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Aim Availability of uniformly collected presence, absence, and abundance data remains a key challenge in species distribution modeling (SDM). For invasive species, abundance and impacts are highly variable across landscapes, and quality occurrence and abundance data are critical for predicting locations at high risk for invasion and impacts, respectively. We leverage a large aquatic vegetation dataset comprising point‐level survey data that includes information on the invasive plant Myriophyllum spicatum (Eurasian watermilfoil) to: (a) develop SDMs to predict invasion and impact from environmental variables based on presence–absence, presence‐only, and abundance data, and (b) compare evaluation metrics based on functional and discrimination accuracy for presence–absence and presence‐only SDMs. Location Minnesota, USA. Methods Eurasian watermilfoil presence–absence and abundance information were gathered from 468 surveyed lakes, and 801 unsurveyed lakes were leveraged as pseudoabsences for presence‐only models. A Random Forest algorithm was used to model the distribution and abundance of Eurasian watermilfoil as a function of lake‐specific predictors, both with and without a spatial autocovariate. Occurrence‐based SDMs were evaluated using conventional discrimination accuracy metrics and functional accuracy metrics assessing correlation between predicted suitability and observed abundance. Results Water temperature degree days and maximum lake depth were two leading predictors influencing both invasion risk and abundance, but they were relatively less important for predicting abundance than other water quality measures. Road density was a strong predictor of Eurasian watermilfoil invasion risk but not abundance. Model evaluations highlighted significant differences: Presence–absence models had high functional accuracy despite low discrimination accuracy, whereas presence‐only models showed the opposite pattern. Main conclusion Complementing presence–absence data with abundance information offers a richer understanding of invasive Eurasian watermilfoil's ecological niche and enables evaluation of the model's functional accuracy. Conventional discrimination accuracy measures were misleading when models were developed using pseudoabsences. We thus caution against the overuse of presence‐only models and suggest directing more effort toward systematic monitoring programs that yield high‐quality data., Multiple niche models for invasive Eurasian watermilfoil were developed and compared to evaluate the importance of response data and model accuracy metrics. Abundance data are crucial in gaining a fuller understanding of EWM invasion and improve model evaluation metrics. Thus, the study cautions against the overuse of simple presence–absence niche models.

Urban environments frequently play an important role in the initial stages of biological invasions, often serving as gateways for non-native species, which may propagate to nearby natural and agricultural ecosystems in the event of spillover. In California, citrus trees are a dominant ornamental and food plant in urban and peri-urban environments. We studied the invasion dynamics of the Asian citrus psyllid (Diaphorina citri), which became widespread in urban areas of southern California starting in 2008, to understand the factors driving its more recent invasion in commercial citrus groves. Using a multi-year monitoring database, we applied a suite of models to evaluate the rate at which groves accrued their firstD. citridetection and the cumulative number of detections thereafter. Grove characteristics and landscape context proved to be important, with generally higher invasion rates and more cumulative detections in groves that were larger, had more edge, or had more perforated shapes, with greater urbanization intensity favoring more rapid invasion, but with inconsistent effects of distance to roads among models. Notably, distance to urban or other grove occurrences proved to be among the most important variables. During the early phase ofD. citriinvasion in the region, groves closer to urban occurrences were invaded more rapidly, whereas more recently, invasion rate depended primarily on proximity to grove occurrences. Yet, proximity to urban and grove occurrences contributed positively to cumulativeD. citridetections, suggesting a continued influx from both sources. These results suggest that inherent features of agroecosystems and spatial coupling with urban ecosystems can be important, temporally dynamic, drivers of biological invasions. Further consideration of these issues may guide the development of strategic responses toD. citri's ongoing invasion.

