We were able to detect these patterns even though the resolution of the remote sensing data was limited to 250 m. As remote sensing data technology advances, we will be able to further explore these patterns. There are clearly different patterns of bee abundance between land use types over the course of the year. We detected differences in land surface phenology between land use types through remote sensing, although the relationship between remote sensing vegetation indices and the bee community is complex due to the role of seasonality and major differences in vegetation between land use types. The strongest relationship between vegetation indices and bee abundances were in natural sites, but sites classified as natural also exhibited high colinearity between seasonality and vegetation indices, making it difficult to tease apart the differences. The overlapping points between seasons in the urban and agricultural areas mean that vegetation indices are less tied to seasonality. Despite this lack of colinearity with seasonality in human-altered landscapes, there were still significant patterns between vegetation indices and bee abundance, square pot indicating the potential to use remote sensing to detect certain aspects of biodiversity. Further exploration into the relative contributions of different types of vegetation within each land use type and between time of year would contribute to the strength of applying these findings more broadly.
In addition,building on the idea of “scaling up from urban gardens”, it would be valuable to see if patterns change when the larger surrounding area of each location is investigated. No significant relationship was detected between vegetation indices and species richness as a general indicator, although focusing on certain species or functional groups may highlight which are more vulnerable to changes in phenological patterns. In this study, bee collections were made approximately every two months, yet certain species peaked in abundance at different times between land use types. This lack of synchrony in peak abundance between land use types could be the result of two possibilities: either bees are moving between land use types in search of better resources, or localized population structuring is occurring between different land use types based on differences in timing of emergences. Perhaps on a finer temporal scale of collections, for example, on the same frequency as MODIS composite products , these subtleties in timing between land use types could be better captured. Additionally, the role of time lags should be further explored by investigating floral resource availability. We found better fitting models with increasing time lags, although this may still be tied to the strong effect of seasonality in natural areas. However, several other studies have shown the importance of time lags between changes and ecological responses. In our case, this is likely the result of challenges in using vegetation indices as proxies for floral availability. Sometimes extreme floral abundance can lead to an underestimation of vegetation biomass. Perhaps our time delay is the result of plants experiencing green up through leaf growth before producing floral reproductive structures. A better understanding of species associations with vegetation indices would further improve predictive power of utilizing remote sensing data to predict species distributions.
These findings have implications for how we think about human-altered landscapes and restoration. A great amount of interest and resources go into creating “green spaces” and restoring patches of land within urban and agricultural matrices. However, most restoration practices are based on studies in more natural land use contexts. Due to the much higher degree of patchiness in human-altered landscapes and changes in phenological patterns, restoration goals and strategies may need to be altered when working in human-altered landscape contexts. Additionally, this study highlights the importance of temporal differences in human-altered landscapes. Understanding how human-altered landscapes impact species distributions and interactions is critical as land use change accelerates globally. In order to overcome previous limitations when using remote sensing to estimate biodiversity, it is first important to further understand the dynamics of vegetation type, phenology, and the ecology of interacting taxa. Our results clearly indicate that the phenology of vegetation in different land use types are not synchronized, and vegetation indices created through remote sensing can predict bee community abundance. Such findings suggest the potential to use remote sensing to estimate other taxa beyond bees to estimate biodiversity, as well as provide a new way of understanding the ecological challenges of urbanization and agriculture due to phenological differences.Human-altered landscapes are expanding globally and are often associated with declining natural habitat, non-native species, fragmentation, and transformations in structure, inputs, climate, and connectivity. These changes collectively have resulted in shifts in both spatial distributions and species diversity across many taxa including birds, mammals, reptiles, amphibians, invertebrates, and plants. One common driver of global change is urbanization, which in the extreme is associated with a reduction in biodiversity compared to habitats in their more natural state.
