Bayesiläisiä piilomuuttujiin perustuvia lähestymistapoja ekologisten lajiyhteisöjen mallinnukseen

In the last decades, the aims of research in community ecology have been shifting from the mere description of observed patterns towards a mechanistic perspective that seeks to understand the processes shaping observed species communities. Simultaneously, the technical advances in data collection techniques dramatically raised the amount and quality of ecological data annually obtained and provided opportunities to address more comprehensive research questions. The combination of these novel aims and data increased the interest in the statistical ecology, seeking analytical methods capable to harness the full potential of the emerging data. A special interest has been focused on the development of approaches capable to combine multiple types of existing data and jointly model the dynamics and distributions of entire species communities or ecosystems. This doctoral thesis contributes to the ongoing methodological development of analytical tools for the joint species modeling. In the presented research I combine both perspectives of the statistical ecology: the ecologist’s practical point of view and the statistician’s methodological/theoretical vision. The thesis consists of four Chapters that are arranged to form a coherent narrative. I start with a synthesis of the recent advances in joint species modeling and propose a unifying statistical framework that enables scientists to easily address many common questions in community ecology simultaneously. This framework, called Hierarchical Model of Species Communities (HMSC), is capable to incorporate information on species occurrences, environmental covariates, species traits and phylogenic relationships, as well as the structure of study design. Next, I devise and present two important extensions to this framework. The first extension enables HMSC to neatly assess the variation in species associations and relate it to environmental factors. My second extension aims to achieve better numerical properties for the HMSC-based analysis of numerous spatial observations. I carry out a set of simulated data experiments to assess the performances of the proposed extensions in comparison to existing methods. To demonstrate how the proposed methods can be used in practice, I accompany these methodological developments with real-data examples and additionally present one detailed applied ecological study. My results demonstrate that the unifying HMSC framework can be robustly used to address a wide set of fundamental and applied ecological questions for various natural systems and contexts. Conducted simulation experiments verify that the proposed extensions considerably expand the framework’s potential. The developed software implementation of the HMSC and detailed user manual provide a practical guidance for ecologists on how to apply this framework for analysis of their own data on species communities. Although this thesis is a completed research item, it should be seen as a solid foundation for further developments in the field of joint species modeling. Some of these potential developments are related to how more comprehensive ecological questions could be answered with statistical models, while other correspond to the numerical challenges posed by emerging types and amounts of ecological data. I believe that advances and results of my study will enable future research to tackle these challenges and that the joint species modeling framework will become generally