Remotely Sensed Variables of Ecosystem Functioning Support Robust Predictions of Abundance Patterns for Rare Species

Global environmental changes are affecting both the distribution and abundance of species at an unprecedented rate. To assess these effects, species distribution models (SDMs) have been greatly developed over the last decades, while species abundance models (SAMs) have generally received less attention even though these models provide essential information for conservation management. With population abundance defined as an essential biodiversity variable (EBV), SAMs could offer spatially explicit predictions of species abundance across space and time. Satellite-derived ecosystem functioning attributes (EFAs) are known to inform on processes controlling species distribution, but they have not been tested as predictors of species abundance. In this study, we assessed the usefulness of SAMs calibrated with EFAs (as process-related variables) to predict local abundance patterns for a rare and threatened species (the narrow Iberian endemic ‘Gerês lily’ Iris boissieri; protected under the European Union Habitats Directive), and to project inter-annual fluctuations of predicted abundance. We compared the predictive accuracy of SAMs calibrated with climate (CLI), topography (DEM), land cover (LCC), EFAs, and combinations of these. Models fitted only with EFAs explained the greatest variance in species abundance, compared to models based only on CLI, DEM, or LCC variables. The combination of EFAs and topography slightly increased model performance. Predictions of the inter-annual dynamics of species abundance were related to inter-annual fluctuations in climate, which holds important implications for tracking global change effects on species abundance. This study underlines the potential of EFAs as robust predictors of biodiversity change through population size trends. The combination of EFA-based SAMs and SDMs would provide an essential toolkit for species monitoring programs.This work has been carried out within the H2020 project ECOPOTENTIAL: Improving Future Ecosystem Benefits Through Earth Observations (http://www.ecopotential-project.eu). The project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 641762. S.A.-C., D.A.-S., and J.H. received funding from the ECOPOTENTIAL project. A.R. was financially supported by the Xunta de Galicia, Spain (post-doctoral fellowship ED481B2016/084-0). J.F.G. was funded by the Individual Scientific Employment Stimulus Program (2017) by the Portuguese Foundation for Science and Technology (FCT CEEC-2017)

Model-Assisted Bird Monitoring Based on Remotely Sensed Ecosystem Functioning and Atlas Data

Urgent action needs to be taken to halt global biodiversity crisis. To be effective in the implementation of such action, managers and policy-makers need updated information on the status and trends of biodiversity. Here, we test the ability of remotely sensed ecosystem functioning attributes (EFAs) to predict the distribution of 73 bird species with different life-history traits. We run ensemble species distribution models (SDMs) trained with bird atlas data and 12 EFAs describing different dimensions of carbon cycle and surface energy balance. Our ensemble SDMs—exclusively based on EFAs—hold a high predictive capacity across 71 target species (up to 0.94 and 0.79 of Area Under the ROC curve and true skill statistic (TSS)). Our results showed the life-history traits did not significantly affect SDM performance. Overall, minimum Enhanced Vegetation Index (EVI) and maximum Albedo values (descriptors of primary productivity and energy balance) were the most important predictors across our bird community. Our approach leverages the existing atlas data and provides an alternative method to monitor inter-annual bird habitat dynamics from space in the absence of long-term biodiversity monitoring schemes. This study illustrates the great potential that satellite remote sensing can contribute to the Aichi Biodiversity Targets and to the Essential Biodiversity Variables framework (EBV class “Species distribution”)Fieldwork campaigns were carried out within the project “Estudios sobre a biodiversidade do Macizo Central Galego. Lugar de Importancia Comunitaria” (PGIDT99PXI20002B) and “Caracterización de los vertebrados del LIC Macizo Central e Bidueiral de Montederramo”, code: 2008-CE227”, funded by SAYFOR S.L. This work also received funding from Xunta de Galicia through the grant to structure and consolidate competitive research groups of Galicia (ED431B 2018/36). A.R. was funded by the Xunta de Galicia, Spain (post-doctoral fellowship ED481B2016/084-0). S.A.-C. was financially supported by PORBIOTA—E-Infraestrutura Portuguesa de Informação e Investigação em Biodiversidade (POCI-01-0145-FEDER-022127)S

Mainstreaming remotely sensed ecosystem functioning in ecological niche models

Part of this work was funded by the EU H2020 Project 641762 ‘ECOPOTENTIAL: Improving Future Ecosystem Benefits through Earth Observations’, from which many valuable thoughts originated. A.R. was funded by the Xunta de Galicia (post‐doctoral fellowship ED481B2016/084‐0) and currently by ‘Juan de la Cierva’ fellowship program funded by the Spanish Ministry of Science and Innovation (IJC2019‐041033‐I). J.G. was funded by the Individual Scientific Employment Stimulus Program (2017) by the Portuguese Foundation for Science and Technology (FCT CEECIND/02331/2017/CP1423/CT0012). S.A‐C was funded by the PORBIOTA ‐ Portuguese e‐Infrastructure for Information and Research on Biodiversity (POCI‐01‐0145‐FEDER‐022127) project grant and is currently supported by the 'María Zambrano' program funded by the Spanish Ministry of Universities and the EU‐NextGenerationEU fund.Biodiversity is declining globally at unprecedented rates. Ecological niche models (ENMs) are one of the most widely used toolsets to appraise global change impacts on biodiversity. Here, we identify a variety of advantages of incorporating remotely sensed ecosystem functioning attributes (EFAs) into ENMs. The development of ENMs that explicitly incorporate ecosystem functioning will allow a more holistic and integrative perspective of the habitat dynamics. The synergies between the increasingly available open-access satellite images and cloud-based platforms for planetary-scale geospatial analysis offer an unprecedented opportunity to incorporate ecosystem processes and disturbances (such as fires, insect outbreaks or droughts) that have been so far largely neglected in ecological niche characterization and modelling. The most paradigmatic example of EFAs is the application of time series of spectral vegetation indices related to primary productivity and carbon cycle. EFAs related to surface energy balance and water cycles derived from remote sensing products such as land surface temperature or soil moisture enable a fine-scale characterization of the species' niche—eventually improving the predictive performance of ENMs. All these advantages confirm that a new generation of ENMs based on such EFAs would offer great perspectives to increase our ability to monitor habitat suitability trends and population dynamics. However, despite the technical advances and increasing effort of remote sensing community to develop integrative EFAs, ENMs have yet to make full profit of the most recent developments by integrating them in ENMs. A coordinated agenda for remote sensing experts and ecological modellers will be essential over the coming years to bridge the gap between remote sensing and ecology disciplines and to take full (and timely) advantage of the fast-growing body of Earth observation data and remote sensing technologies—with special emphasis on the development and testing of new variables related to key processes driving ecosystem functioning.EU H2020 641762Individual Scientific Employment Stimulus ProgramSpanish Ministry of Universitiese‐Infrastructure for Information and Research on BiodiversityFundação para a Ciência e a TecnologiaMinisterio de Ciencia e InnovaciónFundació Catalana de Trasplantament CEECIND/02331/2017/CP1423/CT0012, POCI‐01‐0145‐FEDER‐022127Xunta de Galicia ED481B2016/084‐

Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling

Global environmental changes are rapidly affecting species' distributions and habitat suitability worldwide, requiring a continuous update of biodiversity status to support effective decisions on conservation policy and management. In this regard, satellite-derived Ecosystem Functional Attributes (EFAs) offer a more integrative and quicker evaluation of ecosystem responses to environmental drivers and changes than climate and structural or compositional landscape attributes. Thus, EFAs may hold advantages as predictors in Species Distribution Models (SDMs) and for implementing multi-scale species monitoring programs. Here we describe a modelling framework to assess the predictive ability of EFAs as Essential Biodiversity Variables (EBVs) against traditional datasets (climate, land-cover) at several scales. We test the framework with a multi-scale assessment of habitat suitability for two plant species of conservation concern, both protected under the EU Habitats Directive, differing in terms of life history, range and distribution pattern (Iris boissieri and Taxus baccata). We fitted four sets of SDMs for the two test species, calibrated with: interpolated climate variables; landscape variables; EFAs; and a combination of climate and landscape variables. EFAbased models performed very well at the several scales (AUCmedian from 0.881±0.072 to 0.983±0.125), and similarly to traditional climate-based models, individually or in combination with land-cover predictors (AUCmedian from 0.882±0.059 to 0.995±0.083). Moreover, EFAbased models identified additional suitable areas and provided valuable information on functional features of habitat suitability for both test species (narrowly vs. widely distributed), for both coarse and fine scales. Our results suggest a relatively small scale-dependence of the predictive ability of satellite-derived EFAs, supporting their use as meaningful EBVs in SDMs from regional and broader scales to more local and finer scales. Since the evaluation of species' conservation status and habitat quality should as far as possible be performed based on scalable indicators linking to meaningful processes, our framework may guide conservation managers in decision-making related to biodiversity monitoring and reporting schemes.This research was developed as part of the ECOPOTENTIAL project financed by European Union's Horizon 2020 research and innovation program under grant agreement No. 641762. SAC, DAS and JPH received funding from the ECOPOTENTIAL project. JG was supported by FCT (Portuguese Science Foundation) through PhD grant SFRH/BD/90112/2012. DAS received funding from Ministerio de Educación, Cultura y Deporte, JC2015-00316 grant, and Ministerio de Ciencia e Innovación, CGL2014-61610-EXP project

Kliimamuutuse mõju hindamine rannaniidu taimekooslusele mesokosmi katse ja mehitamata õhusõidukiga kogutud andmete põhjal

