Applying suitability distributions in a geological context

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ASSESSING THE RELATIVE INFLUENCES OF ABIOTIC AND BIOTIC FACTORS ON A SPECIES’ DISTRIBUTION USING PSEUDO-ABSENCE AND FUNCTIONAL TRAIT DATA: A CASE STUDY WITH THE AMERICAN EEL (Anguilla rostrata)

Species’ distributions are influenced by abiotic and biotic factors but direct comparison of their relative importance is difficult, particularly when working with complex, multi-species datasets. Here, we present a flexible method to compare abiotic and biotic influences at common scales. First, data representing abiotic and biotic factors are collected using a combination of geographic information system, remotely sensed, and species’ functional trait data. Next, the relative influences of each predictor variable on the occurrence of a focal species are compared. Specifically, ‘sample’ data from sites of known occurrence are compared with ‘background’ data (i.e. pseudo-absence data collected at sites where occurrence is unknown, combined with sample data). Predictor variables that may have the strongest influence on the focal species are identified as those where sample data are clearly distinct from the corresponding background distribution. To demonstrate the method, effects of hydrology, physical habitat, and co-occurring fish functional traits are assessed relative to the contemporary (1950 – 1990) distribution of the American Eel (Anguilla rostrata) in six Mid-Atlantic (USA) rivers. We find that Eel distribution has likely been influenced by the functional characteristics of co-occurring fishes and by local dam density, but not by other physical habitat or hydrologic factors

Linking landscape characteristics, streamwater acidity and brown trout (Salmo trutta) distributions in a boreal stream network

Perturbations of stream ecosystems are often mediated by the terrestrial watershed, making the understanding of linkages between watersheds and streams essential. In this thesis I explore the connections between landscape characteristics, streamwater acidity and brown trout (Salmo trutta) distributions in Krycklan, a 67 km2 boreal stream network in northern Sweden. The study focuses on hydrochemical changes during the snowmelt-driven spring flood, a period of episodic acidity which is thought to place a restraint on acid-sensitive biota such as brown trout. pH ranged from 4.5-7.0 at different stream sites during winter baseflow, and declined by 0-2 pH units during spring flood. The magnitude of the pH drop at a given site was in large part controlled by changes in acid neutralizing capacity (ANC) and in natural organic acids associated with dissolved organic carbon (DOC). pH, ANC and DOC were all correlated with landscape characteristics such as proportion of peat wetlands, and stream hydrochemical response during spring flood could be explained by altered hydrological flowpaths through the catchment. The impact of acidity on brown trout distributions within the stream network was evaluated and compared to the apparent influence of other site and catchment-scale environmental factors. In situ bioassays demonstrated a strong relationship between spring flood pH and juvenile brown trout mortality, with a toxicity threshold at pH 4.8-5.4. In field surveys brown trout were not found at any sites which had pH <5.0 during spring flood, and were rare at sites which had pH <5.5 during spring flood, suggesting limitation by acidity for some streams. However, over the whole of the Krycklan stream network brown trout were more consistently associated with alluvial sediment deposits than with high pH or low inorganic aluminum concentrations. Acidity thus apparently influences trout distributions by setting a maximum potential distribution; within that potential distribution, actual dispersal is influenced by other factors, notably presence of physical substrate suitable for feeding and spawning habitat. Fulfilling chemical thresholds is therefore necessary but not sufficient for sustaining brown trout populations. In the context of environmental monitoring or stream restoration, consideration of physical habitat together with chemical conditions is advised

On the detectability of latitudinal biodiversity gradients in deep time

The latitudinal biodiversity gradient (LBG), in which species richness increases from the poles to tropical regions, is one of the most pervasive biodiversity patterns today. However, deep-time studies suggest that the LBG has varied in the geological past, with a range of taxonomic groups characterised by flattened or even bimodal gradients. Moreover, these studies suggest that tropical peaks and poleward declines in biodiversity are restricted to intervals of the Palaeozoic, and the last 30 million years (Myr), when cool icehouse climatic regimes persisted. Yet, the reconstruction of macroecological patterns in deep time is hampered by inherent geological and anthropogenic biases. In particular, spatial sampling heterogeneity has the potential to hinder the reconstruction of LBGs due to the ubiquitous scaling of species richness with area. In this thesis, a series of case studies that attempt to quantify the impact of spatial sampling heterogeneity on the reconstruction of LBGs are presented. Earth System and ecological niche modelling are applied to test whether observed biodiversity trends are the result of spatial sampling heterogeneity, or a genuine biological signal. In addition, a novel subsampling protocol is implemented to provide sampling-standardised estimates of biodiversity. Collectively, this work suggests spatial sampling heterogeneity often prevents the recovery of genuine LBGs in deep time. Estimates of zooxanthellate coral richness over the past 250 Myr demonstrate that the modern LBG got markedly steeper during the last 20 Myr, and a unimodal-type LBG likely persisted during the Early Cretaceous, coinciding with a geologically long-lived ‘cold-snap’. These findings are supported by ecological niche modelling, which suggest a tropical increase, and temperate decline in suitable habitat area during these intervals. Overall, these studies highlight the significance of correcting for spatial sampling heterogeneity when reconstructing biodiversity patterns from the fossil record, as well as the value of inferential methods in understanding past macroecological patterns.Open Acces

Data Assimilation for a Geological Process Model Using the Ensemble Kalman Filter

We consider the problem of conditioning a geological process-based computer simulation, which produces basin models by simulating transport and deposition of sediments, to data. Emphasising uncertainty quantification, we frame this as a Bayesian inverse problem, and propose to characterize the posterior probability distribution of the geological quantities of interest by using a variant of the ensemble Kalman filter, an estimation method which linearly and sequentially conditions realisations of the system state to data. A test case involving synthetic data is used to assess the performance of the proposed estimation method, and to compare it with similar approaches. We further apply the method to a more realistic test case, involving real well data from the Colville foreland basin, North Slope, Alaska.Comment: 34 pages, 10 figures, 4 table