applicable and insightful for a wide array of real-world problems.Viimeisten vuosikymmenien aikana yhteisöekologinen tutkimus on siirtynyt empiirisestä eliöyhteisöjen tutkimuksesta mekanistisempaan tutkimukseen, joka hakee vastauksia eliöyhteisöjä muovaaviin prosesseihin. Samaan aikaan uudet menetelmät aineiston keruuseen ovat dramaattisesti kasvattaneet ekologisten aineistojen määrää ja laatua. Tämä tarjoaa uusia mahdollisuuksia entistä kokonaisvaltaisempien tutkimuskysymysten asetteluun. Kasvava aineistojen määrä on luonut tarpeen kehittää sellaisia uusia analyyttisiä menetelmiä tilastolliseen ekologiaan, joiden avulla voidaan hyödyntää kasvavan aineistomäärän potentiaali. Erityisen kiinnostuksen kohteena ovat olleet sellaisten tilastollisten menetelmien kehitys, joiden avulla on mahdollista yhdistää erilaisia aineistoja sekä mallintaa yhtäaikaisesti lajien, lajiyhteisöjen ja ekosysteemien dynamiikkaa ja levinneisyyttä. Väitöskirjassani olen keskittynyt yhteisöekologisessa mallinnuksessa tarvittavien analyyttisten työkalujen metodologiseen kehitykseen. Tutkimuksessani olen yhdistänyt tilastollisen ekologian erilaisia näkökulmia: ekologin maastohavaintoihin perustuvaa näkemystä sekä tilastotieteilijän metodologista näkemystä. Väitöskirjani koostuu neljästä luvusta, jotka yhdessä muodostavat yhtenäisen kokonaisuuden. Aloitan synteesillä yhteisöekologisen mallintamisen viimeaikaisista kehityksestä ja esittelen yhtenäisen tilastotieteellisen kehyksen, jonka avulla tutkijoiden on helppo tutkia yleisiä yhteisöekologia kysymyksiä samanaikaisesti. Tämän kehyksen avulla, jota tässä kutsutaan yhteisöekologiseksi hierakiseksi malliksi (HMSC), on mahdollista sisällyttää tietoa lajien esiintymisestä, ympäristömuuttujista, lajien ominaisuuksista, fylogeniasta, sekä tutkimusmallien rakenteista. Seuraavaksi esitän kaksi tärkeää laajennusta HMSC kehykseen. Ensimmäinen laajennus mahdollistaa systemaattisen tarkastelun siitä miten lajien yhteisesiintyminen riippuu ympäristötekijöistä. Toinen laajennus mahdollistaa HMSC kehyksen soveltamisen erittäin laajoihin spatiaalisiin aineistoihin. Tutkin näiden laajennusten etuja ja haittoja soveltamalla sekä niitä että vaihtoehtoisia menetelmiä simuloituihin aineistoihin. Lisäksi havainnollistan molempia menetelmällisten laajennusten käytännön soveltuvuutta oikeiden ekologisten aineistojen avulla, ja esitän yhden soveltaviin ekologisiin kysymyksiin keskittyvän tapaustutkimuksen. Väitöskirjatutkimukseni tulokset osoittavat, että yhtenäisen HMSC kehyksen avulla voidaan vastata laajaan kirjoon perustavanlaatuisiin ja soveltaviin kysymyksiin siitä miten eliöyhteisöt ovat rakentuneet. Suoritetut simulaatiokokeet osoittavat että ehdottamani laajennukset HMSC- malliin tuovat entistä tehokkaampia työkaluja yhteisöekologiseen tutkimukseen. Väitöskirjatyössäni julkaistut ohjelmistot ja käyttöoppaat helpottavat HMSC kehyksen käyttöönottoa yhteisöekologian tutkimuksessa. Ennen kaikkea väitöskirjani tulisi nähdä vakaana perustana jonka päälle on mahdollista kehittää entistä syvällisempää yhteisöekologista mallinnusta. Osa mahdollisista tulevaisuuden kehittämisen kohteista liittyy siihen miten erilaisilla mallinrakenteilla ja mallien estimointiin liittyvillä tekniikoilla voidaan vastata entistä kokonaisvaltaisemmin yhteisöekologisiin kysymyksiin, ja osa niihin numeerisiin haasteisiin joita uudentyyppisten ja entistä suurempien aineistojen käsittely tuo mukanaan. Uskon, että väitöskirjatyöni tulokset luovat hyvän lähtökohdan uusille menetelmille joilla näihin haasteisiin voidaan vastata

Tree diversity patterns along the latitudinal gradient in the northwestern Russia

Background One of the key forest characteristics is the biodiversity, particularly the diversity of trees which are forest ecosystem engineers. Nowadays the most worldwide common approach for assessment of forest conditions and dynamics is based on the systematic monitoring, performed at a set of regularly structured plots. To fulfill the existing gap in this sort of knowledge on the Russian forests, an extensive study of tree species diversity on a regular network was conducted in north-west of Russia. Methods The study used the ICP Forests monitoring network that spans over 1700 km along the western Russian border from forest-tundra in the north to broadleaved-coniferous forests in the south. Tree data were collected at 710 sites that were assigned along a regular grid. We performed series of statistical analyses of the tree species distribution and diversity in relation to environmental and anthropogenic factors. Results According to the Maxent species distribution modelling results only Pinus sylvestris, Betula sp. and Picea abies have the potential to grow throughout the study area. The locally maximum tree species diversity varies along the latitudinal gradient from 1 to 3 species in the north to 5–7 species in the south. Monocultural stands are relatively abundant across the study area, being especially common in the south taiga. The prevailing part of the monocultural stands is represented by Scots pine (72%). The age distribution of dominant trees has a clear connection with the intensity of forest use. We found that recent wildfire events had only little effect on tree diversity in the study area. Conclusions We demonstrated that ICP Forests monitoring network enables to successfully establish the main qualitative and quantitative relations of the spatial variation of tree species diversity to climatic, landscape, soil and anthropogenic factors. Analysis of the influence of these factors on tree species distribution allowed us to conclude that with the continuing trend of reducing the frequency and intensity of fires, Norway spruce will further replace Scots pine and Betula sp. in the north-western Russia. Extending the monitoring network, especially adding the time-series context, could provide novel appealing opportunities for forest dynamics projection and sustainable management.Peer reviewe

How to make more out of community data? A conceptual framework and its implementation as models and software

Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. While non-manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data-driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species-to-species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and implement it as R-and Matlab-packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time-series data. We illustrate the use of this framework through a series of diverse ecological examples.Peer reviewe

Joint species distribution modelling with the r-package Hmsc

Joint Species Distribution Modelling (JSDM) is becoming an increasingly popular statistical method for analysing data in community ecology. Hierarchical Modelling of Species Communities (HMSC) is a general and flexible framework for fitting JSDMs. HMSC allows the integration of community ecology data with data on environmental covariates, species traits, phylogenetic relationships and the spatio-temporal context of the study, providing predictive insights into community assembly processes from non-manipulative observational data of species communities. The full range of functionality of HMSC has remained restricted to Matlab users only. To make HMSC accessible to the wider community of ecologists, we introduce Hmsc 3.0, a user-friendly r implementation. We illustrate the use of the package by applying Hmsc 3.0 to a range of case studies on real and simulated data. The real data consist of bird counts in a spatio-temporally structured dataset, environmental covariates, species traits and phylogenetic relationships. Vignettes on simulated data involve single-species models, models of small communities, models of large species communities and models for large spatial data. We demonstrate the estimation of species responses to environmental covariates and how these depend on species traits, as well as the estimation of residual species associations. We demonstrate how to construct and fit models with different types of random effects, how to examine MCMC convergence, how to examine the explanatory and predictive powers of the models, how to assess parameter estimates and how to make predictions. We further demonstrate how Hmsc 3.0 can be applied to normally distributed data, count data and presence-absence data. The package, along with the extended vignettes, makes JSDM fitting and post-processing easily accessible to ecologists familiar with r.Peer reviewe

The microbiome of the Melitaea cinxia butterfly shows marked variation but is only little explained by the traits of the butterfly or its host plant

Understanding of the ecological factors that shape intraspecific variation of insect microbiota in natural populations is relatively poor. In Lepidopteran caterpillars, microbiota is assumed to be mainly composed of transient bacterial symbionts acquired from the host plant. We sampled Glanville fritillary (Melitaea cinxia) caterpillars from natural populations to describe their gut microbiome and to identify potential ecological factors that determine its structure. Our results demonstrate high variability of microbiota composition even among caterpillars that shared the same host plant individual and most likely the same genetic background. We observed that the caterpillars harboured microbial classes that varied among individuals and alternated between two distinct communities (one composed of mainly Enterobacteriaceae and another with more variable microbiota community). Even though the general structure of the microbiota was not attributed to the measured ecological factors, we found that phylogenetically similar microbiota showed corresponding responses to the sex and the parasitoid infection of the caterpillar and to those of the host plant's microbial and chemical composition. Our results indicate high among-individual variability in the microbiota of the M. cinxia caterpillar and contradict previous findings that the host plant is the major driver of the microbiota communities of insect herbivores.Peer reviewe