A Thesis for applying for the degree of Doctor of Philosophy in Environmental Protection.Väitekiri filosoofiadoktori kraadi taotlemiseks keskkonnakaitse erialal.Semi-natural grasslands are an essential part of the cultural landscape of Europe. Semi-natural grasslands are commonly characterised by a very high biodiversity, including rare species. Beyond the high biodiversity value, semi-natural grasslands worldwide provide many ecosystem services, including: carbon sequestration and storage, nutrient cycling, regulation of soil quality, habitats for migrating birds, erosion control, and flood regulation. Within the realm of semi-natural grasslands, coastal meadows are particularly important. However, coastal grasslands are threatened by a range of factors such as coastal squeeze, transformation into monoculture ponds, pollution, and climate change. Coastal areas are threatened at a range of spatial scales as a result of sea-level rise, and can include higher flooding frequency in coastal areas, salt water intrusion in aquifers, and potential declines in the extent of coastal wetlands. A warmer climate also implies a modification in precipitation patterns affecting runoff into the sea. In coastal areas, both water levels and salinity have a strong impact on species distribution and therefore on the structure and composition of aquatic and coastal floral and faunal communities. Consequently, plant communities in coastal meadows are expected to undergo changes in their composition and structure. The current thesis explores different methodologies to assess plant community distribution, above-ground biomass, and the effects of management type, duration, and intensity on sward structure using UAV-derived multispectral data and aerial photogrammetry. In addition, the keystone of this thesis is a mesocosm experiment that was used to assess shifts in species richness and abundance in plant community types in Estonian coastal meadows related to future change scenarios of water level and salinity for the Baltic Sea. a. Unmanned Aerial Vehicle (UAV) The use of UAV demonstrated to be able to identify plant community extent and distribution in high biodiversity value coastal meadows in West Estonia. Species diversity and biomass significantly influence the quality of data and this should be accounted for when planning the sample collection to achieve better results. This study has shown that UAVs are useful tools of mapping grasslands at a plant community level. Also, UAV showed to be possible to reveal the structure of the grassland and how it is affected by the management history. For example, the grassland turns more homogeneous under long-term monospecific grazing, b. Mesoscosm Experiment The mesocosm experiment in the present study revealed different temporal changes of wetland communities to altered salinity and water conditions, highlighting the response of plant species to environmental variables. These changes were not significant according to alteration of water level and salinity in the Open Pioneer community, but they were over time. On the other hand, Lower Shore and Upper Shore had significant changes according to time and treatments. These could be explained by dynamic differences in the communities, since Open Pioneer was more variable. c. Conclusions Both methodologies, remote sensing and the mesocosm experiment, are evidently important to evaluate the structure and function of Estonian coastal meadows. The mapping of the extent and structure of coastal plant communities allows an evaluation of the current state of the ecosystem. The mesocosm experiment helps to understand changes in plant community composition under altered conditions of water level and salinity in Estonian coastal meadows and consequently, understand how species richness, abundance, and biomass will respond to those changes. This information is important when considering the protection and potential management of these areas taking into account the species diversity of fauna and flora as well as that of livestock.Uuring viidi läbi kahel tasandil: uuringukohtades Lääne-Eestis ja katsekeskkonnas. Esimesel juhul valiti Silma looduskaitsealal, Matsalu rahvuspargis ja Vormsi saarel ranniku taimekoosluste ja maapealse biomassi kaardistamiseks kokku üheksa