurred in western and central (eastern) Kyrgyzstan, and significant duration of the snow season shortening (extension) across western and eastern (northern and southwestern) Kyrgyzstan. Below 3400 m, there was a general trend of significantly earlier snowmelt, and this area of earlier snowmelt was 15 times greater in eastern than western rayons. In terms of land surface phenology, there was a predominant and significant trend of increasing peak greenness, and a significant positive relationship between snow covered dates and modeled peak greenness. While there were more negative correlations between snow cover onset and peak greenness, there were more positive correlations between snowmelt timing and peak greenness, meaning that a longer snow cover season increased the amplitude of peak greenness. The amount of thermal time (measured in accumulated growing degree-days) to reach peak greenness was significantly negatively correlated both with the number of snow covered dates and the snowmelt date. Thus, more snow covered dates translated into fewer growing degree-days accumulated to reach peak greenness in the subsequent growing season. Terrain features influenced the timing of snowmelt more strongly than the number of snow covered dates. Slope was more important than aspect, but the strongest effect appeared from the interaction of aspect and the steepest slopes. The influence of atmospheric teleconnection arising from climate oscillation modes was marginal at the spatial resolutions of this study. Thermal time accumulation could be largely explained with Partial Least Squares (PLS) regression models by elevation and snow cover metrics. However, explanation of peak greenness was less susceptible to terrain and snow cover variables. This research study provides a comprehensive evaluation of the spatial variation of interannual phenology in the highland pastures that serve as the foundation of rural Kyrgyz economy. Finally, it contributes to a better understanding of recent environmental changes in remote highland Central Asia

Comparing Autumn Phenology Derived from Field Observations, Satellite Data, and Carbon Flux Measurements in a Northern Mixed Forest

In this project, autumn phenological transition dates and senescence rate (derived from field observation, satellite data and carbon flux measurements) are compared in a northern Wisconsin deciduous forest. Field data cover 2010 and 2012 for the northern site and 2010, 2012 and 2013 for the southern site, with leaf coloration and leaf fall recorded. Satellite indices are EVI and NDVI obtained from the MODIS V006 product via Google Earth Engine platform, covering 2000 to 2017. Carbon flux indices are NEE and GPP covering 1997 to 2017. Field data and normalized satellite data are fitted by a two-section logistic model while carbon data are fitted by a double-logistic model to derive three transition dates and senescence rate parameters. Comparison among these dates and parameters suggests: (a) Generally, the transition dates derived from NDVI is closest to the transitions of leaf coloration and leaf fall; (b) The senescence rate based on NDVI is also closest to the rate of leaf coloration and leaf fall; (c) In year-to-year comparisons, either NEE or GPP can be the least accurate approach in estimating leaf coloration and leaf fall progress; while in long-term comparisons, the accuracy order of EVI, NEE and GPP is variable; and (d) NDVI-based senescence rate is faster, while the senescence rate derived from the other three approaches don’t differ a lot. Speculations on the reasons for these findings are as follows: (a) canopy senescence is asynchronous, so the timing of first observed leaf coloration from above-canopy and below-canopy can be different; (b) Compared with NDVI, EVI is more sensitive to the subtle canopy change in early autumn and is less affected by soil noise in late autumn, resulting in longer senescence duration; (c) Photosynthesis starts to decrease before visual senescence due to environmental and leaf physiological change, which leads to the bias between field data and carbon data derived transition in early autumn; and (d) The life activities of shrubs and coniferous trees cause carbon exchange to continue changing after deciduous tree senescence terminates

Responses of Land Surface Phenology to Wildfire Disturbances in the Western United States Forests

Land surface phenology (LSP) characterizes the seasonal dynamics in the vegetation communities observed for a satellite pixel and it has been widely associated with global climate change. However, LSP and its long-term trend can be influenced by land disturbance events, which could greatly interrupt the LSP responses to climate change. Wildfire is one of the main disturbance agents in the western United States (US) forests, but its impacts on LSP have not been investigated yet. To gain a comprehensive understanding of the LSP responses to wildfires in the western US forests, this dissertation focused on three research objectives: (1) to perform a case study of wildfire impacts on LSP and its trend by comparing the burned and a reference area, (2) to investigate the distribution of wildfire impacts on LSP and identify control factors by analyzing all the wildfires across the western US forests, and (3) to quantify the contributions of land cover composition and other environmental factors to the spatial and interannual variations of LSP in a recently burned landscape. The results reveal that wildfires play a significant role in influencing spatial and interannual variations in LSP across the western US forests. First, the case study showed that the Hayman Fire significantly advanced the start of growing season (SOS) and caused an advancing SOS trend comparing with a delaying trend in the reference area. Second, summarizing \u3e800 wildfires found that the shifts in LSP timing were divergent depending on individual wildfire events and burn severity. Moreover, wildfires showed a stronger impact on the end of growing season (EOS) than SOS. Last, LSP trends were interrupted by wildfires with the degree of impact largely dependent on the wildfire occurrence year. Third, LSP modeling showed that land cover composition, climate, and topography co-determine the LSP variations. Specifically, land cover composition and climate dominate the LSP spatial and interannual variations, respectively. Overall, this research improves the understanding of wildfire impacts on LSP and the underlying mechanism of various factors driving LSP. This research also provides a prototype that can be extended to investigate the impacts on LSP from other disturbances

Triennial Report: 2012-2014

Triennial Report Purpose [Page] 3 Geographical Information Science Center of Excellence [Page] 5 SDSU Faculty [Page] 6 EROS Faculty [Page] 13 Research Professors [Page] 19 Postdoctoral Fellows [Page] 24 GSE Ph.D Program [Page] 36 Ph.D. Fellowships [Page] 37 Ph.D. Students [Page] 38 Recent Ph.D. Graduates [Page] 46 Masters Students [Page] 56 Previous Ph.D. Students [Page] 58 Center Scholars Program [Page] 59 Research Staff [Page] 60 Administrative and Information Technology Staff [Page] 62 Computer Resources [Page] 66 Research Funding [Page] 67 Glancing Back, Looking Forward [Page] 68 Appendix I Alumni Faculty and Staff Appendix II Cool Faculty Research and Locations Appendix III Non-Academic Fun Things To Do Appendix IV Publications 2012-2014 Appendix V Directory Appendix VI GIScCE Birthplace Map Appendix VII How To Get To The GIScC

Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects