Biotic interactions in driving biodiversity : Insights into spatial modelling

The effects of co-occurring species, namely biotic interactions, govern performance and assemblages of species along with abiotic factors. They can emerge as positive or negative, with the outcome and magnitude of their impact depending on species and environmental conditions. However, no general conception of the role of biotic interactions in functioning of ecosystems exists. Implementing correlative spatial modelling approaches, combined with extensive data on species and environmental factors, would complement the understanding of biotic interactions and biodiversity. Moreover, the modelling frameworks themselves, conventionally based on abiotic predictors only, could benefit from incorporating biotic interactions and their context-dependency. In this thesis, I study the influence of biotic interactions in ecosystems and examine whether their effects vary among species and environmental gradients (sensu stress gradient hypothesis = SGH), and consequently, across landscapes. Species traits are hypothesized to govern the species-specific outcomes, while the SGH postulates that the frequency of positive interactions is higher under harsh environmental conditions, whereas negative interactions dominate at benign and productive sites. The study applies correlative spatial models utilizing both regression models and machine-learning methods, and fine-scale (1 m2) data on vascular plant, bryophyte and lichen communities from Northern Finland and Norway (69°N, 21°E). In addition to conventional distribution models of individual species (SDM), also species richness, traits and fitness are modelled to capture the community-level impacts of biotic interactions. The underlying methodology is to incorporate biotic predictors into the abiotic-only models and to examine the impacts of biotic interactions and their dependency on species traits and environmental conditions. Cover values of the dominant species of the study area are used as proxies for the intensity of their impact on other species. The results show, firstly, that plant plant interactions consistently and significantly affect species performance and richness patterns. Secondly, the results make evident that the impacts of biotic interactions vary between species, and, more importantly, that the guild, geographic range and traits of species can indicate the outcome and magnitude of the impact. For instance, vascular plant species, particularly competitive ones, respond mainly negatively to the dominant species, whereas lichens tend to show more positive responses. Thirdly, as proposed, the manifestation of biotic interactions also varies across environmental gradients. Support for the SGH is found as the effect of the dominant species is more negative under ameliorate conditions for most species and guilds. Finally, simulations of species richness, where the cover of the dominant species is modified, demonstrate that the biotic interactions exhibit a strong control over landscape-level biodiversity patterns. These simulations also show that even a moderate increase in the cover of the dominant species can lead to drastic changes in biodiversity patterns. Overall, all analyses consistently demonstrate that taking into account biotic interactions improves the explanatory power and predicting accuracy of the models. There are global demands to understand species-environment relationships to enable predictions of biodiversity changes with regard to a warming climate or altered land-use. However, uncertainties in such estimates exist, especially due to the precarious influence of biotic interactions. This thesis complements the understanding of biotic interactions in ecosystems by demonstrating their fundamental, yet species-specific and context-dependent, role in shaping species assemblages and performance across landscapes. From an applied point of view, our study highlights the importance of recognizing biotic interactions in future forecasts of biodiversity patterns.Bioottiset interaktiot (= lajien väliset vuorovaikutukset) vaikuttavat lajien levinneisyyteen abioottisten (mm. lämpötila, vesi) tekijöiden ohella. Bioottisten interaktioiden vaikutus voi olla negatiivinen (esim. lajien välinen kilpailu) tai positiivinen (esim. auringon paahteelta suojaaminen), ja vaikutuksen suunnan ja voimakkuuden on havaittu riippuvan lajeista ja vallitsevista ympäristöoloista. Ymmärrys bioottisten interaktioiden merkityksestä biodiversiteetille on kuitenkin vähäistä, mutta tätä voitaisiin täydentää useita lajeja ja ympäristöoloja yhtäaikaisesti käsittelevillä alueellisilla malleilla. Lisäksi myös alueellisia biodiversiteettiennusteita voitaisiin parantaa huomioimalla bioottiset interaktiot malleissa. Tämä väitöskirja tutkiikin bioottisten interaktioiden vaikutusta kasvillisuuteen, sekä miten nämä vaikutukset vaihtelevat lajien välillä ja erilaisissa ympäristöoloissa. Tutkimus nojautuu alueelliseen mallintamiseen hyödyntäen kattavia laji- ja ympäristöaineistoja Pohjois-Suomesta ja -Norjasta (69°N, 21°E). Mallinnuksessa käytetään niin yksittäisiä lajilevinneisyysmalleja, kuin myös lajirunsautta ja -rakennetta ja lajien kelpoisuutta tarkastelevia malleja. Mallinnusprosessissa malleihin tuodaan abioottisten tekijöiden lisäksi myös bioottisia muuttujia; tässä työssä valtalajien peittävyysarvot. Tutkimustulokset osoittavat ensinnäkin, että bioottiset interaktiot vaikuttavat kasvillisuuteen ja sen diversiteettiin. Tutkimuksen ennusteiden perusteella vähäinenkin kasvu tutkimusalueen valtalajin peitossa voi vähentää lajirunsautta merkittävästi. Toiseksi, bioottisten interaktioiden vaikutukset vaihtelevat lajien välillä, mutta vaikutuksen suuntaa ja voimakkuutta voidaan arvioida lajien ominaisuuksien perusteella. Kolmanneksi, bioottisten interaktioiden vaikutuksen suunta ja voimakkuus vaihtelee myös ympäristöolojen suhteen siten, että negatiivisimmat interaktiot vallitsevat suotuisissa ympäristöoloissa (ts. tuottoisat ympäristöt), kun positiivisia vuorovaikutuksia ilmenee taas karuissa ympäristöissä (esim. kylmät ja kuivat alueet). Viimeiseksi, bioottiset interaktiot vaikuttavat kasvillisuuteen koko maisematasolla ja niiden huomiotta jättäminen heikentää siten alueellisia biodiversiteettimalleja. Ilmastonmuutos ja maankäytön muuttuminen uhkaavat ekosysteemejä, mikä asettaa tarpeen tarkoille ja realistisille ennusteille biodiversiteetin muutoksista tulevaisuudessa. Tämä tutkimus osoittaa, että näitä ennusteita on mahdollista parantaa huomioimalla bioottiset interaktiot malleissa. Lisäksi tämä tutkimus valottaa bioottisten interaktioiden merkitystä kasvillisuudelle, etenkin herkillä pohjoisilla tundra-alueilla