rannaniiduala (I, II). Teine osa hõlmab mesokosmi katset (III), mille käigus kasutati katse seadmiseks ja eksperimenteerimiseks Silma looduskaitsealalt kogutud proove. Vaatamata oma suhteliselt väikesele pindalale (45 228 km2) iseloomustab Eestit mitmekesine geoloogia, pinnamood ja kliima. Läänemere rannaniidud on tekkinud ja need säilivad maa isostaatilise tõusu, setete kogunemise ja alade vähese intensiivsusega majandamise – karjatamise või niitmise – tõttu. Eesti rannikumärgaladel on ebatavaline hüdroloogiline režiim. Kuna loodete ulatus on väga väike (~0,02 m), põhjustab rannaniitude üleujutusi valdavalt tsüklonaalne aktiivsus Põhja-Atlandil ja Fennoskandias. Üleujutuste sagedus ja ulatus on ebaregulaarne ning varieerub kogu rannikumaastikul, sõltudes tuule kiirusest ja suunast. Hiljutised hinnangud suhtelise meretaseme tõusu kohta kolmelt mõõnamõõturilt piki Eesti rannikut on järgmised: Tallinnas 1,5–1,7 mm a-1, Narva-Jõesuus 1,7–2,1 mm a-1 ja Pärnus 2,3–2,7 mm a-1 (Ward et al., 2014). Taimekoosluse klassifitseerimiseks ja biomassi prognoosimiseks analüüsiti üheksat rannikumärgala kolmes kohas Silma looduskaitsealal, kahes kohas Matsalu rahvuspargis ja neljas kohas Vormsi saarel. Neis kohtades esinevad kõik väitekirjas käsitletud taimekooslused. Uurimiskohtade taimekooslused liigitati vastavalt Burnside´i jt fütosotsioloogilisele klassifikatsioonile (2007): pilliroostik, võsasoo, madal rannik, kõrgrannik, pioneerliikidega paljakud, kõrgrohustu, võsa ja metsamaa. Võsasoo ning võsa ja metsamaa jäeti nende marginaalse esinemise tõttu uurimusest välja. Uurimistöö käigus tehti kaks erinevat analüüsi, kasutades UAV-ga kogutud multispektraal- ja rgb-fotosid. UAV multispektraalseid pilte kasutati taimekoosluste kaardistamiseks Silma looduskaitsealal Põhja-Tahu, Lõuna-Tahu ja Kudani rannaniidul (I). Järgnevalt kasutati multispektraalseid ja rgb-pilte kõrge ruumilise eraldusvõimega kaartide koostamiseks maapealse biomassi tuvastamiseks kõigis üheksas uuringukohas (II). Taimekoosluste kaardistamiseks (I) ja maapealse biomassi prognoosimiseks (II) kasutati otsustusmetsa klassifikatsiooni. Seejärel analüüsiti maapealse biomassi kaartide abil majandamisviisi ja intensiivsuse mõju rannaniitude heinamaade struktuurile (II). Teavet rannaniitude kasutusviisi kohta saadi maaomanikega isiklikult suheldes. Uurimistöö teises osas valiti mesokosmi katse jaoks kolm taimekooslust: pioneerliikidega paljakud, madal rannik ja kõrgrannik. Need kooslused valiti sealsete võtmeliikide spetsiifilise autökoloogilise kasvukohaeelistuse tõttu (nt soolsus ja mulla veesisaldus). Katsest välja jäetud pilliroostikus ja võsasoos domineerivad üleujutust taluvad liigid; kõrgrohustu kujutab endast maismaa ja märgalade ökosüsteemi vahelist kooslust, ning võsa on täielikult maismaa. Silma looduskaitsealal varuti Põhja-Tahu alalt 2018. aasta juunis kolmest valitud taimekooslusest 15 mätast (suurus 50 x 70 cm, paksus 30 cm). Mesokosmi katse varustus koosnes mahutitest (90L, mõõtmed 56 x 79 x 32 cm), mis olid täidetud 2:1:1 mullaseguga, mis koosneb pestud sõmera struktuuriga liivast, savist ja kompostist, mis on väga sarnane märgala põhjasubstraadiga. Mahutid numereeriti ja varustati vastava tähisega. Mahutid asusid kogu katse jooksul samal kohal. Katse käiku hinnati alalise gradueeritud 50 cm2 kvadraadi abil, mis jaotati 25 kvadraadiks (10 x 10 cm), ja määrati kindlaks muutused esinevate taimeliikide arvukuses pinnakatte pindala järgi (katteprotsent). Katse kestis kolme aastat veetaseme ja soolsuse tingimustes, mis tuletati kliimamuutuste prognoosidest 2100. aastaks. Liikide arvukus ja liigirikkus arvutati 2018., 2019. ja 2020. aastaks iga taimekoosluse kohta eraldi. Liigirikkuse erinevusi aastati ja kasvutingimuste suhtes hinnati Kruskal Wallise testiga, mis põhineb Bonferroni kohandustega Dunni testil, et tuvastada liigirikkuse erinevusi igal aastal. Liigilise arvukuse esitamiseks kasutati arvukuse kõveraid. Taimekoosluse koostise erinevuste uurimiseks kasutati permutatsioonilist mitmemõõtmelist analüüsi Bray-Curtise erinevusega. Aasta ristmõju analüüsis käsitleti töötlemisviisi fikseeritud mõju ja valimeid juhusliku mõjuna. Tulemused ja järeldused Rannaniitudel hinnati taimekoosluste levikut, maismaa biomassi ja taimestiku vertikaalset struktuuri. Fleissi kapa kordaja 