Faglige interesser
Trond forsker på naturvariasjon og økologi på landskapsnivå, og studerer endringer i landskap og arealbruk over tid. Han arbeider med en PhD i økologi (2016-2020). Han arbeider med kartlegging av landskapstyper og geografisk fordeling av økosystemer og landskapselementer og i Norge. Trond deltar i arbeidet med utvikling av landskapsnivået i Artsdatabankens naturtype- og naturbeskrivelsessystem, Natur i Norge.
Bakgrunn
Trond er utdannet Cand. agric. ved Norges Landbrukshøgskole og har videreutdanning i vegetasjonsøkologi, landskapsøkologi, fjernmåling, økologisk modellering, og bruk av statistiske metoder i analyser av biologiske data. Han har arbeidet med arealplanlegging, miljøkonsekvensutredninger og naturforvaltning med erfaring fra konsulentvirksomhet og fra offentlig forvaltning.
Samarbeid
Trond sitt Ph.D.-prosjekt "Kartlegging og analyse av landskapsmessig mangfold i Norge", inngår i ordningen "Offentlig sektor Ph.D." i regi av Norges Forskningsråd. Prosjektet gjennomføres som et tverrfaglig samarbeidsprosjekt mellom Miljødirektoratet, Universitetet i Oslo, Norsk Institutt for naturforskning og Artsdatabanken.
Emneord:
Landskap,
Økologi,
Modellering
Publikasjoner
-
Horvath, Peter; Halvorsen, Rune; Simensen, Trond & Bryn, Anders
(2021).
A comparison of three ways to assemble wall-to-wall maps from distribution models of vegetation types.
GIScience & Remote Sensing.
ISSN 1548-1603.
58(8),
s. 1458–1476.
doi:
10.1080/15481603.2021.1996313.
Fulltekst i vitenarkiv
Vis sammendrag
Distribution modeling methods are used to provide occurrence probability surfaces for modeled targets. While most often used for modeling species, distribution modeling methods can also be applied to vegetation types. However, surfaces provided by distribution modeling need to be transformed into classified wall-to-wall maps of vegetation types to be useful for practical purposes, such as nature management and environmental planning. The paper compares the performance of three methods for assembling predictions for multiple vegetation types, modeled individually, into a wall-to-wall map. The authors used grid-cell based probability surfaces from distribution models of 31 vegetation types to test the three assembly methods. The first, a probability-based method, selected for each grid cell the vegetation type with the highest predicted probability of occurrence in that cell. The second, a performance-based method, assigned the vegetation types, ordered from high to low model performance, to a fraction of the grid cells given by the vegetation type’s prevalence in the study area. The third, a prevalence-based method, differed from the performance-based method by assigning vegetation types in the order from low to high prevalence. Thus the assembly methods worked in two principally different ways: the probability-based method assigned vegetation types to grid cells in a cell-by-cell manner, and both the performance-based method and prevalence-based method assigned them in a type-by-type manner. All methods were evaluated by use of reference data collected in the field, more or less independently of the data used to parameterize the vegetation-type models. Quantity, allocation, and total disagreement, as well as proportional dissimilarity metrics, were used for evaluation of assembly methods. Overlay analysis showed 38.1% agreement between all three assembly methods. The probability-based method had the lowest total disagreement with, and proportional dissimilarity from, the reference datasets, but the differences between the three methods were small. The three assembly methods differed strongly with respect to the distribution of the total disagreement on its quantity and allocation components: the cell-by-cell assignment method strongly favored allocation disagreement and the type-by-type methods strongly favored quantity disagreement. The probability-based method best reproduced the general pattern of variation across the study area, but at the cost of many rare vegetation types, which were left out of the assembled map. By contrast, the prevalence-based and performance-based methods represented vegetation types in accordance with nationwide area statistics. The results show that maps of vegetation types with wall-to-wall coverage can be assembled from individual distribution models with a quality acceptable for indicative purposes, but all the three tested methods currently also have shortcomings. The results also indicate specific points in the methodology for map assembly that may be improved.
area frame survey, assembly strategies, distribution modeling, spatial probabilities, vegetation mapping, vegetation types
-
-
Halvorsen, Rune; Skarpaas, Olav; Bryn, Anders; Bratli, Harald; Erikstad, Lars & Simensen, Trond
[Vis alle 7 forfattere av denne artikkelen]
(2020).
Towards a systematics of ecodiversity: the Ecosyst framework.
Global Ecology and Biogeography.
ISSN 1466-822X.
