TY  - JOUR
A1  - Marquart, Arnim
A1  - Eldridge, David J.
A1  - Geissler, Katja
A1  - Lobas, Christoph
A1  - Blaum, Niels
T1  - Interconnected effects of shrubs, invertebrate-derived macropores and soil texture on water infiltration in a semi-arid savanna rangeland
JF  - Land degradation & development
N2  - Many semi arid savannas are prone to degradation, caused for example, by overgrazing or extreme climatic events, which often lead to shrub encroachment. Overgrazing by livestock affects vegetation and infiltration processes by directly altering plant composition (selective grazing) or by impacting soil physical properties (trampling). Water infiltration is controlled by several parameters, such as macropores (created by soil-burrowing animals or plant roots) and soil texture, but their effects have mostly been studied in isolation. Here we report on a study, in which we conducted infiltration experiments to analyze the interconnected effects of invertebrate-created macropores, shrubs and soil texture (sandy soil and loamy sand) on infiltration in two Namibian rangelands. Using structural equation modeling, we found a direct positive effect of shrub size on infiltration and indirectly via invertebrate macropores on both soil types. On loamy sands this effect was even stronger, but additionally, invertebrate-created macropores became relevant as a direct driver of infiltration. Our results provide new insights into the effects of vegetation and invertebrates on infiltration under different soil textures. Pastoralists should use management strategies that maintain a heterogeneous plant community that supports soil fauna to sustain healthy soil water dynamics, particularly on soils with higher loam content. Understanding the fundamental functioning of soil water dynamics in drylands is critical because these ecosystems are water-limited and support the livelihoods of many cultures worldwide.
KW  - hydrology
KW  - infiltration
KW  - invertebrate macropores
KW  - shrub-encroachment
KW  - soil function
KW  - soil texture
Y1  - 2020
U6  - https://doi.org/10.1002/ldr.3598
SN  - 1085-3278
SN  - 1099-145X
VL  - 31
IS  - 16
SP  - 2307
EP  - 2318
PB  - Wiley
CY  - Chichester, Sussex
ER  - 
TY  - GEN
A1  - Mooij, Wolf M.
A1  - Trolle, Dennis
A1  - Jeppesen, Erik
A1  - Arhonditsis, George B.
A1  - Belolipetsky, Pavel V.
A1  - Chitamwebwa, Deonatus B. R.
A1  - Degermendzhy, Andrey G.
A1  - DeAngelis, Donald L.
A1  - Domis, Lisette Nicole de Senerpont
A1  - Downing, Andrea S.
A1  - Elliott, J. Alex
A1  - Fragoso Jr., Carlos Ruberto
A1  - Gaedke, Ursula
A1  - Genova, Svetlana N.
A1  - Gulati, Ramesh D.
A1  - Håkanson, Lars
A1  - Hamilton, David P.
A1  - Hipsey, Matthew R.
A1  - ‘t Hoen, Jochem
A1  - Hülsmann, Stephan
A1  - Los, F. Hans
A1  - Makler-Pick, Vardit
A1  - Petzoldt, Thomas
A1  - Prokopkin, Igor G.
A1  - Rinke, Karsten
A1  - Schep, Sebastiaan A.
A1  - Tominaga, Koji
A1  - Van Dam, Anne A.
A1  - Van Nes, Egbert H.
A1  - Wells, Scott A.
A1  - Janse, Jan H.
T1  - Challenges and opportunities for integrating lake ecosystem modelling approaches
T2  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
N2  - A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1326 
KW  - aquatic
KW  - food web dynamics
KW  - plankton
KW  - nutrients
KW  - spatial
KW  - lake
KW  - freshwater
KW  - marine
KW  - community
KW  - population
KW  - hydrology
KW  - eutrophication
KW  - global change
KW  - climate warming
KW  - fisheries
KW  - biodiversity
KW  - management
KW  - mitigation
KW  - adaptive processes
KW  - non-linear dynamics
KW  - analysis
KW  - bifurcation
KW  - understanding
KW  - prediction
KW  - model limitations
KW  - model integration
Y1  - 2010
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-429839
SN  - 1866-8372
IS  - 1326
ER  - 
TY  - JOUR
A1  - Mooij, Wolf M.
A1  - Trolle, Dennis
A1  - Jeppesen, Erik
A1  - Arhonditsis, George B.
A1  - Belolipetsky, Pavel V.
A1  - Chitamwebwa, Deonatus B. R.
A1  - Degermendzhy, Andrey G.
A1  - DeAngelis, Donald L.
A1  - Domis, Lisette Nicole de Senerpont
A1  - Downing, Andrea S.
A1  - Elliott, J. Alex
A1  - Fragoso Jr, Carlos Ruberto
A1  - Gaedke, Ursula
A1  - Genova, Svetlana N.
A1  - Gulati, Ramesh D.
A1  - Håkanson, Lars
A1  - Hamilton, David P.
A1  - Hipsey, Matthew R.
A1  - ‘t Hoen, Jochem
A1  - Hülsmann, Stephan
A1  - Los, F. Hans
A1  - Makler-Pick, Vardit
A1  - Petzoldt, Thomas
A1  - Prokopkin, Igor G.
A1  - Rinke, Karsten
A1  - Schep, Sebastiaan A.
A1  - Tominaga, Koji
A1  - Van Dam, Anne A.
A1  - Van Nes, Egbert H.
A1  - Wells, Scott A.
A1  - Janse, Jan H.
T1  - Challenges and opportunities for integrating lake ecosystem modelling approaches
JF  - Aquatic ecology
N2  - A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
KW  - aquatic
KW  - food web dynamics
KW  - plankton
KW  - nutrients
KW  - spatial
KW  - lake
KW  - freshwater
KW  - marine
KW  - community
KW  - population
KW  - hydrology
KW  - eutrophication
KW  - global change
KW  - climate warming
KW  - fisheries
KW  - biodiversity
KW  - management
KW  - mitigation
KW  - adaptive processes
KW  - non-linear dynamics
KW  - analysis
KW  - bifurcation
KW  - understanding
KW  - prediction
KW  - model limitations
KW  - model integration
Y1  - 2010
U6  - https://doi.org/10.1007/s10452-010-9339-3
SN  - 1573-5125
SN  - 1386-2588
VL  - 44
SP  - 633
EP  - 667
PB  - Springer Science + Business Media B.V.
CY  - Dordrecht
ER  -