Successfully eradicated invasions are ideal opportunities for understanding the factors governing biological invasions and developing robust management strategies should the same, or similar, organisms again invade. We used geospatial analyses and habitat suitability modeling to reconstruct the spatiotemporal dynamics of an invasive vineyard pest, the European grapevine moth (Lobesia botrana [Denis & Schiffermuller]), in northern California. L. botrana detections were most strongly autocorrelated at local spatial scales (≤250 m) and remained clustered up to ~10 km. Generalized linear model, boosted regression tree, and random forest modeling methods performed well in predicting habitat suitability for L. botrana; annual mean temperature, elevation, and distance to the nearest road were identified as important predictors. Hotspots in L. botrana occurrence were spatiotemporally dynamic, yet habitat suitability was less important than purely spatial effects in explaining hotspot persistence. Our results indicate that local regulatory response to novel L. botrana detections was appropriate; 500 m treatment zones around detections are sufficient given the apparent propensity for very local movement by L. botrana. Our results also confirm the role of anthropogenic effects in L. botrana spread and support the establishment of quarantine procedures to limit human-mediated dispersal. Lastly, ensemble predictions provide a fine-scale measure of relative risk for a portion of northern California in the event of future L. botrana introductions.

Biological invasions are characterized by dynamic spatiotemporal distribution patterns. Yet landscape scale distribution patterns of invasive species are rarely leveraged to understand invasion in a comprehensive manner. Characterize three key stages of invasion from the landscape scale distribution patterns for an invasive insect—Asian citrus psyllid (Diaphorina citri), and investigate the potential spatial variability in establishment and impact risk. The study focuses on the recent invasion of D. citri in Southern California’s urban landscape. We used a combination of spatiotemporal pattern analyses and species distribution models (SDMs) to characterize the introduction, establishment and impact of D. citri invasion. Diaphorina citri detections in relation to major roadways revealed that initial detections are significantly closer to major roads than later detections, suggesting that transportation corridors serve as introduction pathways. SDMs of D. citri establishment and impact potential using presence-(pseudo)absence and abundance revealed percent impervious surface as the best landscape predictor. Inclusion of prior D. citri detections as a lagged spatiotemporal effect improved both model fit and performance suggesting that small-scale dispersal events do matter. Interestingly, interpolated risk maps based on the predicted occurrence and abundance highlighted substantial spatial variability in D. citri establishment and impact risk. Our study demonstrates how examining spatiotemporal patterns of an invasive species is useful in inferring key stages of the invasion, and how occurrence and abundance information can be leveraged to quantify variability in establishment and impact risk. The study thus provides a useful baseline in formulating effective landscape-level management actions.

Background Many CpGs become hyper or hypo-methylated with age. Multiple methods have been developed by Horvath et al. to estimate DNA methylation (DNAm) age including Pan-tissue, Skin & Blood, PhenoAge, and GrimAge. Pan-tissue and Skin & Blood try to estimate chronological age in the normal population whereas PhenoAge and GrimAge use surrogate markers associated with mortality to estimate biological age and its departure from chronological age. Here, we applied Horvath’s four methods to calculate and compare DNAm age in 499 subjects with type 1 diabetes (T1D) from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study using DNAm data measured by Illumina EPIC array in the whole blood. Association of the four DNAm ages with development of diabetic complications including cardiovascular diseases (CVD), nephropathy, retinopathy, and neuropathy, and their risk factors were investigated. Results Pan-tissue and GrimAge were higher whereas Skin & Blood and PhenoAge were lower than chronological age (p < 0.0001). DNAm age was not associated with the risk of CVD or retinopathy over 18–20 years after DNAm measurement. However, higher PhenoAge (β = 0.023, p = 0.007) and GrimAge (β = 0.029, p = 0.002) were associated with higher albumin excretion rate (AER), an indicator of diabetic renal disease, measured over time. GrimAge was also associated with development of both diabetic peripheral neuropathy (OR = 1.07, p = 9.24E−3) and cardiovascular autonomic neuropathy (OR = 1.06, p = 0.011). Both HbA1c (β = 0.38, p = 0.026) and T1D duration (β = 0.01, p = 0.043) were associated with higher PhenoAge. Employment (β = − 1.99, p = 0.045) and leisure time (β = − 0.81, p = 0.022) physical activity were associated with lower Pan-tissue and Skin & Blood, respectively. BMI (β = 0.09, p = 0.048) and current smoking (β = 7.13, p = 9.03E−50) were positively associated with Skin & Blood and GrimAge, respectively. Blood pressure, lipid levels, pulse rate, and alcohol consumption were not associated with DNAm age regardless of the method used. Conclusions Various methods of measuring DNAm age are sub-optimal in detecting people at higher risk of developing diabetic complications although some work better than the others.