Joint species distribution modelling with the r-package Hmsc

Joint Species Distribution Modelling (JSDM) is becoming an increasingly popular statistical method for analysing data in community ecology. Hierarchical Modelling of Species Communities (HMSC) is a general and flexible framework for fitting JSDMs. HMSC allows the integration of community ecology data with data on environmental covariates, species traits, phylogenetic relationships and the spatio-temporal context of the study, providing predictive insights into community assembly processes from non-manipulative observational data of species communities. The full range of functionality of HMSC has remained restricted to Matlab users only. To make HMSC accessible to the wider community of ecologists, we introduce Hmsc 3.0, a user-friendly r implementation. We illustrate the use of the package by applying Hmsc 3.0 to a range of case studies on real and simulated data. The real data consist of bird counts in a spatio-temporally structured dataset, environmental covariates, species traits and phylogenetic relationships. Vignettes on simulated data involve single-species models, models of small communities, models of large species communities and models for large spatial data. We demonstrate the estimation of species responses to environmental covariates and how these depend on species traits, as well as the estimation of residual species associations. We demonstrate how to construct and fit models with different types of random effects, how to examine MCMC convergence, how to examine the explanatory and predictive powers of the models, how to assess parameter estimates and how to make predictions. We further demonstrate how Hmsc 3.0 can be applied to normally distributed data, count data and presence-absence data. The package, along with the extended vignettes, makes JSDM fitting and post-processing easily accessible to ecologists familiar with r.Peer reviewe

Higher host-plant specialization of root-associated endophytes than mycorrhizal fungi along an arctic elevational gradient

How community-level specialization differs among groups of organisms, and changes along environmental gradients, is fundamental to understanding the mechanisms influencing ecological communities. In this paper, we investigate the specialization of root-associated fungi for plant species, asking whether the level of specialization varies with elevation. For this, we applied DNA barcoding based on the ITS region to root samples of five plant species equivalently sampled along an elevational gradient at a high arctic site. To assess whether the level of specialization changed with elevation and whether the observed patterns varied between mycorrhizal and endophytic fungi, we applied a joint species distribution modeling approach. Our results show that host plant specialization is not environmentally constrained in arctic root-associated fungal communities, since there was no evidence for changing specialization with elevation, even if the composition of root-associated fungal communities changed substantially. However, the level of specialization for particular plant species differed among fungal groups, root-associated endophytic fungal communities being highly specialized on particular host species, and mycorrhizal fungi showing almost no signs of specialization. Our results suggest that plant identity affects associated mycorrhizal and endophytic fungi differently, highlighting the need of considering both endophytic and mycorrhizal fungi when studying specialization in root-associated fungal communities.Peer reviewe

Higher host-plant specialization of root-associated endophytes than mycorrhizal fungi along an arctic elevational gradient

How community-level specialization differs among groups of organisms, and changes along environmental gradients, is fundamental to understanding the mechanisms influencing ecological communities. In this paper, we investigate the specialization of root-associated fungi for plant species, asking whether the level of specialization varies with elevation. For this, we applied DNA barcoding based on the ITS region to root samples of five plant species equivalently sampled along an elevational gradient at a high arctic site. To assess whether the level of specialization changed with elevation and whether the observed patterns varied between mycorrhizal and endophytic fungi, we applied a joint species distribution modeling approach. Our results show that host plant specialization is not environmentally constrained in arctic root-associated fungal communities, since there was no evidence for changing specialization with elevation, even if the composition of root-associated fungal communities changed substantially. However, the level of specialization for particular plant species differed among fungal groups, root-associated endophytic fungal communities being highly specialized on particular host species, and mycorrhizal fungi showing almost no signs of specialization. Our results suggest that plant identity affects associated mycorrhizal and endophytic fungi differently, highlighting the need of considering both endophytic and mycorrhizal fungi when studying specialization in root-associated fungal communities.Peer reviewe