geographic information report and need the explanation and answer to help me learn.

I need help writing a research paper in GIS and Technology. The paper needs to be spatially focused. I have attached the syllabus screenshot and an example of a paper that the professor would expect.
1 A StudentENVS 196-01 Wild Bee Habitat Suitability of San Joaquin County, California Abstract This research intends to illustrate the economic value of wild bees, a group of organisms vulnerable to the impacts of climate change, and how action at the local level, informed by spatial analysis, could aid in their conservation. Wild bee habitat suitability is gauged through 6 spatial variables: land use, plant community composition, pesticide use, aspect, soil drainability, and proximity to water, via index modeling and map algebra with ArcGIS software. 39.83 square miles is the determined area of maximum relative habitat suitability. Background/Introduction In the U.S., the benefit pollinator species provide is valued at an estimated $14.6 billion in agricultural production and of that value, “at least 20% ($3.07 billion) is provided by wild pollinators that depend on suitable land for nesting and foraging” (Morse & Calderone, 2000; Losey & Vaughan, 2006). California’s demand for pollinators continues to grow, as it contributes the most agricultural production value of any state to the economy. More than a third of the nation’s vegetables and two-thirds of the fruits and nuts are grown in California (CDFA, 2017). Pollinators species are necessary to ensure the high production yields of many agricultural commodities. For numerous farmers in California, pollinators are so essential that they have to rent bee colonies from professional beekeepers (Rucker​ et al.​, 2012). In fact, Almonds farmers in California spend more money on pollination services than anywhere else (Zee ​et al.​, 2018).
Sommer 2 The decline of wild bee population is due to a combination of factors. As Goulson ​et al. writes, “The abundance and diversity of flowers has declined; bees are chronically exposed to cocktails of agrochemicals, and they are simultaneously exposed to novel parasites accidentally spread by humans. climate change is likely to exacerbate these problems in the future” (Goulson et al.​, 2015). Global warming mitigation efforts are fundamental in the preservation of Earth’s biodiversity, including bees, especially in terms of species distribution. For example, “For vertebrates and plants, the number of species losing more than half their geographic range by 2100 is halved when warming is limited to 1.5°C, compared with projected losses at 2°C. But for insects, the number is reduced by two-thirds” (Warren ​et al​., 2018). Though mitigation of global warming is at the forefront of public concern, local efforts to conserve wild bee populations are lacking. There has however, been a recent state effort to conserve managed bees. Backed by the Almond Board of California, The ​Beewhere​ project set out use GIS mapping to bring “beekeepers and pesticide applicators together to share best practices by tracking and safeguarding hive locations across California” (CDPR ​et al., ​2019), with financial support from the California Dept. of Pesticide Regulation and Dept. of Food and Agriculture. Economically, it might seem sensible to conserve managed bees, given their importance to the almond market. There is some overlap of the variables affecting wild and managed populations. But scientific evidence suggests that managed colonies have negative effects on wild bee populations and that wild bees are more effective at pollinating certain crops (Mallinger ​et al.​, 2017). While recent federal governance has significantly harmed bee populations (CBD, 2019), just a few years ago, conservation of wild pollinators was of national concern. Following
Sommer 3 Obama’s 2014 presidential memorandum to monitor and promote pollinator species, a country wide, spatial research study on wild bee populations and habitat was conducted and published in 2015. It was the first study of its kind and scale, accounting the status and trends of wild bees, as well as their potential role in pollination services throughout the country. Koh​ et al.​ approximate wild bee abundance and corresponding statistical uncertainties, incorporating national land-cover data, extensive expert knowledge, and a spatial habitat model to identify 139 counties where low bee abundance and high pollinator crop demand coincide. Of those counties, Koh ​et al.​ identify 39 where that difference was exceptionally high. California’s central valley is one of the most significant regions highlighted in their analysis, and contains 12 of the top 39 counties found with low pollinator-high pollination demand (Koh ​et al.​, Fig. 1D, 2015). These regions could undoubtedly benefit from further spatial research of habitat restoration and species conservation efforts, such as integrating flower-rich habitat and hedgerows into farmland, minimizing pesticide use, and incorporating quarantine measures on bee movement (Goulson ​et al.​, 2015; Morandin & Kremen, 2013). In the context of ‘anti-ecological’ federal governance in 2019 and the looming impacts of global warming, the public must take action to save wild pollinator populations; particularly for bees as they the pillars supporting California’s extensive agricultural production. Study Area and Model Framework Building upon the analysis of Koh ​et al.​, this research examines San Joaquin county, CA (37.9176° N, 121.1710° W), one of the 12 counties identified as location in critical need of wild bee conservation efforts. The focus on San Joaquin (highlighted red,
