scholarly journals Using ecosystem simulation models to teach about the ‘resilient nature’ / Χρησιμοποιώντας μοντέλα προσομοίωσης οικοσυστημάτων για τη διδασκαλία της «ελαστικής» φύσης

2017 ◽  
Vol 9 (6Β) ◽  
pp. 224
Author(s):  
Georgios Ampatzidis ◽  
Marida Ergazaki
Keyword(s):  
Simulation Models ◽  
Ecosystem Simulation

Το παρόν άρθρο αφορά την ανάπτυξη μοντέλων NetLogo τα οποία προσομοιώνουν την πορεία οικοσυστημάτων που διαταράσσονται ή προστατεύονται και βασίζονται σε ευρήματα της σύγχρονης οικολογικής έρευνας. Τα τέσσερα μοντέλα που παρουσιάζονται ενσωματώθηκαν σε ένα μαθησιακό περιβάλλον που στοχεύει να υποστηρίξει μη βιολόγους φοιτητές (α) στην αντικατάστασή της ιδέας της «φύσης σε ισορροπία» από τη σύγχρονη ιδέα της «ελαστικής» φύσης, και (β) στην αξιοποίηση αυτής της αντίληψης για την ενίσχυση «δεξιοτήτων συστημικής σκέψης». Κάθε μοντέλο έχει δύο εκδοχές που προσομοιώνουν δύο διαφορετικές εκδοχές της πορείας του οικοσυστήματος ανάλογα με συγκεκριμένες αρχικές συνθήκες ή δράσεις στην προσπάθεια επαναφοράς. Στις μελέτες περίπτωσης που πραγματοποιήσαμε, οι φοιτητές σε τριάδες διερεύνησαν τα μοντέλα με τη βοήθεια φύλλων εργασίας, που τους ζητούσαν να κάνουν προβλέψεις για την πορεία του υπό μελέτη οικοσυστήματος και έπειτα να τις ελέγξουν «τρέχοντας» το μοντέλο. Οι μισές τριάδες των φοιτητών ασχολήθηκαν με την 1η εκδοχή κάθε μοντέλου και οι άλλες μισές με τη 2η . Στο τέλος κάθε μαθήματος, oι δύο διαφορετικές εκδοχές της πορείας του οικοσυστήματος που προσομοίωνε κάθε μοντέλο γίνονταν θέμα συζήτησης όλης της τάξης ώστε να αναδειχθεί η ιδέα της ενδεχομενικότητας. Τα αποτελέσματα της λειτουργίας της τελευταίας εκδοχής του μαθησιακού περιβάλλοντος είναι ενθαρρυντικά και δημοσιεύονται σε άλλα άρθρα μας.

scholarly journals Ecosystem models of Lake Victoria (East Africa): exploring the sensitivity of ecosystem effects of fishing to model choice

2018 ◽  
Author(s):  
Vianny Natugonza ◽  
Cameron Ainsworth ◽  
Erla Sturludóttir ◽  
Laban Musinguzi ◽  
Richard Ogutu-Ohwayo ◽  
...  
Keyword(s):  
Fisheries Management ◽  
Species Interactions ◽  
Lake Victoria ◽  
Simulation Models ◽  
Model Choice ◽  
Ecosystem Models ◽  
Distinct Structure ◽  
Ecosystem Effects ◽  
Ecosystem Simulation ◽  
Ecosystem Level

Ecosystem simulation models are valuable tools for strengthening and promoting ecosystem-based fisheries management (EBFM). However, utility of these models in practical fisheries management is often undermined by lack of simple means to test the effect of uncertainty on model outputs. Recently, the use of multiple ecosystem models has been recommended as an ‘insurance’ against effects of uncertainty that comes with modelling complex systems. The assumption is that if models with different structure and formulation give consistent results, then, policy prescriptions are robust (i.e. less sensitive to model choice). However, information on the behaviour of trends from structurally-distinct ecosystem models with respect to changes in fishing conditions is limited, especially for freshwater systems. In this study, we compared outputs of two ecosystem models, Ecopath with Ecosim (EwE) and Atlantis, for Lake Victoria under different fishing pressure scenarios. We compared model behaviour at the ecosystem level, and also at a level of functional groups. At functional group level, we determined two questions: what is the change in the targeted group, and what are the consequent effects in other parts of the system? Overall results suggest that different model formulations can provide similar qualitative predictions (direction of change), especially for targeted groups with similar trophic interactions and adequate data for parameterization and calibration. However, considerable variations in predictions (where models predict opposite trends) may also occur due to inconsistencies in the strength of the aggregate multi-species interactions between species and models, and not necessarily due to model detail and complexity. Therefore, with more information and data, especially on diet, and comparable representation of feeding interactions across models, ecosystem models with distinct structure and formulation can give consistent policy evaluations for most biological groups.