Bioottisten interaktioiden merkitys ympäristögradienteilla : Esimerkkinä variksenmarjan vaikutus arktis-alpiinisessa kasvillisuudessa

Plant-plant interactions, i.e. biotic interactions, shape plant communities and the vegetation's succession along abiotic environmental factors. Positive interactions (e.g. facilitation) may expand species niches and enhance growth and reproduction. Negative interactions (e.g. competition, allelopathy) can interfere with growth and reproduction, even out competing some species from their niches. Negative and positive interactions co-occur, but research has shown that positive interactions are generally more common and important than negative ones in harsh environments. The theory of change of net-interaction from negative to positive along an environmental gradient is called the stress gradient hypothesis (=SGH). This work examines nordic crowberry's (Empetrum nigrum ssp. hermaphroditum) effect on arctic-alpine species' sexual reproduction under different environmental stress levels. Crowberry is a dominant species in low-nutrient, acidic arctic-alpine ecosystems. Its competitive ability is based on allelopathic characteristics and a forming of dense mats. It is also unpalatable for herbivores. The species facilitative characteristics include providing shelter from the wind and maintaining an ericoidmycorrhiza community. In this research environmental stress is represented by geomorphological disturbance and soil moisture, with the interactions between crowberry and other species are examined as the relationship between crowberry cover and the fitness measures (e.g. abundance of flowers or fruits) of study species. Explanation for the variation in the effects of crowberry is tried to find from the traits of the study species. Data was collected in May 2011 from Kilpisjärvi, northernmost Finland. The study area comprised 960 1m2 cells. In each cell the cover of each species (including crowberry), the abundance of each species flowers or fruits, the cover of geomorfological disturbance and soil moisture were recorded. Generalized linear models (=GLM) were run for all species to identify the best model for predicting fitness (as selected by the AIC-criterion). Spatial autocorrelation was accounted for by repeating analyses using generalized estimation equation models (GEE), which explicitly account for the spatial structure of data. 17 species were included to the research based on the abundance of their flowers and berries in the research area. Crowberry is included in the best fit model for 14 out of 17 species. The effect of crowberry was positive for four species and negative for ten species based on the modeling results. Interactions of the crowberry and one of the environmental variables are included to the models 19 times. In ten of these cases the interactions agreed with the predictions of the SGH (i.e. effect of crowberry became less negative with increasing abiotic stress). No species traits were consistently related to the outcome of interaction between crowberry and environmental variable, although crowberry effects on dwarf shrub species appeared to be more commonly positive than on other growthforms. According to these results, crowberry has dominant role in arctic-alpine plant communities. The species effect on sexual reproduction of other plant species is commonly negative, but the effect can change to positive along environmental stress gradients, supporting the SGH. Dwarf shrubs may interact positively with crowberry because of sharing the same mycorrhiza type, while more generally species may benefit from crowberry due to its provisioning of shelter from the wind and increased soil moisture. The negative effect of crowberry might be related to its production of allelopathic compounds or its dense growth. The reason for crowberry having a facilitative affect under disturbed conditions might be an indirect effect of disturbance decreasing crowberry's allelopathic effects. These results show that the roles of crowberry and biotic interactions in arctic-alpine vegetation are important. Therefore understanding their effects and mechanisms is important in predicting