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Pro Gradu Miia Farstad 2021 04 Master's thesis in Geography Physical Geography Biotic Interactions with Distributions of Arctic and Alpine Flora in Fennoscandia Miia Farstad 2021 Supervisors: Miska Luoto Pekka Niittynen UNIVERSITY OF HELSINKI FACULTY OF SCIENCE DEPARTMENT OF GEOSCIENCES AND GEOGRAPHY GEOGRAPHY P. O. Box 64 (Gustaf Hällströmin katu 2) 00014 University of Helsinki Tiedekunta – Fakultet – Faculty Osasto – Institution – Department Faculty of Science Department of Geosciences and Geography Tekijä – Författare – Author Miia Farstad Tutkielman otsikko – Avhandlings titel – Title of thesis Biotic Interactions with Distributions of Arctic and Alpine Flora in Fennoscandia Koulutusohjelma ja opintosuunta – Utbildningsprogram och studieinriktning – Programme and study track Master's programme in geography, Physical geography Tutkielman taso – Avhandlings nivå – Level of the thesis Aika – Datum – Date Sivumäärä – Sidoantal – Number of pages Master's thesis May 2021 88 pages + 4 appendices Tiivistelmä – Referat – Abstract Due to the harsh conditions in high latitude alpine and arctic regions, climate or land use changes make them very vulnerable. Thus, it is vital to study the habitats of these regions and increase our understanding of what factors impact species distributions. Species distribution modelling can be used to predict possible habitats for species and further inspect the relationships between different environmental variables and species. Generally, these species distribution models have been created using variables describing the topographical and climatic conditions of the study area. Recently there has been more evidence supporting the inclusion of biotic variables to species distribution models at all scales. Including biotic variables can be difficult, as these relationships can be challenging to quantify. This study uses the Normalized Difference Vegetation Index (NDVI) as a surrogate for plant biomass, thus representing biotic interactions. This study aims to answer what are the relationships between environmental variables and the predicted distributions and will including a biotic variable improve the species distribution models. The study data includes observational data from 683 arctic and alpine plant species from Norway, Sweden, and Finland. The observation data were collected from the three national databanks of Norway, Sweden and Finland and completed with observations from the Global Biodiversity Information Facility and observation data collected by the BioGeoClimate Modelling Lab. The cohesive study area was outlined with the biogeographical regions defined by the European Environment Agency. Overall, six environmental variables are used in this study: annual mean temperature, the maximum temperature of the warmest month, annual precipitation, elevation difference in a cell, bedrock class, and NDVI. The NDVI data was gathered by NASA’s MODIS sensors. The observations and the environmental variables were projected into a grid consisting of 1 x 1 km cells covering the whole study area. This study uses the ensemble modelling technique with four individual modelling methods: generalized linear models (GLM), generalized additive models (GAM), generalized boosted models (GBM) and random forests (RF). The modelling process consisted of two modelling rounds so that the impact of NDVI could be evaluated. The first modelling round included all the environmental variables except NDVI (the topoclimate model) and the second modelling round included all the environmental variables (the full model). The two temperature variables, annual mean temperature and the maximum temperature of the warmest month, had the highest mean variable importance values. With the topoclimate model, annual precipitation ranked third with the rest of the climate variables, but when NDVI was added to the models, it rose above annual precipitation. Overall, among the studied arctic and alpine species, the variable importance values of both the edaphic and topographical variables were low. In general, both the topoclimate models and full models performed very well. The mean AUC- and TSS-values were all higher for the full models, indicating that including a biotic variable improved the models. When the binary predictions of both modelling rounds were compared, it was clear that NDVI refined the projected distributions for most species. The results from this study confirm the discovery that including a biotic variable, such as NDVI, has the potential to increase the predictive power of species distribution models. One of the main problems with including biotic variables in species distribution models has been the difficulty of quantifying biotic interactions. NDVI can thus be a promising tool to overcome these difficulties, as it is one