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Institut for Ecoscience

Modellering og forudsigelse

Statistisk modellering og prognoser er afgørende for at anvende videnskabelig viden til at løse økologiske og miljømæssige spørgsmål.

Enhver troværdig prognose bør ledsages af en vurdering af usikkerhed, og ved at bruge avancerede statistiske teknikker tilstræber vi at kvantificere usikkerheden, samtidig med at vi undersøger, hvordan man bliver bedre til at lave prognoser.

Publikationer fra PURE

Sortér efter: Dato | Forfatter | Titel

Damgaard, C. (2024). Ecosystem dynamics in wet heathlands: spatial and temporal effects of environmental drivers on the vegetation. bioRxiv. https://doi.org/10.1101/2024.08.06.606794

Damgaard, C. (2024). Om brug af eksperter i miljø- og naturforvaltningen. https://cfddiverse.blogspot.com/2024/03/om-brug-af-eksperter-i-milj-og.html

Moeslund, J. E. & Damgaard, C. F. (2024). Predicting Abiotic Soil Characteristics Using Sentinel-2 at Nature-Management-Relevant Spatial Scales and Extents. Remote Sensing, 16(16), Artikel 3094. https://doi.org/10.3390/rs16163094

Damgaard, C., Bak, J. L., Strandberg, M. & Hansen, R. R. (2024). The resilience of heathland ecosystems: A working hypothesis. Acta Oecologica, 125, Artikel 104037. https://doi.org/10.1016/j.actao.2024.104037

Damgaard, C. (2023). Spatio-temporal modelling of the effect of environmental and land-use factors on species-rich calcareous grasslands. Basic and Applied Ecology, 72, 22-29. https://doi.org/10.1016/j.baae.2023.07.001

Sørensen, P. B., Damgaard, C., Bjerg, P. L., Andersen, H. E., Holm, P. E., Bak, J. L., Rasmussen, D. & Kjeldgaard, A. (2023). Videreudvikling af model for metaller i vandløbsvand: MetalStat. Aarhus University, DCE - Danish Centre for Environment and Energy. Videnskabelig rapport fra DCE - Nationalt Center for Miljø og Energi Bind 522 http://dce2.au.dk/pub/SR522.pdf

Damgaard, C. (2022). Adaptive management plans rooted in quantitative ecological predictions of ecosystem processes: putting monitoring data to practical use. Environmental Conservation, 49(1), 27 - 32. https://doi.org/10.1017/S0376892921000357

Løvschal, M. & Damgaard, C. (2022). Mapping the ecological resilience of Atlantic postglacial heathlands. Journal of Applied Ecology, 59(11), 2825-2838. https://doi.org/10.1111/1365-2664.14278

Damgaard, C. (2022). Spatio-temporal modelling of the effect of selected environmental and land-use factors on acid grassland vegetation. Journal of Plant Ecology, 15(2), 253-264. https://doi.org/10.1093/jpe/rtab088

Damgaard, C. (2022). Troværdige økologiske prognoser skal baseres på statistiske modeller fremfor kvalitative vurderinger. Altinget.

Sørensen, P. B., Damgaard, C., Løgstrup Bjerg, P., Andersen, H. E., Holm, P. E., Tornbjerg, H., Bak, J. L., Heckrath, G. J., Kjeldgaard, A. & Fauser, P. (2022). Udvikling af model til understøttelse af vandplanlægningen i fastlæggelsen af koncentrationsniveauer af metaller i danske vandløb. Aarhus University, DCE - Danish Centre for Environment and Energy. Videnskabelig rapport fra DCE - Nationalt Center for Miljø og Energi Nr. 484 https://dce2.au.dk/pub/SR484.pdf

Damgaard, C. (2021). Integrating hierarchical statistical models and machine-learning algorithms for ground-truthing drone images of the vegetation: taxonomy, abundance and population ecological models. Remote Sensing, 13(6), Artikel 1161. https://doi.org/10.3390/rs13061161

Damgaard, C. (2021). Use ecological data from national monitoring to help set-up local adaptive management plans.

Damgaard, C. (2020). Measurement uncertainty in ecological and environmental models. Trends in Ecology & Evolution, 35(10), 871-873. https://doi.org/10.1016/j.tree.2020.07.003

Damgaard, C. (2020). Miljømodellering og usikkerheder. Vand & Jord, 27(3), 115-116.

Damgaard, C. F. (2019). Spatio-Temporal Structural Equation Modeling in a Hierarchical Bayesian Framework: What Controls Wet Heathland Vegetation? Ecosystems, 22(1), 152-164. https://doi.org/10.1007/s10021-018-0259-8

Damgaard, C., Strandberg, M. T., Kjær, C. & Sørensen, P. B. (2019). Use "Risk of system failure" rather than additive aggregation methods of indicators when assessing habitat quality. Ecological Indicators, 107, Artikel 105564. https://doi.org/10.1016/j.ecolind.2019.105564

Revideret 03.09.2024

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Else Vihlborg Staalsen