how this vegetation will respond to changing climate.Kasvien väliset vuorovaikutukset eli bioottiset interaktiot muokkaavat ympäristötekijöiden ohella tietylle alueelle kehittyvää kasvillisuutta. Positiiviset interaktiot (mm. fasilitaatio) laajentavat lajien levinneisyyksiä ja edesauttavat lajien kasvua ja lisääntymistä. Negatiiviset interaktiot (mm. kilpailu ja allelopatia) taas häiritsevät muiden lajien kasvua ja lisääntymistä ja jopa häätävät lajeja niiden esiintymisalueilta. Negatiiviset ja positiiviset interaktiot vaikuttavat lajien välillä samanaikaisesti, mutta tutkimuksissa on havaittu korkean ympäristöstressin alueilla positiivisten vuorovaikutuksen olevan negatiivisia voimakkaampia. Teoriaa kasvien välisen nettointeraktion suunnan muuttumisesta negatiivisesta positiiviseksi ympäristögradientilla kutsutaan stressigradienttihypoteesiksi (=SGH).Tässä työssä tarkastellaan pohjanvariksenmarjan (Empetrum nigrum ssp. hermaphroditum) vaikutusta muiden lajien lisääntymiskelpoisuudelle ympäristöstressin vaihdellessa. Variksenmarja on heikkoravinteisten ja happamien alueiden dominoivampia lajeja arktis-alpiinisissa ympäristöissä. Sen kilpailukyky perustuu sen allelopatisiin ominaisuuksiin, mattomaiseen kasvutapaan ja kelpaamattomuuteen kasvinsyöjien ravinnoksi. Sen fasilitoivia ominaisuuksia ovat mm. hyvä tuulensietokyky ja erikoidmykorritsayhdyskunnan ylläpito. Tutkimuksessa ympäristöstressiä edustavat geomorfologinen häiriö ja maaperän kosteus. Variksenmarjan ja lajien vuorovaikutusta tarkastellaan variksenmarjan peiton ja lajien lisääntymiskelpoisuuden välillä. Variksenmarjan vaikutuksen ja vaikutuksen suunnan muuttumisen lisäksi tarkastellaan lajien piirteitä mahdollisina interaktion suunnan selittäjinä. Aineisto kerättiin heinäkuussa 2011 Kilpisjärveltä, Luoteis-Lapista. Aineisto koostuu 960 neliömetrin ruudusta, joista on laskettu kukkivien tai marjovien lajien kukat ja marjat, arvioitu variksenmarjan ja muiden lajien peitto ja geomorfologisen häiriön osuus sekä mitattu maaperän kosteus. Variksenmarjan vaikutuksen selvittämiseksi aineistoa analysoitiin tilastollisin menetelmin, ensisijaisesti käyttäen yleistettyjä lineaarisia malleja (GLM). Mallit ajettiin kaikille lajeille kaikilla mahdollisilla muuttujakombinaatioilla sallien toisen asteen interaktiot. Kunkin lajin kelpoisuutta parhaiten mallintava malli valittiin AIC-mallinvalintamenettelyllä. Spatiaalisen autokorrelaation mahdollisuus huomioitiin vertaamalla tuloksia GEE- (Generalised estimation equations) menetelmällä saatuihin vastaaviin tuloksiin. GEE:ssä on mahdollisuus huomioida aineiston spatiaalinen rakenne. Mallinnettavia lajeja oli 17. Lajit valittiin niiden kukkimisen ja marjomisen yleisyyden perusteella. Variksenmarja on mukana 14 lajin parhaassa mallissa 17:stä lajista. Variksenmarjan vaikutus on mallinnustulosten perusteella positiivinen neljälle lajille ja negatiivinen kymmenelle. Variksenmarjan ja toisen ympäristömuuttujan interaktio on mukana 19 kertaa. Kymmenessä tapauksessa interaktion suunnan muuttuminen ympäristögradientilla tukee stressigradienttihypoteesia. Lajien piirteistä ei löytynyt yksiselitteistä selitystä interaktion suunnalle, mutta tulosten perusteella variksenmarja vaikuttaa positiivisesti yleisimmin muihin varpukasveihin. Tulosten perusteella variksenmarjalla on varsin dominoiva rooli arktis-alpiinisessa ympäristössä. Se vaikuttaa lajien kelpoisuuteen yleisesti negatiivisesti, mutta vaikutus muuttuu usein positiiviseksi lajin ankaraksi kokemassa ympäristössä tukien SGH:a. Variksenmarjan negatiivinen vaikutus liittyy luultavasti allelopatisiin ominaisuuksiin ja mattomaiseen kasvutapaan. Fasilitointi taas erikoidmykorritsayhdyskunnan ylläpitoon, tuulen suojan tarjoamiseen ja maaperän kosteuttamiseen, sekä välillisesti allelopatisten mekanismien heikentymiseen geomorfologisen häiriön ollessa suurta. Varpukasvien positiiviseen ragointiin voi olla syynä samanlainen kasvumuoto tai symbioosin muodostaminen saman mykorritsatyypin kanssa. Tulokset osoittavat, että bioottisten interaktioiden merkitys arktis-alpiinisessa kasvillisuudessa ovat tärkeitä. Niiden vaikutusten ja mekanismien tunteminen on tärkeää muuttuvassa ilmastossa