Mercury pollution is a global environmental problem that threatens ecosystems, and negatively impacts human health and well-being. Mercury accumulation in fish within freshwater lakes is a complex process that appears to be driven by factors such as individual fish biology and water chemistry at the lake-scale, whereas, climate, and land-use/land-cover conditions within lake catchments can be influential at relatively larger scales. Nevertheless, unravelling the intricate network of pathways that govern how lake-scale and large-scale factors interact to affect mercury levels in fish remains an important scientific challenge. Using structural equation models (SEMs) and multiple long-term databases we identified direct and indirect effects of lake-scale and larger-scale factors on mercury levels in Walleye and Northern Pike - two species that are valued in inland fisheries. At the lake-level, the most parsimonious path models contained direct effects of fish weight, DOC, and pH, as well as an indirect effect of DOC on fish mercury levels via fish weight. Interestingly, lakeshed-, climate-, and full-path models that combine the effects of both lakeshed and climate revealed indirect effects of surrounding landscape conditions and latitude via DOC, pH, and fish weight but no direct effects on fish mercury levels. These results are generally consistent across species and lakes, except for some differences between stratified and non-stratified lakes. Our findings imply that understanding climate and land-use driven alterations of water chemistry and fish biology will be critical to predicting and mitigating fish mercury bioaccumulation in the future.

Bioaccumulation of mercury in sport fish is a complex process that varies in space and time. Both large-scale climatic as well as fine-scale environmental factors are drivers of these space-time variations. In this study, we avail a long-running monitoring program from Ontario, Canada to better understand spatiotemporal variations in fish mercury bioaccumulation at two distinct scales. Focusing on two common large-bodied sport fishes (Walleye and Northern Pike), the data were analyzed at fine- and broad-scales, where fine-scale implies variations in bioaccumulation at waterbody- and year-level and broad-scale captures variations across 3 latitudinal zones (~5&deg, each) and eight time periods (~5-year each). A series of linear mixed-effects models (LMEMs) were employed to capture the spatial, temporal and spatiotemporal variations in mercury bioaccumulation. Fine-scale models were overall better fit than broad-scale models suggesting environmental factors operating at the waterbody-level and annual climatic conditions matter most. Moreover, for both scales, the space time interaction explained most of the variation. The random slopes from the best-fitting broad-scale model were used to define a bioaccumulation index that captures trends within a climate change context. The broad-scale trends suggests of multiple and potentially conflicting climate-driven mechanisms. Interestingly, broad-scale temporal trends showed contrasting bioaccumulation patterns&mdash, increasing in Northern Pike and decreasing in Walleye, thus suggesting species-specific ecological differences also matter. Overall, by taking a scale-specific approach, the study highlights the overwhelming influence of fine-scale variations and their interactions on mercury bioaccumulation, while at broad-scale the mercury bioaccumulation trends are summarized within a climate change context.

