Bayesian Nonparametric Models for Modelling Ecological Data and Stochastic Processes for Modelling Species Interactions

In this thesis, we present four manuscripts, described in the second to fifth chapter. Chapter 2 presents a Bayesian nonparametric model for capture-recapture (CR) data collected at different sites and for several years. To estimate arrival and departure patterns at the different sites and years, we build an extension of the Dirichlet process, the Hierarchical Dependent Dirichlet process, which allows us to perform density estimation jointly across different sites and in the presence of covariates. In this case, we use a year-specific covariate, and model the correlation structure of the covariate across years using a multivariate Gaussian process. In Chapter 3, we present a model for estimating entry and exit patterns, as well as the population size, using count data (CD), by employing a Polya Tree (PT) prior. In Chapter 4 we present several extensions of chapter 3. More specifically, we extend the model to CR and to ring-recovery data and develop a joint model for CR and CD. In addition, we consider the case when multiple data-sets are modelled at the same time, by defining a hierarchical extension of the PT, which we define as Hierarchical Logistic PT. Finally, we extend the model to the case of long time series, by borrowing ideas from the Optional PT. Chapter 5 presents a spatial model to estimate interactions between multiple species using CR data. The model uses a vector of interaction point process (IPP), which allows us to estimate interactions between and within species. The use of an IPP leads to an intractable ratio of normalising constants (RNC), and hence we use the Monte Carlo Metropolis Hastings algorithm to approximate the RNC with an importance sampling estimate. The supplementary material for each paper is presented in the appendix

Quantitative climate reconstructions based on fossil pollen : novel approaches to calibration, validation, and spatial data analysis

Palaeoclimatic reconstructions from fossil proxies have provided important insights into the natural variability of climate in the late Quaternary. However, major challenges remain in ensuring the robustness of these reconstructions. Multiple factors may introduce variability and biases into the palaeoclimatic estimates. For example, quantitative reconstructions use diverse modern calibration data-sets, and a wide variety of numerical calibration methods. While the choice of calibration data-set and calibration method may significantly influence the reconstructions, the comparison and analysis of these data-sets and methods have received relatively little attention. Further challenges are presented by the validation of the prepared reconstructions and the identification of climatic variables which can be robustly reconstructed from a given proxy. In this work, summer temperature reconstructions are prepared based on late-Quaternary pollen sequences from northern Finland and northern Russia, covering the Holocene and the early part of the last glacial period (Marine Isotope Stages 5d c). The major aim of this work is to validate these reconstructions and to identify sources of bias in them. Reconstructions are prepared using a number of different calibration methods and calibration sets, to analyse the between-reconstruction variability introduced by the choice of calibration method and calibration set. In addition, novel regression tree methods are used to test the ecological significance of different climatic factors, with the aim of identifying parameters which could feasibly be reconstructed. In the results, it is found that the choice of calibration method, calibration data-set, and fossil pollen sequence can all significantly affect the reconstruction. The problems in choosing calibration data are especially acute in pre-Holocene reconstructions, as it is difficult to find representative calibration data for reconstructions from non-analogue palaeoclimates which become increasingly common in the more distant past. First-order trends in the reconstructed palaeoclimates are found to be relatively robust. However, the degree of between-reconstruction variability stresses the importance of independent validation, and suggests that ensemble reconstructions using different methods and proxies should be increasingly relied on. The analysis of climatic response in northern European modern pollen samples by regression trees suggests secondary climatic determinants such as winter temperature and continentality to have major ecological influence, in addition to summer temperature which has been the most commonly reconstructed variable in palaeoclimatic studies. This suggests the potential to reconstruct the secondary parameters from fossil pollen. However, validating the robustness of secondary-parameter reconstructions remains a major challenge for future studies.Väitöstutkimuksessa selvitettiin Pohjois-Euroopan ilmaston kehitystä viimeisten noin sadantuhannen vuoden aikana. Tutkimus perustuu Euroopan puoleisen Pohjois-Venäjän tundra-alueelta ja Pohjois-Suomesta kerättyihin kasvifossiiliaineistoihin. Tutkimus edustaa paleoklimatologiaa eli tutkimusalaa, joka pyrkii selvittämään ilmastossa erittäin pitkillä aikaväleillä, tuhansien ja miljoonien vuosien aikana tapahtuneita muutoksia. Tutkimuksessa tehtiin niin sanottuja rekonstruktioita Pohjois-Euroopan ilmastosta ja kasvillisuudesta fossiiliaineistojen perusteella: tuhansien vuosien takaisten ilmastojen ja kasvillisuuden piirteitä arvioitiin tutkimalla eri-ikäisissä maaperän kerrostumissa säilyneitä fossiileita. Tässä tutkimuksessa erityishuomion kohteena olivat fossiiliset siitepölyhiukkaset. Fossiilisten siitepölyjen lajisuhteiden perusteella arvioitiin muinaisten ilmastovaiheiden lämpötiloja käyttämällä erilaisia tilastollisia menetelmiä. Yksi väitöstutkimuksen keskeisistä tuloksista koskee Euroopan puoleisen Pohjois-Venäjän tundra-alueen herkkyyttä ilmaston lämpenemiselle. Tutkimuksessa selvitettiin kasvillisuuden ja lämpötilan muutoksia tällä alueella noin 5000 vuotta sitten, nykyistä selvästi lämpimämmän ilmastovaiheen aikana. Tulosten perusteella lämpötila oli 5000 vuotta sitten noin 2,5 °C nykyistä korkeammalla. Samalla havumetsän pohjoisraja oli n. 150 km pohjoisempana, kaukana nykyisellä tundra-alueella. Myös ikiroudan määrä alueella oli tuolloin huomattavasti nykyistä pienempi. Nämä suuret ympäristömuutokset osoittavat Euraasian pohjoisen tundra-alueen olevan erittäin herkkä ilmaston muutoksille. Pohjoisen Euraasian on ennustettu lämpenevän tämän vuosisadan aikana useilla asteilla, mikä saattaa tämän tutkimuksen tulosten perusteella aiheuttaa suuria muutoksia alueen kasvillisuudessa ja ikiroudassa. Tutkimuksessa analysoitiin lisäksi erilaisia tilastollisia menetelmiä, joita käytetään paleoklimatologiassa ilmastoarvioiden tekemiseen fossiilien perusteella. Tulosten mukaan eri tilastolliset menetelmät näyttävät tuottavan jossain määrin erilaisia ilmastoarvioita. Muinaisten ilmastojen rekonstruktiot olivat kuitenkin pääpiirteiltään samanlaisia riippumatta siitä, mitä tilastollista menetelmää käytettiin. Tämä viittaa siihen, että ilmastojen keskeisimpiä piirteitä voidaan luotettavasti arvioida fossiiliaineistojen perusteella