How to evaluate community predictions without thresholding?

Stacked species distribution models (S-SDM) provide a tool to make spatial predictions about communities by first modelling individual species and then stacking the modelled predictions to form assemblages. The evaluation of the predictive performance is usually based on a comparison of the observed and predicted community properties (e.g. species richness, composition). However, the most available and widely used evaluation metrics require the thresholding of single species' predicted probabilities of occurrence to obtain binary outcomes (i.e. presence/absence). This binarization can introduce unnecessary bias and error. Herein, we present and demonstrate the use of several groups of new or rarely used evaluation approaches and metrics for both species richness and community composition that do not require thresholding but instead directly compare the predicted probabilities of occurrences of species to the presence/absence observations in the assemblages. Community AUC, which is based on traditional AUC, measures the ability of a model to differentiate between species presences or absences at a given site according to their predicted probabilities of occurrence. Summing the probabilities gives the expected species richness and allows the estimation of the probability that the observed species richness is not different from the expected species richness based on the species' probabilities of occurrence. The traditional Sorensen and Jaccard similarity indices (which are based on presences/absences) were adapted to maxSorensen and maxJaccard and to probSorensen and probJaccard (which use probabilities directly). A further approach (improvement over null models) compares the predictions based on S-SDMs with the expectations from the null models to estimate the improvement in both species richness and composition predictions. Additionally, all metrics can be described against the environmental conditions of sites (e.g. elevation) to highlight the abilities of models to detect the variation in the strength of the community assembly processes in different environments. These metrics offer an unbiased view of the performance of community predictions compared to metrics that requiring thresholding. As such, they allow more straightforward comparisons of model performance among studies (i.e. they are not influenced by any subjective thresholding decisions).Peer reviewe

Arctic shrubification mediates the impacts of warming climate on changes to tundra vegetation

Peer reviewe

Outcomes of biotic interactions are dependent on multiple environmental variables