0,89 põhjal kaardistati põhjalikult taimekooslused (I). Otsustusmetsa klassijärgsed vead näitavad, et homogeensema struktuuriga piirkondi on kergem klassifitseerida kui keerulise struktuuriga koosluseid. Otsustusmetsa algoritmi jõudlusanalüüs näitas, et biomassi hindamisel saadi parim tulemus, kui multispektraalne info kombineeriti fotogramm-meetriliselt loodud digitaalse maastikumudeliga (DTM, ingl digital terrain model) (II). Tulemused viitavad sellele, et mitme anduri kombinatsiooni saab kasutada ökosüsteemi omaduste mõõtmiseks, mida ainult spektraalinformatsiooni analüüsides ei pruugi tuvastada. Siinse uuringu maapealse biomassi prognooside suur täpsus näitab, et rannaniitude jälgimisel on kaugseire UAV-ga sobiv meetod. Struktuurianalüüsi tulemused näitasid, mil määral mõjutab biomassi jaotust karjatamise kestus ja heterogeensus. Pidevalt majandatavatele rohumaadele on iseloomulikud suuremad ja homogeensemad alad (II). Üldine lineaarne mudelianalüüs ja Mann-Whitney u-testid näitasid, kuidas taimtoidulised liigid mõjutavad rohumaa struktuuri. Rohumaad, millel karjatatakse erinevaid taimtoidulisi, on mitmekesisema struktuuriga kui veiste karjamaa (II). Mesokosmi katse tulemused näitasid, et kõigis kolmes Läänemere ranniku märgalade koosluses ilmnesid aja jooksul vee- ja soolsusrežiimis märkimisväärsed muutused, mis tõi esile taimeliikide reaktsiooni keskkonnamuutuste suhtes (III). Pioneerliikidega paljakutel suurenes liigirikkus ja taimkate kõigi keskkonnamuutuste korral, sellega võrreldes esines madalal rannikul ja kõrgrannikul nii veetaseme kui ka soolsusega seotud muutusi vähemal määral. Pioneerliikidega paljakuid mõjutab enamasti soolsus, seda isegi peamiselt sõmerast, keskmise fraktsiooniga ja peenest liivast koosnevas pinnases, mis säilitab vähem toitaineid kui peenema fraktsiooniga muld. Spergularia marina ja Glaux maritima aitasid kaasa liigirikkuse suurenemisele mulla suurenenud ja vähenenud soolsuse tingimustes. Üldiselt ei ilmnenud madala ranniku ja kõrgranniku taimekooslustes soolsuse muutumise korral olulisi muutusi võrreldes kontrollkatsega. Nendes kooslustes on liike, mis kasvavad nii soolases kui ka mittesoolases keskkonnas. Veetaseme muutus mõjutas pioneerliikidega paljakute taimekooslust sarnaselt soolsuse muutmisega. Selle koosluse liigirikkus suurenes kõrgema veetaseme korral, võrreldes kontrollkatsega. Kõrgema veetasemega kohanenud liike nagu Eleocharis palustris esines kõrgenenud veetaseme korral kolmandal aastal rohkem; alanenud vees leidus katse lõpus rohkem väiksema veevajadusega liike, nagu Glaux maritima ja Centaurium littorale. Madalal rannikul registreeriti madalama veetaseme korral liigirikkuse muutus, võrreldes kontrollkatsega. Aja jooksul toimuv liikide varieeruvus ilmnes vähese pinnakatvusega liikide puhul, nt ahenesid Carex flacca ja Triglochin palustris´e kasvukohad. Madal rannik asub veetasemelt pioneerliikidega paljaku ja kõrgranniku vahel ning see võib seletada, miks sealsed liigid taluvad mulla mitmesuguseid niiskustingimusi. Kõrgranniku koosluses vähenes kõrgenenud veetaseme korral liikide arv ja sellest tulenevalt ka liigirikkus; sealjuures laienesid vähese pinnakatvuse ja madala veetasemega kohastunud liikide Stellaria graminea ja Viola canina kasvukohad. See uurimus näitas, et ökoloogilistes uuringutes võib erinevate metoodikate kombinatsioon osutuda tõhusaks. Vaid vähestes uuringutes kombineeritakse ökosüsteemiprotsesside mõistmiseks erinevaid lähenemisviise, nt kaugseiret ja katseplatvorme, antud töös mesokosmi katset. Uued tehnoloogilised edusammud kaugseire vallas võivad lahendada küsimusi, millele vastuse leidmine traditsiooniliste ökoloogiliste meetodite abil oleks keeruline või ebapraktiline. Samas on traditsioonilise lähenemisviisiga, nt mesokosmi katsega saadud teadmised uue tehnoloogilise potentsiaali rakendamiseks väga vajalikud. Uurimus näitas, et UAV on sobiv vahend rannikurohumaade struktuuri ja taimekoosluste leviku täpse eraldusvõimega kaartide koostamiseks. Teisest küljest aitab mesokosmi katse mõista taimekoosluse koostise muutusi eri veetaseme ja soolsuse tingimustes.Publication of this thesis is supported by the Estonian University of Life Sciences and by the Doctoral School of Earth Sciences and Ecology created under the auspices of the European Social Fund