29(11),
s. 1887–1906.
doi:
10.1111/geb.13164.
Fulltekst i vitenarkiv
Vis sammendrag
Background
Although a standard taxonomy of organisms has existed for nearly 300 years, no consensus has yet been reached on principles for systematization of ecological diversity (i.e., the co‐ordinated variation of abiotic and biotic components of natural diversity). In a rapidly changing world, where nature is under constant pressure, standardized terms and methods for characterization of ecological diversity are urgently needed (e.g., to enhance precision and credibility of global change assessments).
Aim
The aim is to present the EcoSyst framework, a set of general principles and methods for systematization of natural diversity that simultaneously addresses biotic and abiotic variation, and to discuss perspectives opened by this framework.
Innovation
EcoSyst provides a framework for systematizing natural variation in a consistent manner across different levels of organization. At each ecodiversity level, EcoSyst principles can be used to establish: (a) an extensive attribute system with descriptive variables that cover all relevant sources of variation; (b) a hierarchical‐type system; and (c) a set of guidelines for land‐cover mapping that is consistent across spatial scales. EcoSyst type systems can be conceptualized as multidimensional models, by which a key characteristic (the response) is related to variation in one or more key sources of variation (predictors). EcoSyst type hierarchies are developed by a gradient‐based iterative procedure, by which the “ecodiversity distance” (i.e., the extent to which the key characteristic differs between adjacent candidate types) is standardized and the ecological processes behind observed patterns are explicitly taken into account.
Application
We present “Nature in Norway” (NiN), an implementation of the EcoSyst framework for Norway for the ecosystem and landscape levels of ecodiversity. Examples of applications to research and management are given.
Conclusion
The EcoSyst framework provides a theoretical platform, principles and methods that can complement and enhance initiatives towards a global‐scale systematics of ecodiversity.
iodiversity
complex gradient
continuum theory
ecodiversity
ecosystem
geodiversity
gradient
landscape
mapping
typology
-
Simensen, Trond; Horvath, Peter; Vollering, Julien; Erikstad, Lars; Halvorsen, Rune & Bryn, Anders
(2020).
Composite landscape predictors improve distribution models of ecosystem types.
Diversity and Distributions: A journal of biological invasions and biodiversity.
ISSN 1366-9516.
26(8),
s. 928–943.
doi:
10.1111/ddi.13060.
Fulltekst i vitenarkiv
Vis sammendrag
Aim: Distribution modelling is a useful approach to obtain knowledge about the spatial
distribution of biodiversity, required for, for example, red-list assessments. While
distribution modelling methods have been applied mostly to single species, modelling
of communities and ecosystems (EDM; ecosystem-level distribution modelling)
produces results that are more directly relevant for management and decision-making.
Although the choice of predictors is a pivotal part of the modelling process, few
studies have compared the suitability of different sets of predictors for EDM. In this
study, we compare the performance of 50 single environmental variables with that
of 11 composite landscape gradients (CLGs) for prediction of ecosystem types. The
CLGs represent gradients in landscape element composition derived from multivariate
analyses, for example “inner-outer coast” and “land use intensity.”
Location: Norway.
Methods: We used data from field-based ecosystem-type mapping of nine ecosystem
types, and environmental variables with a resolution of 100 × 100 m. We built
nine models for each ecosystem type with variables from different predictor sets.
Logistic regression with forward selection of variables was used for EDM. Models
were evaluated with independently collected data.
Results: Most ecosystem types could be predicted reliably, although model performance
differed among ecosystem types. We identified significant differences in predictive
power and model parsimony across models built from different predictor sets.
Climatic variables alone performed poorly, indicating that the current climate alone is
not sufficient to predict the current distribution of ecosystems. Used alone, the CLGs
resulted in parsimonious models with relatively high predictive power. Used together
with other variables, they consistently improved the models.
Main conclusions: Our study highlights the importance of variable selection in EDM.
We argue that the use of composite variables as proxies for complex environmental
gradients has the potential to improve predictions from EDMs and thus to inform
conservation planning as well as improve the precision and credibility of red lists and
global change assessments.conservation planning, distribution modelling, ecosystem classification, ecosystem types,
IUCN Red List of Ecosystems, landscape gradients, spatial prediction, species response curves
-
Se alle arbeider i Cristin
-
-
Landrø, Juliet; Stokke, Bård Gunnar; Forseth, Torbjørn; Sundt-Hansen, Line Elisabeth Breivik; Hagen, Dagmar & Fossøy, Frode
[Vis alle 8 forfattere av denne artikkelen]
(2022).