Assessment of Factors Influencing Migratory Landbird Use of Forested Stopover Sites Along the Delmarva Peninsula During Autumn Migration

Autumn migration is a time when billions of birds move from breeding grounds in North America to wintering grounds in Central and South America, with many individuals relying on stopover habitats en route for resting and refueling purposes. These stopover sites are critical to the survival of the hundreds of species of migratory landbirds that migrate annually, and thus identifying important stopover sites is a high priority for conserving such taxa. The Delmarva Peninsula; a coastal region of Delaware, Maryland, and Virginia along the mid-Atlantic flyway; consists of forested habitats with ample food and shelter that likely serves as quality stopover sites for many species during autumn migration. Determining both extrinsic and intrinsic factors that most influence migrant use of forested stopover sites during this period is a necessary step towards providing adequate protection for vulnerable species, and one requiring a multi-scale analytical approach. I assessed the influence of variables at the regional- (i.e. proximity to the coast, location latitudinally), landscape- (i.e. proportions of surrounding land cover types), and patch-scales (i.e. habitat structure and vegetative characteristics) on migratory landbird use of forested stopover sites at 48 forested areas located across Delaware, Maryland, and Virginia during autumn migration in 2013 and 2014. Using boosted regression tree modelling techniques, I conducted analyses to determine variable influence on forested site use for 13 migratory species, as well as season-wide and early- vs. mid-season analyses using all nocturnal migratory landbird species. For season-wide analyses, autumn migration was separated into four 21-day sampling periods (period 1 = 15 Aug – 4 Sep, period 2 = 5 Sep – 15 Oct, period 3 = 26 Sep – 16 Oct, period 4 = 17 Oct – 7 Nov). Predictor variables were not consistent in influence across multiple spatial and temporal scales during the migratory season. For all season-wide analyses, including the grouped model and thirteen individual species models, time of sampling (sampling period) was the most influential predictor variable in explaining migrant density. At the regional-scale, latitude was the most consistently influential predictor variable in explaining migrant density, generally showing higher densities at sites located further north. At the landscape-scale, proportion of hardwood forest, shrubland, impervious surface, and permanent water surrounding stopover sites were all influential at predicting migrant bird density, although their degrees of influence and relationship to migrant density (positive or negative) varied greatly across models. At the patch-scale, densities of invertebrate food resources and understory vegetation were influential predictor variables across migrant models. Early in the migratory season (15 Aug – 4 Sep), proportion of surrounding land cover (low impervious surface and high shrubland and hardwood forest) and metrics associated with patch-scale habitat structure (high ground vegetation and shrub counts) were the most influential predictor variables of migrant density. Alternatively, during the middle of the migratory season (26 Sep – 16 Oct), latitude and food availability were far more influential in predicting migrant use. These results demonstrate how spatially and temporally variable migrant use of forested stopover sites can be. Using a multi-scale approach, while logistically difficult, is necessary to understand the complexity of migrant use of stopover sites