of the most direct variables to describe ecosystem productivity, can be acquired at various scales, and as remotely sensed data, it can also cover areas that are difficult to access. Avainsanat – Nyckelord – Keywords biogeography, species distribution modelling, biotic interactions, NDVI, arctic and alpine plant species Säilytyspaikka – Förvaringställe – Where deposited University of Helsinki electronic theses library E-thesis/HELDA Muita tietoja – Övriga uppgifter – Additional information Tiedekunta – Fakultet – Faculty Osasto – Institution – Department Matemaattis-luonnontieteellinen tiedekunta Geotieteiden ja maantieteen osasto Tekijä – Författare – Author Miia Farstad Tutkielman otsikko – Avhandlings titel – Title of thesis Bioottiset vuorovaikutukset arktisen ja alpiinisen kasvilajiston levinneisyyksien kanssa Fennoskandiassa Koulutusohjelma ja opintosuunta – Utbildningsprogram och studieinriktning – Programme and study track Maantieteen maisteriohjelma, luonnonmaantiede Tutkielman taso – Avhandlings nivå – Level of the thesis Aika – Datum – Date Sivumäärä – Sidoantal – Number of pages Pro gradu Toukokuu 2021 88 sivua + 4 liitettä Tiivistelmä – Referat – Abstract Korkeiden leveysasteiden alpiinisten ja arktisten alueiden ankarien olosuhteiden vuoksi muutokset ilmastossa tai maankäytössä tekevät niistä erityisen haavoittuvia. Siksi on erittäin tärkeää tutkia näitä elinympäristöjä lisätäksemme ymmärrystä siitä, mitkä tekijät vaikuttavat lajien levinneisyyksiin näillä alueilla. Lajien levinneisyysmallinnusta voidaan käyttää ennustamaan lajien mahdollisia elinympäristöjä ja tutkimaan myös eri ympäristömuuttujien ja lajien keskinäisiä suhteita. Yleensä lajien levinneisyysmalleissa käytetyt muuttujat ovat kuvanneet topografisia ja ilmastollisia olosuhteita, mutta viime vuosina on tullut enemmän todisteita siitä, että bioottisten muuttujien sisällyttäminen lajien levinneisyysmalleihin parantaa niitä kaikissa mittakaavoissa. Bioottisten muuttujien sisällyttäminen voi olla vaikeaa, sillä näiden suhteiden kvantifiointi voi olla haastavaa. Tässä tutkimuksessa käytetään normalisoitua kasvillisuusindeksiä (NDVI) edustamaan kasvien biomassaa ja samalla edustamaan bioottisia vuorovaikutuksia. Tämän tutkimuksen tarkoituksena on vastata siihen, että mitkä ovat ennustettujen levinneisyyksien ja niissä käytettyjen ympäristömuuttujien suhteet tutkimuslajeihin, sekä parantaako bioottisen muuttujan sisällyttäminen lajien levinneisyysmalleja. Tässä tutkimuksessa käytetään 683 arktisen ja alpiinisen kasvilajin havaintodataa Norjasta, Ruotsista ja Suomesta. Havaintodata on kerätty Norjan, Ruotsin ja Suomen kansallisista datapankeista ja havaintoja on myös täydennetty Global Biodiversity Information Facility-databankin havainnoilla sekä BioGeoClimate Modelling Lab -tutkimusryhmän keräämillä havainnoilla. Yhtenäinen tutkimusalue rajattiin käyttämällä Euroopan ympäristökeskuksen määrittelemiä biogeografisia alueita. Kaiken kaikkiaan tässä tutkimuksessa käytettiin kuutta ympäristömuuttujaa: vuotuinen keskilämpötila, lämpimimmän kuukauden maksimilämpötila, vuotuinen sadanta, korkeusero solussa, kallioperä, sekä NDVI. NDVI-data on kerätty NASA:n MODIS-sensoreilla. Havainnot ja ympäristömuuttujat projisointiin koko tutkimusalueen kattavaan 1 x 1 km ruudukkoon. Tässä tutkimuksessa käytettiin kokoomamallia, joka muodostuu neljästä erillisestä mallintamismenetelmästä: yleistetty lineaarinen malli (GLM), yleistetty additiivinen malli (GAM), yleistetty luokittelupuumenetelmä (GBM) ja satumetsä (RF). Mallinnus koostui kahdesta mallintamiskerroksesta, jotta NDVI:n vaikutus ennusteisiin voitiin arvioida. Ensimmäinen mallintamiskierros sisälsi kaikki ympäristömuuttujat paitsi NDVI:n (topoklimaattinen malli) ja toinen mallintamiskierros sisälsi kaikki ympäristömuuttujat (täysmalli). Mallinnuksessa käytetyt kaksi lämpötilamuuttujaa, vuoden keskilämpötila ja lämpimimmän kuukauden maksimilämpötila, saivat korkeimmat keskimääräiset muuttujien tärkeysarvot. Topoklimaattisilla malleilla vuotuinen sadanta sijoittui kolmanneksi muiden ilmastomuuttujien kanssa, mutta kun malleihin lisättiin NDVI, nousi se vuotuisen sadannan yläpuolelle. Kaiken kaikkiaan tutkittujen arktisten ja alpiinisten lajien joukossa sekä edafisen ja topografisen muuttujien tärkeysarvot olivat matalimmat. Yleisesti sekä topoklimaattiset että täysmallit suoriutuivat hyvin. Keskimääräiset AUC- ja TSS-arvot olivat kuitenkin korkeammat täysmallissa (niin kokoomamalleilla kuin yksittäisillä malleilla), mikä osoitti, että bioottisen muuttujan lisääminen paransi malleja. Kun molempien mallintamiskierrosten binaarisia ennusteita verrattiin, oli selkeää, että useimmille lajeille NDVI:n sisällyttäminen tarkensi ennustettuja levinneisyyksiä. Tämä tutkimuksen tulokset vahvistavat havainnon siitä, että lisäämällä
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