However, in moderately urbanized areas, the effects of urban impacts on species distribution and diversity can vary greatly and depends on region, type of change, and taxonomic group, among other factors. Documenting the effects of urbanization compared to natural communities has proven problematic, making predictions of community change associated with urbanization difficult. Human-altered landscapes are often associated with many non-native species which add to species diversity but also can obscure changes in community dynamics. Thus, to assess accurately the complex impacts of land use change on ecological communities, one must look beyond species richness to investigate ecological processes themselves. Ecological processes are the links between organisms in a functioning ecosystem, and are critical in understanding how altered biodiversity can lead to changes in ecosystem functioning. Global environmental change has been found to have a wide variety of impacts on ecological processes in different systems. Pollinator-plant relationships in particular are found to be particularly vulnerable to land use change, resulting in decreases in interaction strength and frequency. Pollination services are crucial ecosystem processes in natural systems, but also in agricultural and urban areas. Bees provide the majority of animal mediated pollination services on which it is estimated 87.5% of flowering plants depend. The value of pollination in agriculture is estimated at $200 billion worldwide, largely due to many foods that are essential for food security and a healthy human diet, including numerous fruits, vegetables, and nuts that require bee pollination. As urban areas expand, there has been increasing interest in urban agriculture to ensure food security and access to healthy foods for growing populations, and these systems also depend on pollination. For example, Kollin estimated that the economic value of urban fruit trees to be worth $10 million annually in San Jose, California. Despite the important role of pollinators and concerns about bee declines, there remain many uncertainties regarding the impact of land use change on pollinators. Urbanization has resulted in more interfaces with both natural and agricultural landscapes, creating new transitional zones of peri-urbanization. While there has been extensive pollinator research in agricultural and natural systems, less attention has focused on pollination in neighboring urban areas and how the changing landscape has impacted pollination. In addition, square plastic planter very few studies of urban areas have looked beyond changes in bee diversity to understand explicitly the effect of urbanization on pollinator-plant interactions. Here, we investigate the effect of land use change on pollinator-plant ecosystem processes. We make use of a “natural experimental design” in which urban, agricultural, and natural areas intersect. Bees visit flowers for both pollen and nectar resources, and floral visitation is a commonly used as an index of pollination services. However, depending on the flower, certain bee groups are much more effective pollinators than others.
Thus,while visitation is important, it alone does not definitively indicate whether pollination services were received by the plant. When pollen is limited by other factors, consequences for plant fitness can include failure to set seed, production of smaller fruits, and even complete lack of reproduction. By looking at rates of bee visitation and comparing this with other measures of plant fitness, such as seed set, we can develop a more complete understanding of how shifts in bee distributions between areas that differ in land use are impacting pollination services. To study the impact of changing land use on pollinator-plant interactions, we focus on bee pollination of a widespread plant, yellow starthistle , a common weed found in natural, agricultural, and urban habitats. Using standardized observations of floral visitation and seed set measurements of yellow starthistle, we test the hypotheses that increasing urbanization decreases 1) rates of bee visitation, 2) viable seed set, and 3) the efficiency of pollination . In addition to contributing to a better understanding of how change in landscape use, particularly urbanization, affects pollination-plant interactions, the study illustrates the importance of use of neighboring lands for pollination services.Our study system was located around Brentwood, in east Contra Costa County, California, where natural, agricultural, and urban areas intersect with each other within a 20 x 20 km region . A county water district , regional park district , and California state park all fall within the region, leaving large areas of land protected from development. This protected land consists mainly of grasslands and oak woodlands, some portions of which are managed for grazing. The agricultural areas of Brentwood, Knightsen, and Byron mostly consist of orchards , corn, alfalfa, and tomatoes. A housing boom in the 1990s led to massive residential growth in the area. The city of Brentwood has grown from less than 2500 people in the 1970s to over 50,000 today , and nearby Antioch has now over 100,000 residents . We selected 12 sites dominated by yellow star thistle in a stratified design to span the different land use types . Yellow starthistle is a common weedy plant that forms homogenous flowering patches in grassy areas throughout this region. Many different bee taxa in a range of functional groups and size classes have been observed to visit yellow starthistle, in part because it flowers late in the season relative to other floral resources. Despite being considered a serious introduced weed, yellow starthistle is unusual as an invasive species in that it depends on animal pollinator visits in order to set seed.Within each site we selected a 50 m x 50 m plot such that each plot was at least 2 km away from all others, a distance larger than the maximum assumed typical bee foraging ranges. Although certain bee species have been recorded foraging as far as 1400 m, most bees in this type of habitat have nesting and foraging habitat within a few hundred meters of each other. Within each plot we estimated number of flowering yellow starthistle blooms by randomly placing 10, 1 m x 1 m quadrats and counting the number of flowering blooms in each. We also measured the spatial area of yellow starthistle patches within each 50 m x 50 m plot to obtain an estimate of total flowering blooms within each plot. We categorized total blooms/plot on a log scale: <103 , 103 -104 , and >104 . Using NOAA’s 2006 Pacific Coast Land Cover dataset , a 500 m buffer was created around each plot, and the number of pixels classified as agricultural, urban, natural, water, or bare land was extracted. Because each site had an AM, Mid-Day, and PM observation event, there were a total of 36 observation events, each with unique wind and temperature recordings, and visit observations of the 15 bee morphotypes. From these, we calculated the total number of bee visitors, total number of bee morphotypes, Shannon diversity of morphotypes, and morphotype evenness. Shannon diversity and evenness were calculated using the R package vegan. The spatial autocorrelation of all bee visitor response variables was assessed by Mantel tests in R package ade4, using the average values for each time of day at each site. Spatial autocorrelation was not detected . To test for the effect of land use type on each of the response variables we used a generalized linear mixed model using the R package lme4. We designated land use type, bloom category of flowering patch, observation time period, wind, and temperature as fixed effects and site as a random effect. Natural land use and AM observation time period were the model baselines for the categorical variables of land use type and observation time.