Sommer 4 Figure 1)​ is additionally due to its importance as one of the nation’s leading agricultural counties in terms of gross economic value. It consists of analysis, in ArcMap, through the intersection of six primary spatial variables, shown by Koh ​et al. ​and a 2009 USDA report, ​Pollinator Biology and Habitat in California​, to be significant in the spatial distribution of wild bee species. San Joaquin county’s local bee habitat suitability is gauged, utilizing state and county data on land use, plant community composition, pesticide use, topography (aspect), soil drainability, and proximity to water. The models design follows a standard workflow for habitat suitability via index modeling the ranks of the variables. This analysis is concerned more with gross pollination benefit, rather than one particular species. This is in part, because of the uncertainty surrounding the presence of individual species in San Joaquin out of the potential 1,600 native California bee species (USDA, 2009). Framework is built upon a cross-species, inclusive species distribution factors, for wild species such as Solitary bees and Bumble Bees. Although the model’s design is largely based upon the guidelines provided by Koh ​et al.​ and the USDA’s California pollinator report, the underlying spatial variables controlling bee distribution and their importance were informed further through an in depth review of scientific literature surrounding their habitat dynamics. That is, the ranks of landscape and environmental variables were assigned following scientific consensus and journal publications on wild bees. Methods and Data Due to the prevalence county data projected to California Coordinate System NAD83 (​EPSG 2227​, feet), all other spatial data is projected to the same spatial references for accurate analysis.
Sommer 5 Soil Drainability Soil drainability is a major underlying variable affecting bee habitat suitability. The majority of solitary bee species nest in areas of well-drained (USDA, 2009). Pollination services (the focus of this analysis) are greater and more efficient in areas with greater species diversity or richness (Kremen ​et al., 2002)​. And wild bee species is richness is more abundant on dry soils than moist soils (Dauber et al., 2003), which is directly related to soil drainability. Bee species richness is associated with greater pollination benefits and efficiency. Data for soil drainability and topographic aspect was acquired from the USDA’s Soil Survey Geographic database (SSURGO) online downloader. 30 meter SSURGO data was available in downloadable map package sections, with polygons already joined to the pertantent tables. However, each map package only contained the data for 1 watershed, and given San Joaquin’s unique delta landscape, it has 6 separate watersheds. The essential files were extracted from the packages with archiver software. Using the Merge function in ArcGIS tool box, the isolated watersheds were compacted together into one raster dataset. The layer was then clipped to the San Joaquin county limit, the area of concern for this study. A drainability rank field was added to the attribute table. Ranks were assign to the classifications as: ​5​: Well Drained , ​4​: Moderately Well Drained, ​3​: Somewhat Excessively Drained; Excessively Drained; Somewhat poorly Drained, ​2​: Poorly Drained, ​1: ​Very Poorly Drained. Because the corresponding tables for the watershed data were fully inclusive with all kinds geology and topography data, aspect data was derived by exporting the aspect field, with its associated shapefiles to a separate layer.
Sommer 6 Pesticides In considering the use of agricultural pesticides and insecticides, in other words, the agrochemicals that are largely toxic to many species of wild bees, this model needed to maintain a certain degree of accuracy. It does not entail the uncertain prediction of pesticide use, like others have (Tian, 2016). Ecological toxicity of agrochemicals and their environmental impacts involves numerous factor which go beyond the scope and resources of this analysis: variables such as aerial vs ground application, application duration, amount, acreage, pesticide drift via wind, hydrology (runoff, etc.), topography, and geology. Detailed GIS analyses have examined ecological influence of agrochemicals alone, but particularly for water use (Zhang ​et al​., 2018; Faure ​et al​. 2018). Lacking quantitative data beyond the county’s pesticide use reports and agricultural parcel data, the design of this model made the following assumptions. First, farmlands registered with the county agriculture commission, that were classified as using pesticides or soil fumigants were presumed to be areas with bee-toxic type and scale of agrochemical application. This assumption was also informed by the prevalence of farms registered as using soil fumigants and neonicotinoids applied in large amounts (>5,000 lbs over 1000’s of acres) noted on the county 2018, Pesticide Use Reports (SJ Agriculture Commissioner’s Office). Many studies have shown that soil fumigants and neonicotinoids kill or adversely affect bees species (Whitehorn ​et al.​, 2012; EFSA Journal, 2012). In lieu of the blind assignment of toxic agro-chemical use likelihood for the entire county, based on proximity to an area with probable use, this model simply the involved ranking agricultural regions known to be using bee-toxic agrochemicals ‘0’. This decision is justified further by the abundance of conventional, homogenized monoculture crop fields in the county,