Machine-learning emulation of a forest biogeochemistry model for efficient biosphere optimization

2020 ◽  
Author(s):  
Nikolay Strigul ◽  
Adam Erickson
Keyword(s):  
Real World ◽  
Biological Diversity ◽  
Natural Capital ◽  
Simulation Models ◽  
Well Being ◽  
Human Populations ◽  
World Systems ◽  
Ecosystem Simulation ◽  
Systems Research ◽  
Forest Biogeochemistry

<div> <div> <div> <p>Management controls the spatial configuration of a number of landscapes globally, from forests to rangelands. The majority of landcover change and all land-use change is the result of human decision-making. As human populations and global temperatures continue to increase, an engineering approach is needed to ensure the persistence of biological diversity and natural capital critical to human well-being. Such an approach may be based on manipulating ecosystems to achieve desired future states, informed by the latest simulation models. Models of the land surface are now being used to inform policy in the form of planning and management practices. This often involves the application of models that include spatial dynamics and operate at a landscape scale. The strong correspondence between the resolution and extent of modeling and management activities at this scale, and ability to efficiently simulate the decadal-to-centennial time-scales of interest, provide managers with a credible scientific tool for anticipating future land states under different scenarios. The importance of such tools to managers has grown dramatically with the challenges posed by anthropogenic climate change. As ecosystem simulation models continually improve in precision, accuracy, and robustness, we posit that models may be mathematically optimized as a basis for optimizing the management of real-world systems. Since current ecosystem simulation models are coarse approximations of highly complex and dynamic real-world systems, such optimizations should ideally account for uncertainty and physical or biochemical constraints, thereby improving the tractability of the optimization problem. In this work, we demonstrate the emulation and optimization of a forest biogeochemistry model from the SORTIE-PPA family of models. In doing so, we provide the first demonstration of the concept of biosphere optimization (Erickson 2015), which may one day be extended to include computational genetic manipulation experiments. To perform this work, we utilize the open-source Earth-systems Research and Development Environment (ERDE) library, which contains built-in functions for performing these and other analyses with land models, with a particular focus on forests.</p> </div> </div> </div>

Forest ecosystem simulation modelling: the role of remote sensing

1999 ◽  
Vol 23 (3) ◽  
pp. 391-423 ◽  
Author(s):  
Neil S. Lucas ◽  
Paul J. Curran
Keyword(s):  
Environmental Changes ◽  
Meteorological Data ◽  
Simulation Models ◽  
Simulation Modelling ◽  
Remotely Sensed ◽  
Picea Sitchensis ◽  
Ecosystem Simulation ◽  
Key Issues ◽  
Key Variables ◽  
Computer Simulation Models

In recent years forest ecosystems have come under increasing pressure from environmental changes such as global warming and the impacts of pollution. Recent research has indicated that computer-simulation models driven by remotely sensed estimates of key variables may be used to assess the spatial impact of global environment changes on forest processes. This article begins with a discussion of key issues related to driving such models with remotely sensed estimates of these key variables. The article then outlines an investigation that examined whether a general ecosystem simulation model (FOREST-BGC), driven by remotely sensed and meteorological data, could be used to estimate forest processes for a Sitka spruce ( Picea sitchensis) plantation in mid-Wales.

Correlation of Pore Structure and Permeability by SEM-Image Analysis

1990 ◽  
Vol 48 (1) ◽  
pp. 428-429
Author(s):  
C. A. Callender ◽  
Wm. C. Dawson ◽  
J. J. Funk
Keyword(s):  
Image Analysis ◽  
Pore Structure ◽  
Imaging System ◽  
Pore Space ◽  
Simulation Models ◽  
Image Analyzer ◽  
Imaging Data ◽  
Sem Images ◽  
Thin Sections ◽  
Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

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