Evaluating risk for current and future Bromus tectorum invasion and large wildfires at multiple spatial scales in Colorado and Wyoming, USA

Includes bibliographical references.2015 Summer.The Western United States is experiencing rapid ecologic change. These changes are driven largely by anthropogenic factors including introduction of alien invasive species, wildfire ignition and suppression, climate change, and feedbacks between these occurrences. Average temperatures in some areas of the Western U.S. increased as much as 1.1 °C between 2000 and 2006. The advancement of spring also provides evidence for climate change in the region; earlier snowmelt and runoff has been documented in recent decades for areas of the Intermountain West. These rapid changes will certainly affect the distribution of the alien invasive B. tectorum and large wildfires in Colorado and Wyoming as well as their associated feedbacks and cascading ecosystem effects. Prompted and inspired by natural resource manager concerns, this research evaluates these ecological phenomena at three spatial scales: Rocky Mountain National Park, Colorado; a wildfire disturbance in Medicine Bow National Forest, Wyoming; and the area encompassed by these two states. The products from this research are maps that can be incorporated into decision support systems for land management and vulnerability assessments for climate change preparedness. An evaluation of the current and future suitable habitat for B. tectorum in Rocky Mountain National Park was conducted at a 90 m² spatial resolution using a MaxEnt model fit with climatic, vegetation cover, and anthropogenic covariates (i.e. distance to roads as a surrogate for propagule pressure). One of the important considerations of this research was spatial scale; 250 m² and 1 km² resolution climate surfaces cannot capture climate refugia in a small area such as Rocky Mountain National Park (1,076 km²) with high topographic heterogeneity (2,300 m to 4,345 m elevation). Based on model results, the suitable habitat for B. tectorum in the Park increases more than 150 km2 through the year 2050. Four multi-temporal and multiscale species distribution models were developed for B. tectorum in the Squirrel Creek Wildfire post-burn area of Medicine Bow National Forest using eight spectral indices derived from five months of 30 m² Landsat 8 imagery corresponding to changes in species phenology and time of field data collection. These models were improved using an iterative approach in which a threshold for abundance (i.e. ≥40% foliar cover) was established from an independent dataset, and produced highly accurate maps of current B. tectorum distribution in Squirrel Creek burn with independent AUC values of 0.95 to 0.97. The most plausible model based on field observations showed the distribution of B. tectorum has increased 30% from pre-disturbance observations in the area. This model was incorporated in a habitat suitability model for B. tectorum in the same area using topographic covariates with inclusion of propagule dispersal limitations to provide an estimate of future potential distribution. Three historic (years 1991 – 2000) environmental suitability models for large wildfires (i.e. > 400 ha) in Colorado and Wyoming were developed at a 1 km² spatial resolution and tested using an independent dataset of large wildfire occurrence in the same area from the subsequent decade (years 2001 – 2010). The historic models classified points of known fire occurrence exceptionally well using decadal climate averages corresponding to the temporal resolution of wildfire occurrence and topographic covariates. When applied to an independent dataset, the test sensitivity was 0.91 for the best model (i.e. MaxEnt). We then applied the model to future climate space for the 2020s (years 2010-2039) and 2050s (years 2040-2069) using two future climate ensembles (i.e. two representative concentration pathways; RCP 4.5 and RCP 8.5 with ensemble average projections from 15 global circulation models) to rank areas for large wildfire risk in the future

GIS predictive modelling in the Daniel Boone National Forest: settlement patterns during the intensification or horticulture.

In this study, I explore the Late Archaic and Woodland settlement patterns (3,000 BC – 1,000 AD) in the Daniel Boone National Forest, Kentucky, and surrounding region within the context of the intensification of horticulture. GIS predictive modelling via automated learning algorithms are employed to explore various environmental variables that may have influenced where and why horticultural intensification occurred. Predictive models using random forest and maximum entropy are created and compared for the Late Archaic and Woodland periods. Results show only minimal variance between the Late Archaic and Woodland settlement patterns within the study area with slope and elevation identified as the most important environmental variables. Additional specificity and categorization of the data may serve to refine the findings and reveal further variances or similarities between the Late Archaic and Woodland periods