QUESTION : Can variation in the outcome of biotic interactions in relation to environmental severity bemore accurately predictedwhen consideringmultiple stress and/or disturbance variables? LOCATION : Arctic-alpine tundra in Kilpisj€arvi, North Finland. METHODS : To test the impact of including multiple environmental variables in analyses of the outcomes of biotic interactions, we modelled reproductive effort and cover of 17 arctic-alpine species as a function of Empetrum nigrum subsp. hermaphroditum cover, geomorphological disturbance and soil moisture with statistical interactions of the explanatory variables included.We implemented a best-subset approach using generalized linear models (GLM) and selected the bestmodel for each species based on Akaike’s information criterion (AIC). RESULTS : For the majority of species, models including multiple environmental variables were selected as best. Reproductive effort depended on one or both environmental variables for all species, and 14 species were additionally influenced by Empetrum,with the impact of Empetrum varyingwith abiotic conditions in all but one of those species. Moreover, the three-way interaction of three explanatory variables was included in the best-fit models for six species. The impact of Empetrum on species cover showed a similar pattern, with 11 species affected by Empetrum and its statistical interactions with one or both abiotic variables. CONCLUTIONS : Biotic interactions have an important role in arctic-alpine vegetation, but to fully understand variation in their effects multiple environmental factors should be explicitly considered. In this study, the outcome of biotic interactions was frequently dependent on two abiotic variables (and occasionally additionally on their statistical interaction). Therefore, we demonstrate that studies based on only one environmental factor may cause misleading interpretations of the nature of biotic interactions in plant communities where there are multiple independent variables underlying the habitat severity gradient.Academy of Finland (Project Number 1140873), the Nordenskiöld Foundation and the Department of Geosciences and Geography (University of Helsinki)http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1654-1103hb201

Comparative analysis of diversity and environmental niches of soil bacterial, archaeal, fungal and protist communities reveal niche divergences along environmental gradients in the Alps

Although widely used in ecology, comparative analyses of diversity and niche properties are still lacking for microorganisms, especially focusing on niche variations. Quantifying the niches of microbial taxa is necessary to then forecast how taxa and the communities they compose might respond to environmental changes. In this study, we first identified important topoclimatic, edaphic, spatial and biotic drivers of the alpha and beta di-versity of bacterial, archaeal, fungal and protist communities. Then, we calculated the niche breadth and position of each taxon along the important environmental gradients to determine how these vary within and among the taxonomic groups. We found that edaphic properties were the most important drivers of both, community di-versity and composition, for all microbial groups. Protists and bacteria presented the largest niche breadths on average, followed by archaea, with fungi displaying the smallest. Niche breadth generally decreased towards environmental extremes, especially along edaphic gradients, suggesting increased specialization of microbial taxa in highly selective environments. Overall, we showed that microorganisms have well defined niches, as do macro-organisms, likely driving part of the observed spatial patterns of community variations. Assessing niche variation more widely in microbial ecology should open new perspectives, especially to tackle global change effects on microbes.Peer reviewe

Improving forecasts of arctic-alpine refugia persistence with landscape-scale variables

Refugia, the sites preserving conditions reminiscent of suitable climates, are projected to be crucial for species in a changing climate, particularly at high latitudes. However, the knowledge of current locations of high-latitude refugia and particularly their ability to retain suitable conditions under future climatic changes is limited. Occurrences of refugia have previously been mainly assessed and modelled based solely on climatic features, with insufficient attention being paid to potentially important landscape-scale factors. Here, climate-only models and full' models incorporating topo-edaphic landscape-scale variables (radiation, soil moisture and calcareousness) were developed and compared for 111 arctic-alpine plant species in Northern Fennoscandia. This was done for both current and future climates to determine cells with resilient climatic suitability harbouring refugia. Our results show that topographic and edaphic landscape-scale predictors both significantly improve models of arctic-alpine species distributions and alter projections of refugia occurrence. The predictions of species-climate models ignore landscape-scale ecological processes and may thus provide inaccurate estimates of extinction risk and forecasts of refugia where species can persist under a changing climate.Peer reviewe

Contrasting effects of biotic interactions on richness and distribution of vascular plants, bryophytes and lichens in an arctic–alpine landscape