Great SCO2T! Rapid tool for carbon sequestration science, engineering, and economics

CO2 capture and storage (CCS) technology is likely to be widely deployed in coming decades in response to major climate and economics drivers: CCS is part of every clean energy pathway that limits global warming to 2C or less and receives significant CO2 tax credits in the United States. These drivers are likely to stimulate capture, transport, and storage of hundreds of millions or billions of tonnes of CO2 annually. A key part of the CCS puzzle will be identifying and characterizing suitable storage sites for vast amounts of CO2. We introduce a new software tool called SCO2T (Sequestration of CO2 Tool, pronounced "Scott") to rapidly characterizing saline storage reservoirs. The tool is designed to rapidly screen hundreds of thousands of reservoirs, perform sensitivity and uncertainty analyses, and link sequestration engineering (injection rates, reservoir capacities, plume dimensions) to sequestration economics (costs constructed from around 70 separate economic inputs). We describe the novel science developments supporting SCO2T including a new approach to estimating CO2 injection rates and CO2 plume dimensions as well as key advances linking sequestration engineering with economics. Next, we perform a sensitivity and uncertainty analysis of geology combinations (including formation depth, thickness, permeability, porosity, and temperature) to understand the impact on carbon sequestration. Through the sensitivity analysis we show that increasing depth and permeability both can lead to increased CO2 injection rates, increased storage potential, and reduced costs, while increasing porosity reduces costs without impacting the injection rate (CO2 is injected at a constant pressure in all cases) by increasing the reservoir capacity.Comment: CO2 capture and storage; carbon sequestration; reduced-order modeling; climate change; economic

Variation in Spatial Predictions Among Species Distribution Modeling Methods

<p>Prediction maps produced by species distribution models (SDMs) influence decision-making in resource management or designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor variables varies among model types. Our objective was to expand the evaluation of SDM performance for 45 plant species in southern California to better understand how map predictions vary among model types, and to explain what factors may affect spatial correspondence, including the selection and relative importance of different environmental variables. Four types of models were tested. Correlation among maps was highest between generalized linear models (GLMs) and generalized additive models (GAMs) and lowest between classification trees and GAMs or GLMs. Correlation between Random Forests (RFs) and GAMs was the same as between RFs and classification trees. Spatial correspondence among maps was influenced the most by model prediction accuracy (AUC) and species prevalence; map correspondence was highest when accuracy was high and prevalence was intermediate. Species functional type and the selection of climate variables also influenced map correspondence. For most (but not all) species, climate variables were more important than terrain or soil in predicting their distributions. Environmental variable selection varied according to modeling method, but the largest differences were between RFs and GLMs or GAMs. Although prediction accuracy was equal for GLMs, GAMs, and RFs, the differences in spatial predictions suggest that it may be important to evaluate the results of more than one model to estimate a range of spatial uncertainty before making planning decisions based on map outputs. This may be particularly important if models have low accuracy or if species prevalence is not intermediate.</p>

Using individual tracking data to validate the predictions of species distribution models

The authors would like to thank the College of Life Sciences of Aberdeen University and Marine Scotland Science which funded CP's PhD project. Skate tagging experiments were undertaken as part of Scottish Government project SP004. We thank Ian Burrett for help in catching the fish and the other fishermen and anglers who returned tags. We thank José Manuel Gonzalez-Irusta for extracting and making available the environmental layers used as environmental covariates in the environmental suitability modelling procedure. We also thank Jason Matthiopoulos for insightful suggestions on habitat utilization metrics as well as Stephen C.F. Palmer, and three anonymous reviewers for useful suggestions to improve the clarity and quality of the manuscript.Peer reviewedPostprintPostprintPostprintPostprintPostprin

Assessing the potential replacement of laurel forest by a novel ecosystem in the steep terrain of an Oceanic Island

Biological invasions are a major global threat to biodiversity and often affect ecosystem services negatively. They are particularly problematic on oceanic islands where there are many narrow-ranged endemic species, and the biota may be very susceptible to invasion. Quantifying and mapping invasion processes are important steps for management and control but are challenging with the limited resources typically available and particularly difficult to implement on oceanic islands with very steep terrain. Remote sensing may provide an excellent solution in circumstances where the invading species can be reliably detected from imagery. We here develop a method to map the distribution of the alien chestnut (Castanea sativa Mill.) on the island of La Palma (Canary Islands, Spain), using freely available satellite images. On La Palma, the chestnut invasion threatens the iconic laurel forest, which has survived since the Tertiary period in the favourable climatic conditions of mountainous islands in the trade wind zone. We detect chestnut presence by taking advantage of the distinctive phenology of this alien tree, which retains its deciduousness while the native vegetation is evergreen. Using both Landsat 8 and Sentinel-2 (parallel analyses), we obtained images in two seasons (chestnuts leafless and in-leaf, respectively) and performed image regression to detect pixels changing from leafless to in-leaf chestnuts. We then applied supervised classification using Random Forest to map the present-day occurrence of the chestnut. Finally, we performed species distribution modelling to map the habitat suitability for chestnut on La Palma, to estimate which areas are prone to further invasion. Our results indicate that chestnuts occupy 1.2% of the total area of natural ecosystems on La Palma, with a further 12\u201317% representing suitable habitat that is not yet occupied. This enables targeted control measures with potential to successfully manage the invasion, given the relatively long generation time of the chestnut. Our method also enables research on the spread of the species since the earliest Landsat images