Remote Sensing of the Ecosystem Impact of Invasive Alien Plant Species

Invasive Pflanzenarten können Ökosysteme durch Beeinflussung von einheimischen Pflanzengesellschaften und Ökosystemprozessen verändern. Solche Ökosystemauswirkungen wurden mit Hilfe von Experimenten oder Feldaufnahmen umfassend untersucht. Großflächige Auswirkungen, zum Beispiel auf Habitat- oder Landschaftsebene wurden bisher jedoch kaum untersucht. Mit Hilfe von Fernerkundung können räumlich explizite Informationen über die Verteilung von Arten und Ökosystemeigenschaften erfasst werden und somit die Lücke in der Erforschung der großflächigen Auswirkungen invasiver Arten geschlossen werden. Bisher wurde Fernerkundung vor allem zur Kartierung von Vorkommen invasiver Pflanzenarten eingesetzt, jedoch nur selten zur Abschätzung ihrer Auswirkungen. Diese Arbeit zielt darauf ab, das Potential der Fernerkundung für die Bewertung von Ökosystemauswirkungen invasiver Pflanzenarten zu analysieren. Zu diesem Zweck wurden drei Forschungsarbeiten angefertigt, die verschiedene Aspekte dieses Potenzials beleuchten: (1) Die Ermittlung von Vegetationseigenschaften in von Invasionen betroffenen Ökosystemen, (2) die Analyse von Auswirkungen invasiver Arten auf unterschiedlichen räumlichen Skalen und (3) eine räumlich explizite Darstellung von Ökosystemauswirkungen invasiver Pflanzenarten. Die erste Studie beschäftigt sich mit der Kartierung von Blattstickstoff (N) und -phosphorgehalten (P) in einem Laubmischwald mit Vorkommen der frühblühenden Traubenkirsche (Prunus serotina Ehrh.). Für die Kartierung wurden hyperspektrale und Laserscanning (LiDAR) Daten kombiniert. Die Studie ergab, dass die Bestimmung von N und P aus hyperspektalen Fernerkundungdaten in Baumkronen mit hoher struktureller Heterogenität erschwert wird. Allerdings konnte auch ein Zusammenhang zwischen chemischer Zusammensetzung und der Struktur des Kronendaches festgestellt werden. So konnten die von LiDAR-Daten abgeleiteten Strukturinformationen genutzt werden, um die Vorhersagen von N und P zu verbessern. In der zweiten Studie wurden aus Fernerkundungsdaten erstellte Karten von Ökosystemeigenschaften genutzt, um Gebiete mit und ohne P. serotina zu vergleichen. Die Karten umfassten N und P, sowie das N:P-Verhältnis von Blättern, das Holzvolumen und den Blattflächenindex (LAI). Es wurden sowohl Unterschiede in den Werten von Blattinhaltsstoffen als auch in der Waldstruktur für Standorte mit und ohne P. serotina festgestellt. Diese Unterschiede waren auch auf Bestandsebene erkennbar, wenn auch in geringem Maße. In der dritten Studie wurden hyperspektrale Luftbilder verwendet um die prozentuale Deckung des Kaktusmooses (Campylopus introflexus (Hedw.) Brid.) in einem Dünenökosystem großflächig zu kartieren. Darüber hinaus wurde der Zusammenhang zwischen dem Deckungsgrad von C. introflexus und der Artenvielfalt von Pflanzen untersucht. In Kombination wurden diese Ergebnisse verwendet, um potenzielle Bereiche mit hohen Auswirkungen zu kennzeichnen. Basierend auf diesen drei Studien wurden in dieser Arbeit zwei grundlegende methodische Ansätze zur Analyse von Ökosystemauswirkungen invasiver Pflanzenarten per Fernerkundung identifiziert und angewandt. Der erste Ansatz besteht darin, mit Hilfe von Fernerkundung erstellte Karten von Ökosystemeigenschaften zu verwenden, um diese Eigenschaften in Abhängigkeit des Vorkommens invasiver Arten auszuwerten. Wie gezeigt werden konnte, ist dies auch für große Flächen, beispielsweise auf der Habitat- oder Landschaftsebene, möglich. Somit kann Fernerkundung zu einem besseren Verständnis der Auswirkungen von invasiven Arten beitragen. Der zweite Ansatz basiert auf der Kartierung von Abundanzen invasiver Pflanzenarten. Diese können als Indikator für die Stärke der Auswirkungen genutzt werden. Die resultierenden Karten können verwendet werden, um Bereiche mit hohen Auswirkungen zu identifizieren. Darüber hinaus ermöglicht dieser zweite Ansatz den Vergleich der Auswirkungen zwischen verschiedenen Arten oder Lebensraumtypen und kann somit wertvolle Informationen für Managemententscheidungen liefern. Da die Ableitung vieler Ökosystemeigenschaften aus Fernerkundungsdaten nach wie vor eine Herausforderung darstellt, sollte die zukünftige Forschung darauf abzielen, die Zusammenhänge zwischen den Eigenschaften und der Reflektanz der Vegetation besser zu verstehen. Dies ist eine wesentliche Voraussetzung für eine zuverlässige Vorhersage über verschiedene Lebensräume hinweg. Zukünftige Fernerkundungsstudien, mit dem Ziel invasive Arten zu kartieren, sollten sich auf die Vorhersage von Deckungsgraden konzentrieren. Darüber hinaus sind generalisierte Verfahren wünschenswert, die eine erfolgreiche Identifizierung von Arten unter verschiedenen ökologischen Gegebenheiten gewährleisten. Nicht zuletzt sollte diese Arbeit Invasionsökologen ermutigen, existierende Fernerkundungsprodukte häufiger zu verwenden, um großflächige Auswirkungen von invasiven Pflanzenarten auf Ökosysteme zu analysieren