88 Introduction 89 Methods 92 Results & Discussion 99 Acknowledgements 107 References 107 Tables 113 Figures 114 CHAPTER 5 GENERAL CONCLUSIONS 120

Sport fishes at the apex of aquatic food webs are indicators of mercury in the environment. However bioaccumulation of mercury in fish is a complex process that varies in space and time. Both large-scale climatic and environmental, as well as biological factors are drivers of these space-time variations. In this study, we avail a long-running monitoring program from Ontario, Canada to better understand spatiotemporal variations in fish mercury bioaccumulation. Focussing on two common large-bodied fishes (Walleye and Northern Pike), the data were first stratified by latitudinal zone (north, mid, and south) and eight temporal periods (between 1975 & 2015). A series of linear mixed-effects models (LMEMs) with latitudinal zone, time period, and their interactions as random effects were used to capture the spatial, temporal, and spatiotemporal variations in mercury bioaccumulation. The random slopes from the best-fitting LMEM were used to define bioaccumulation index and capture trends in space and time. Given the generally warming climate trend over the past 45 years, the role of growth dilution in modulating the bioaccumulation trends was also evaluated. The full model comprising of space, time and space-time interactions was the best-fit with interaction effects explaining most of the variation. Spatiotemporal trends showed overall similar patterns for both species. Growth dilution in conjunction with estimated rates of warming for different latitudinal zones failed to explain the spatiotemporal trends. Temporal trends showed contrasting bioaccumulation patterns-increasing in Northern Pike and decreasing in Walleye, suggesting temperature-driven growth dilution is more likely in latter. However, a space-for-time substitution revealed only a weak presence of growth dilution in Walleye, and it was not attributable to temperature differences. Overall, our study summarizes broad-scale variations in fish mercury and explores the role of growth dilution in shaping the observed patterns.

The distribution of invasive plants across a landscape is largely governed by disturbance invoking anthropogenic land-use practices and propagule pressure. However, spatial variability associated with anthropogenic disturbances and propagule pressure is seldom used to develop distribution models of invasive plants. This study makes use of large-scale survey data to develop a spatially explicit predictive model for the invasive wetland plant—purple loosestrife. Using loosestrife presence data and land use land cover information, we first predicted loosestrife occurrences in two types of wetland habitat, namely herbaceous wetlands and open-water edges, with a series of logistic regression models that incorporated surrounding land-use at three different neighborhood scales. The best-fitting surrounding land-use model was then combined with three different distance constraint models that simulated propagule pressure. Loosestrife occurrence as a function of surrounding land-use showed best fit at a neighborhood radius of 400 m. Predictions made from the surrounding land-use model at the 400 m scale were fairly accurate and loosestrife invasion of wetland locations were correlated with the proportion of anthropogenic land-use conditions. Inclusion of an autocovariate simulating propagule pressure improved model fit and performance significantly. Model findings suggest that spatially explicit incorporation of surrounding land-use yields an ecologically realistic projection of invasion risk wherein disturbance prone habitat edges tend to be more invasible. Combining this prediction with location specific estimates of propagule pressure further reduces uncertainty by spatially constraining areas of high invasion risk. Our approach is applicable to other invasive plants since it is based on two key drivers of plant invasion: disturbance and propagule-pressure.

Biological invasions are governed by spatial processes that tend to be distributed in non-random ways across landscapes. Characterizing the spatial and temporal heterogeneities of the introduction, establishment, and spread of non-native insect species is a key aspect of effectively managing their geographic expansion. The Asian citrus psyllid (Diaphorina citri), a vector of the bacterium associated with huanglongbing (HLB), poses a serious threat to commercial and residential citrus trees. In 2008, D. citri first began expanding northward from Mexico into parts of Southern California. Using georeferenced D. citri occurrence data from 2008-2014, we sought to better understand the extent of the geographic expansion of this invasive vector species. Our objectives were to: 1) describe the spatial and temporal distribution of D. citri in Southern California, 2) identify the locations of statistically significant D. citri hotspots, and 3) quantify the dynamics of anisotropic spread. We found clear evidence that the spatial and temporal distribution of D. citri in Southern California is non-random. Further, we identified the existence of statistically significant hotspots of D. citri occurrence and described the anisotropic dispersion across the Southern California landscape. For example, the dominant hotspot surrounding Los Angeles showed rapid and strongly asymmetric spread to the south and east. Our study demonstrates the feasibility of quantitative invasive insect risk assessment with the application of a spatial epidemiology framework.