Lure løsninger! Episode 4 om bærekraft. Sesong 4 episode 5.
[Internett].
Naturligvis : en podkast om natur fra NINA.
Vis sammendrag
Hva kan vi gjøre for å både ha natur og bruke natur? Hele poenget med bærekraftig samfunnsutvikling er at vi fortsatt skal ha det vi trenger, men at ungene våre og andre som kommer etter oss på denne kloden også får dekket sine behov for mat, kraft, byggematerialer og så videre. Og det finnes allerede mange metoder for å bruke naturen på en mer bærekraftig måte, så her får du 9 lure løsninger som det går an å gjennomføre allerede i dag!
-
Erikstad, Lars; Hagen, Dagmar & Simensen, Trond
(2022).
Avslutningsplan for Svea fase 2b. Tilbakeføring av veger og anlegg. Grønt kurs.
-
Simensen, Trond
(2022).
Naturregnskap som verktøy for arealplanlegging.
-
Panzacchi, Manuela; Van Moorter, Bram; Teien, Kristin Thorsrud; Simensen, Trond; Stange, Erik & Chipperfield, Joseph
(2022).
GreenPlan: Launching the "Norwegian Green Infrastructure Network” and decision-support tools for Land Prioritization, Scenario & Impact Assessment
.
-
Simensen, Trond; Panzacchi, Manuela; Van Moorter, Bram & Stange, Erik
(2022).
GreenPlan – The Norwegian Green Infrastructure Network & decision-support tools for Land Prioritization, Scenario, and Impact assessment.
-
Stange, Erik & Simensen, Trond
(2022).
Forslag til nye og forsterkede virkemidler for en bærekraftig arealbruk innenfor rammene av lokalt selvstyre.
-
Panzacchi, Manuela; Van Moorter, Bram; Stange, Erik; Simensen, Trond & Teien, Kristin Thorsrud
(2022).
GreenPlan - Launching the Norwegian Green Infrastructure Network & decision-support tools for Land Prioritization, Scenario and Impact assessment.
-
Kyrkjeeide, Magni Olsen & Simensen, Trond
(2022).
Planer som tar hensyn til naturmangfold og klima.
-
Simensen, Trond
(2022).
Bærekraftig arealbruk innenfor rammene av lokalt selvstyre.
-
Teien, Kristin Thorsrud; Barton, David Nicholas; Simensen, Trond; Venter, Zander; Sydenham, Markus A. K. & Kolstad, Anders Lorentzen
(2021).
Hva er naturregnskap og hvordan kan det brukes?
-
-
Simensen, Trond Aalvik
(2021).
History, state of art and future of landscape ecological research.
-
Simensen, Trond
(2019).
Nordlige barskoger kan bli et usikkert karbonlager.
Aftenposten Viten.
ISSN 2464-3033.
s. 18–19.
-
Halleraker, Jo Halvard; Simensen, Trond; Løbersli, Else Marie; Nybø, Signe & Alfredsen, Knut
(2019).
Bridging ecology-science with decision making - despite different understanding of the science policy interface .
Vis sammendrag
https://site.uit.no/nof2019/en/sample-page/
-
Simensen, Trond; Halvorsen, Rune; Erikstad, Lars & Bakkestuen, Vegar
(2018).
A gradient perspective on landscape classification.
-
Asmervik, Sigmund & Simensen, Trond
(2005).
The roots of ecourbanism.
-
Simensen, Trond & Hofstad, Christian
(2003).
TDR 03.
Vis sammendrag
Det utstilte kunstverket beskriver, forklarer og illustrerer et nytt planverktøy for bærekraftig byutvikling. Forslaget beskriver et planverktøy som griper inn i eiendomsforholdene gjennom en mekansime for overføring av utbyggingsrettigheter.
-
Hagen, Dagmar; Skrindo, Astrid Brekke; Evju, Marianne; Nybø, Signe; Simensen, Trond & Kolstad, Anders Lorentzen
(2022).
Nye virkemidler i arealforvaltningen – naturrestaurering, arealregnskap og naturavgift.
Norsk institutt for naturforskning (NINA).
ISSN 978-82-426-4885-3.
Vis sammendrag
naturregnskap, restaureringsbehov, restaureringskostnader, arealregnskap, økologisk tilstand, naturnøytralitet
-
Se alle arbeider i Cristin
Publisert 3. mai 2018 12:04
- Sist endret 31. jan. 2022 09:10