Sommer 7 containing minimal floral foraging resources for bees and flowering over shorter periods (Ollerton ​et al.​ 2014; Holzschuh ​et al.​ 2016). Furthermore, advancement in systemic pesticides and herbicides, efficiency of chemical application have adversely affected wild pollinator populations in rural areas (Goulson ​et al.​ 2015, Whitehorn ​et al.​ 2012; Simon-Delso ​et al.​, 2014). Pesticide use was additionally accounted for in the parks layer, derived from 30ft data from the county GIS website. The inclusion of parks in this analysis was due to the significance of evidence that urban green space, specifically, parks as likely suitable areas for bee habitat (USDA, 2011). Green space can often provide floral foraging resources and ground nesting sites for many bee species (Threllfall ​et al.​, 2015; Tonieto ​et al.​, 2011). The county parks data by default, includes golf courses and country clubs, which are areas commonly known to use bee-toxic pesticides. Skateboard, tennis, BMX, and sports complex parks were also excluded as these areas consist of concrete material landcover, lacking the floral resources bees require. Land Use & Floral Resources Scholarly literature suggests land cover to be a main spatial variable affecting wild bee distribution (Koh ​et al.​, 2017; Guisse & Miller, 2011; USDA, 2009). Pesticide use is accounted for in land use by seperately exporting the suitable (non-fumigant/pesticide) and unsuitable farm parcels. These two layers were then merged with existing USDA’s California Gap Analysis Project Data from 2011. The original agricultural land category was deleted and replaced with two separate classifications for agrochemical and non-agrochemical land. The USDA’s 30m land cover data was the primary determinant of floral and foraging resources. After the data was clipped to the county limit, there were 9 land cover classifications. With a new field in the
Sommer 8 attribute table, they are ranked as follows: Forest & Woodland: ​4​, Shrubland & Grassland: ​4​, Semi-Desert:​ 4​, Nonvasucular/Sparse & Vascular Rock Vegetation: ​3​, Non-Pesticide Agricultural: ​3, ​Developed & Other Human use: ​2​, Agrochemical Agricultural: ​0​, Recently Disturbed: ​0, ​and Open Water:​ 0​. The multiples ranks of 4 are due to the range of wild bee species this model concerns, whose floral preferences vary greatly. The rank of developed land was due to emerging findings of urban environments as a refuge for bees (Hall ​et al.​, 2017). Given more time and resources, a localized study of floral, foraging, and nesting resources would have been extremely beneficial for this spatial analysis. Such studies, like that of Winfree​ et al. (2007),​ ​classify specific bee species at a local area by their preferred plant species (floral resources) and determine the pollination services provided by individual bee species, measuring per plant pollen deposition. With Arcmap’s Union, an overlay spatial analysis tool, parks, soil drainability, and landcover are combined into one layer for the creation of an index value ranks, summing the attribute table ranks for drainability, park, and land use. The combined layer is accordingly labeled Landcover and Soil . Proximity to Water Proximity to water is a main impactor in spatial distribution and abundance of many bee species. In honey bee species, water is needed to dilute honey, maintain hive temperatures, and feed larvae (Brodschneider & Crailsheim, 2011; Nicolsen, 2009). Other research has shown water availability is crucial for bees to regulate their own body temperature (Härtel & Steffan-Dewenter, 2014)​. ​Water proximity has also been found to be a significant variable in the determination of nesting distribution sites for cavity-nesting bees (Guisse & Miller, 2011). Water
Sommer 9 resources, including canals, rivers, lakes, etc., were exported from the Gap land cover data for proximity analysis with the Euclidean distance tool. After creating the new ‘water buffer’ layer (​Figure 2​), proximity values were weighted using the Reclassify tool as follows ​5​: <300 m, ​4​: 300-500m,​ 3:​ 500-750 m,​ 2:​ 750-1000 m, ​1​: >1000m. Aspect Topographic aspect has additionally been considered of great importance in wild bee distribution. As the USDA notes, east and south-facing slopes are generally more suitable for nesting bees, while other aspects are less suitable: west-facing slopes are often due to excessive sunlight and winds, and North-facing slopes are often dominated by trees (USDA, 2009). As mentioned before, aspect data was exported from the 30m SSURGO soil data. Aspect raster data is ranked using the Reclassify tool as follows: ​5:​ South-East (90-180°), ​4: South-West (180°-270°),​ 3: ​North-East (1°-90°), ​2​: North-West (270°-360°), ​1: Flat (0°). These ranges were taken from the model framework of Tian (2016). Results / Final Spatial Habitat Analysis The last step in modeling wild bee habitat suitability is to combine all variables with map algebra using the Raster Calculator tool. The vector data: soil drainability, land use/cover, and parks, were previously combined via a union to form a Landcover & Soil layer with an index rank. For map algebra analysis with water proximity and aspect, this layer was converted to raster. The Raster Calculator Expression was: Float (“Land&Soil”+ “WaterClass” + “AspectClass”) / 23 ​.