Distribution of invasive species is the outcome of several processes that interact at different hierarchical levels. A hierarchical approach is taken here to analyze the landscape level distribution pattern of Purple loosestrife (Lythrum salicaria), an aggressive wetland invader. Using land use/land cover (LULC) data and loosestrife presence records we were able to identify and characterize the key processes that resulted in the observed large-scale distribution. Herbaceous wetlands, edges of open water sites, and developed open spaces were identified as loosestrife’s preferred LULC types. Analysis of spatial neighborhoods of these key land cover types revealed that disturbance modified open water edges and herbaceous wetlands were more likely to be invaded by loosestrife. Moreover, developed open spaces appear to hold loosestrife only if there is water rich conditions in the immediate neighborhood. Neighborhood analyses also showed that wetlands and open water edges embedded within a neighborhood matrix of grassland and agricultural environments is less likely to contain loosestrife. Finally, there is strong evidence of propagule pressure. Open water edges and wetlands invaded by loosestrife had on an average more loosestrife as neighbors than uninvaded lake edges and wetlands. Taken together, it is apparent that loosestrife’s landscape level distribution is the outcome of three nested hierarchical factors: habitat preference, the spatial neighborhood and propagule pressure. The patterns characterized suggests that occurrence of an invasive species is not merely contingent on availability of suitable habitat but is also influenced by human actions within its proximity, and is further constrained by dispersal limitation.

The montane (shola) grasslands of Western Ghats, South India are a component of a landscape mosaic including the better-studied shola forests. The conservation of the grasslands depends upon understanding their ecology. This review compiles available information and aims to evaluate the conservation potential of these grasslands. Most studies on these grasslands are descriptive in nature. There is little information regarding habitat heterogeneity, landscape configuration, natural disturbance, grazing and productivity in maintaining the grasslands. Succession is poorly understood because of the short-term nature of most studies. Human activities have historically influenced these grasslands and will continue to do so in more intensive manner. Threats to these grasslands include habitat loss, fragmentation, fires, and invasive species.

Refractive error (RE) is a complex, multifactorial disorder characterized by a mismatch between the optical power of the eye and its axial length that causes object images to be focused off the retina. The two major subtypes of RE are myopia (nearsightedness) and hyperopia (farsightedness), which represent opposite ends of the distribution of the quantitative measure of spherical refraction. We performed a fixed effects meta-analysis of genome-wide association results of myopia and hyperopia from 9 studies of European-derived populations: AREDS, KORA, FES, OGP-Talana, MESA, RSI, RSII, RSIII and ERF. One genome-wide significant region was observed for myopia, corresponding to a previously identified myopia locus on 8q12 (p = 1.25×10-8), which has been reported by Kiefer et al. as significantly associated with myopia age at onset and Verhoeven et al. as significantly associated to mean spherical-equivalent (MSE) refractive error. We observed two genome-wide significant associations with hyperopia. These regions overlapped with loci on 15q14 (minimum p value = 9.11×10-11) and 8q12 (minimum p value 1.82×10-11) previously reported for MSE and myopia age at onset. We also used an intermarker linkage- disequilibrium-based method for calculating the effective number of tests in targeted regional replication analyses. We analyzed myopia (which represents the closest phenotype in our data to the one used by Kiefer et al.) and showed replication of 10 additional loci associated with myopia previously reported by Kiefer et al. This is the first replication of these loci using myopia as the trait under analysis. "Replication-level" association was also seen between hyperopia and 12 of Kiefer et al.'s published loci. For the loci that show evidence of association to both myopia and hyperopia, the estimated effect of the risk alleles were in opposite directions for the two traits. This suggests that these loci are important contributors to variation of refractive error across the distribution.