Predicting Distributions of Estuarine Associated Fish and Invertebrates in Southeast Alaska

Thesis (Ph.D.) University of Alaska Fairbanks, 2013Estuaries in Southeast Alaska provide habitat for juveniles and adults of several commercial fish and invertebrate species; however, because of the area's size and challenging environment, very little is known about the spatial structure and distribution of estuarine species in relation to the biotic and abiotic environment. This study uses advanced machine learning algorithms (random forest and multivariate random forest) and landscape and seascape-scale environmental variables to develop predictive models of species occurrence and community composition within Southeast Alaskanestuaries. Species data were obtained from trawl and seine sampling in 49 estuaries throughout the study area. Environmental data were compiled and extracted from existing spatial datasets. Individual models for species occurrence were validated using independent data from seine surveys in 88 estuaries. Prediction accuracy for individual species models ranged from 94% to 63%, with 76% of the fish species models and 72% of the invertebrate models having a predictive accuracy of 70% or better. The models elucidated complex species-habitat relationships that can be used to identify habitat protection priorities and to guide future research. The multivariate models demonstrated that community composition was strongly related to regional patterns of precipitation and tidal energy, as well as to local abundance of intertidal habitat and vegetation. The models provide insight into how changes in species abundance are influenced by both environmental variation and the co-occurrence of other species. Taxonomic diversity in the region was high (74%) and functional diversity was relatively low (23%). Functional diversity was not linearly correlated to species richness, indicating that the number of species in the estuary was not a good predictor of functional diversity or redundancy. Functional redundancy differed across estuary clusters, suggesting that some estuaries have a greater potential for loss of functional diversity with species removal than others

Mapping plant diversity and composition across North Carolina Piedmont forest landscapes using LiDAR-hyperspectral remote sensing

Forest modification, from local stress to global change, has given rise to efforts to model, map, and monitor critical properties of forest communities like structure, composition, and diversity. Predictive models based on data from spatially-nested field plots and LiDAR-hyperspectral remote sensing systems are one particularly effective means towards the otherwise prohibitively resource-intensive task of consistently characterizing forest community dynamics at landscape scales. However, to date, most predictive models fail to account for actual (rather than idealized) species and community distributions, are unsuccessful in predicting understory components in structurally and taxonomically heterogeneous forests, and may suffer from diminished predictive accuracy due to incongruity in scale and precision between field plot samples, remotely-sensed data, and target biota of varying size and density. This three-part study addresses these and other concerns in the modeling and mapping of emergent properties of forest communities by shifting the scope of prediction from the individual or taxon to the whole stand or community. It is, after all, at the stand scale where emergent properties like functional processes, biodiversity, and habitat aggregate and manifest. In the first study, I explore the relationship between forest structure (a proxy for successional demographics and resource competition) and tree species diversity in the North Carolina Piedmont, highlighting the empirical basis and potential for utilizing forest structure from LiDAR in predictive models of tree species diversity. I then extend these conclusions to map landscape pattern in multi-scale vascular plant diversity as well as turnover in community-continua at varying compositional resolutions in a North Carolina Piedmont landscape using remotely-sensed LiDAR-hyperspectral estimates of topography, canopy structure, and foliar biochemistry. Recognizing that the distinction between correlation and causation mirrors that between knowledge and understanding, all three studies distinguish between prediction of pattern and inference of process. Thus, in addition to advancing mapping methodologies relevant to a range of forest ecosystem management and monitoring applications, all three studies are noteworthy for assessing the ecological relationship between environmental predictors and emergent landscape patterns in plant composition and diversity in North Carolina Piedmont forests.Doctor of Philosoph