Sommer 10 Where 23 is the highest possible value of combined ranks and reclassified values. Map Algebra created a new raster layer for bee suitability. Five range classes were created with provide results, then labeled accordingly to their suitability. The class were labeled for suitability as:
Sommer 11 Very Low​ 0.13-0.34, ​Low:​ 0.34-0.43, ​Moderate:​ 0.43-0.51, ​High​ 0.51-0.65, ​Very High​: 0.65-0.95. A corresponding color ramp scale was set in the layer symbology from light blue as very low suitability to solid blue at very high suitability. The resulting map (​Figure 3​) is shown above. Using the classification statistics window, cell count is found for each suitability class. Habitat area of maximum suitability, the very high class, was calculated via cell resolution times cell count (1233786 cells at 30ft cell size). In total, maximum habitat suitability area for San Joaquin is determined to be approximately 39.83 square miles, after subtracting the areas of open water. In total, approximately 1063.38 square miles of the county are classified as very low to moderate suitability. Of this relatively unsuitable land, there are a few options for effective placement or focus for wild bee conservation efforts, in the context of pollination benefit. After a thorough review of the scientific and spatial data research against this model, the best conservation efforts should likely be focused on creating artificial nesting sites and hedgerows around the marginal, unused edges of farmlands, as suggested by the USDA (2009). The marginal areas of non-agrochemical farmland classified here as very low to moderate suitability would presumably be a good choice for such methods. These regions may be ‘unsuitable’ in terms of levels and type for soil drainability, aspect, water proximity, floral resources, and parks. But, these variables don’t necessarily kill bees, they may just not be as suitable for foraging and nesting sites, whereas agrochemicals have been directly linked to bee death and colony collapse. As such, the marginal lands surround non-agrochemical farms found in areas of very low to moderate suitability are likely a good choices in improving overall pollination services through the aforementioned efforts, as this analysis measures those services in the context of economic
Sommer 12 benefit. These unsuitable areas are isolated with the raster calculator expression: SetNull(“suitable” >0.51, 1)​ and a subsequent conversion to shapefile. The non-pesticide farms layer could then be clipped to unsuitable areas to identify locations in need of conservation (​Figure 4​). Again, conservation efforts should be focused on these farms, specifically the permiter or marignal land surrounding them. Discussion Of San Joaquin’s total 1426 square miles, this analysis found only 39.83 square miles of very high habitat suitability for wild bee species. Those regions of very high suitability are concentrated in close proximity to water sources, well-drained soils, and less agriculturally intensive area. This is due to the high ranks and reclassifications, which drive the suitability analysis. These findings can potentially inform the locational need of habitat conservation efforts such as flower box planting, artificial nesting sites, minimizing pesticide use, hedgerow planting. Much of the county is identified to have low habitat suitability, especially areas with high pesticide use and urban centers. Similar site suitability and pollination service analyses have been conducted for both wild bee habitat and managed beekeeping operations. Beichen Tian, of the University of Wisconsin, modeled a similar pollinator suitability evaluation, along with a neighborhood urban planning design for pollinator habitat (Tian, 2016). While the subject matter and variables weighted are certainly comparably, Tian’s project is ultimately of a smaller locational scale and
Sommer 13 based on location-dependent factors, which differ greatly from those of California’s central valley, particularly when considering the differences in land use, pesticide application, and population. A more regionally comparable study, was of Yolo county, coincidentally one of the 12 identified by​ Koh et al.​ (Lonsdorf ​et al.​, 2009). However, their research entailed 5 separate models for each factor: floral resources, land cover, pollinator service, abundance, and nest suitability, whereas this analysis combined such variables to form a single index model. Limitations Though this analysis could be quite useful in the aid of wild bee-conservation efforts, there were a number of limitations. First and foremost, the lack of species-specific data for the county. Additional research on local and seasonal floral resources would also greatly increase the relevance of this model. A detailed, more accurate analysis of local, toxic pesticide variables is recommended for further determination of bee habitat suitability. A model is only as good as the data it contains, and this analysis lacked data that was likely pertinent. The framework presented here can be used as a foundation to aid in further suitability analyses of individual wild bee species. Works Cited Spatial Data: SSURGO soils and aspect 3e07fff USDA California Gap Analysis Project Data (2011), from the P drive on the CISR lab computers San Joaquin County: County Boundary, Parks: Agricultural Parcels: ​
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