Biotic interactions may strongly affect the distribution of individual species and the resulting patterns of species richness. However, the impacts can vary depending on the species or taxa examined, suggesting that the influences of interactions on species distributions and diversity are not always straightforward and can be taxon-contingent. The aim of this study was therefore to examine how the importance of biotic interactions varies within a community. We incorporated three biotic predictors (cover of the dominant vascular species) into two correlative species richness modelling frameworks to predict spatial variation in the number of vascular plants, bryophytes and lichens in arctic-alpine Fennoscandia, in N Europe. In addition, predictions based on single-species distribution models were used to determine the nature of the impact (negative vs. positive outcome) of the three dominant species on individual vascular plant, bryophyte and lichen species. Our results suggest that biotic variables can be as important as abiotic variables, but their relative contributions in explaining the richness of sub-dominant species varies among dominant species, species group and the modelling framework implemented. Similarly, the impacts of biotic interactions on individual species varied among the three species groups and dominant species, with the observed patterns partly reflecting species’ biogeographic range. Our study provides additional support for the importance of biotic interactions in modifying arctic-alpine biodiversity patterns, and highlights that the impacts of interactions are not constant across taxa or biotic drivers. The influence of biotic interactions, including the taxon-contingency and range-based impacts, should therefore be accounted for when developing biodiversity forecasts.Academy of Finland (Project Number 1140873). Research Foundation of the University of Helsinki.http://link.springer.com/journal/3002017-04-30hb2016Plant Production and Soil Scienc

Predicting spatial patterns of soil bacteria under current and future environmental conditions

Soil bacteria are largely missing from future biodiversity assessments hindering comprehensive forecasts of ecosystem changes. Soil bacterial communities are expected to be more strongly driven by pH and less by other edaphic and climatic factors. Thus, alkalinisation or acidification along with climate change may influence soil bacteria, with subsequent influences for example on nutrient cycling and vegetation. Future forecasts of soil bacteria are therefore needed. We applied species distribution modelling (SDM) to quantify the roles of environmental factors in governing spatial abundance distribution of soil bacterial OTUs and to predict how future changes in these factors may change bacterial communities in a temperate mountain area. Models indicated that factors related to soil (especially pH), climate and/or topography explain and predict part of the abundance distribution of most OTUs. This supports the expectations that microorganisms have specific environmental requirements (i.e., niches/envelopes) and that they should accordingly respond to environmental changes. Our predictions indicate a stronger role of pH over other predictors (e.g. climate) in governing distributions of bacteria, yet the predicted future changes in bacteria communities are smaller than their current variation across space. The extent of bacterial community change predictions varies as a function of elevation, but in general, deviations from neutral soil pH are expected to decrease abundances and diversity of bacteria. Our findings highlight the need to account for edaphic changes, along with climate changes, in future forecasts of soil bacteria.Peer reviewe

Low spatial autocorrelation in mountain biodiversity data and model residuals

Spatial autocorrelation (SAC) is a common feature of ecological data where observations tend to be more similar at some geographic distance(s) than expected by chance. Despite the implications of SAC for data dependencies, its impact on the performance of species distribution models (SDMs) remains controversial, with reports of both strong and negligible impacts on inference. Yet, no study has comprehensively assessed the prevalence and the strength of SAC in the residuals of SDMs over entire geographic areas. Here, we used a large-scale spatial inventory in the western Swiss Alps to provide a thorough assessment of the importance of SAC for (1) 850 species belonging to nine taxonomic groups, (2) six predictors commonly used for modeling species distributions, and (3) residuals obtained from SDMs fitted with two algorithms with the six predictors included as covariates. We used various statistical tools to evaluate (1) the global level of SAC, (2) the spatial pattern and spatial extent of SAC, and (3) whether local clusters of SAC can be detected. We further investigated the effect of the sampling design on SAC levels. Overall, while environmental predictors expectedly displayed high SAC levels, SAC in biodiversity data was rather low overall and vanished rapidly at a distance of similar to 5-10 km. We found low evidence for the existence of local clusters of SAC. Most importantly, model residuals were not spatially autocorrelated, suggesting that inferences derived from SDMs are unlikely to be affected by SAC. Further, our results suggest that the influence of SAC can be reduced by a careful sampling design. Overall, our results suggest that SAC is not a major concern for rugged mountain landscapes.Peer reviewe