We simplified the model compared to Charisma 2.0 to reduce the number of
parameters (47 species-specific parameters in Charisma 2.0) by excluding
processes of i) carbonate limitation because the lakes we simulated were
not carbonate limited and no proof was found for the process; ii)
spatial processes like seed dispersal, which we assume not to limit
occurrence of macrophytes within the regional context
(Alahuhta et al.,
2020); iii) water level fluctuation, because corresponding data are not
available for all lake and because the macrophyte mappings correspond
always to the actual water level; iv) interspecific competition and v)
herbivory because both processes are not relevant for modelling the
eco-physiological constraints on the potential distribution of species;
and vi) vegetative reproduction because we focus on eco-physiological
constraints rather than demographic or dispersal constraints. After
that, MGM still includes 28 species-specific parameters. The detailed
Overview, Design concepts, Detail (ODD) protocol (Grimm et al., 2006,
2010) for MGM (see Appendix S3) and the model code (see Appendix S4) is
open access and available as Supporting Information and on GitHub.
Datasets
The available dataset from the German federal state of Bavaria consists
of large lakes (>0.5 km² surface area). We selected all
lakes of natural origin (without artificial influence on water level
fluctuation and not of artificial origin) and with a minimal maximal
depth of 9 m. Their locations are shown in Figure 1c. They are all
carbonate-rich and stratified (formation of different thermal layers
during summer), but provide a broad range of environmental conditions,
e.g. from turbid to clear, or from cold to warm waters. Macrophyte
occurrence in four water depths (0-1m; 1-2m; 2-4m; >4m) and
monthly physical-chemical measurements (e.g. water temperature,
Ptot, and Secchi depth) are recorded in all these large
lakes for the EU-Water Framework Directive (WFD) monitoring and are
publicly provided by the Bavarian State Office for the Environment underwww.gkd.bayern.de. Secchi depth,
a measure for transparency of water, was converted to kD, the extinction
coefficient of light in water
(Holmes, 1970; Kirk,
1935). Data for irradiance are obtained from the nearest German Weather
Service (DWD) weather station as daily mean (StMWi, 2019).
For each lake we selected the most recent macrophyte mapping data
(2004-2017). We excluded all species that are sterile hybrids, emerged,
with floating leaves, mosses, and are non-rooted. In addition,
“indicator species” were selected, i.e. species that are
oligotraphentic, mesotraphentic, or eutraphentic (Melzer, 1999).
Experimental design
Data preparation and analyses were done in R 4.0.5 (R Core Team, 2021).
We defined parameter spaces for oligotraphentic, mesotraphentic, and
eutraphentic functional types. We selected the parameter spaces based on
parameters of reference species out of the groups for which some of the
parameter values are known. Reference species were Chara aspera (Characeae) for oligotraphentic species, Myriophyllum spicatum (Haloragaceae) and Potamogeton perfoliatus (Potamogetonaceae) for
mesotraphentic species as well as Potamogeton pectinatus (Potamogetonaceae), Elodea nuttallii (Hydrocharitaceae), andNajas intermedia (Hydrocharitaceae) for eutraphentic species. As
the eco-physiological parameters of submerged macrophyte species are not
known, we randomly select 300 parameter combinations for each of the
three parameter spaces defined for oligotraphentic, mesotraphentic, and
eutraphentic functional types respectively (Table 1). Each of the
resulting 900 parameter combinations represent hypothetical, virtual
species.
We select lake parameter values according to measured data within the 30
selected lakes from the available dataset (see description above). The
lakes differ not just in their latitudes, but also in their maximal
summer temperature, nutrient content, and turbidity. Based on these four
parameters we classified the lakes into clear, medium, and turbid lakes
by using a clustering method (hclust, “ward.D2”). All other lake
parameters are identical across lakes and are given in the Supporting
Information (Appendix 2; Table S2.1).
We run the model for all 900 virtual species within the 30 lakes at four
depths (0.5m, 1.5m, 3m, 5m) for 10 years to reach equilibrium (Figure
1d). We select as potential growing species those that can establish a
mean biomass of more than 1 g (per depth and in sum over depth) during
summer (June-August). Those species that passed through the considered
processes within MGM in the given lake conditions are denoted as
“potential species”. Species that passed through all ecological
processes (including demography, interaction, and dispersal) in nature
will be called “observed species”.
Table 1: Parameter spaces for
oligotraphentic, mesotraphentic, and eutraphentic functional types.
Parameters are marked as bold if they are different for the functional
types. The spaces were selected based on reference species from the
groups. Reference species were Chara aspera for oligotraphentic
species, Myriophyllum spicatum and Potamogeton perfoliatus for mesotraphentic species, Potamogeton pectinatus, Elodea
nuttallii, and Najas intermedia for eutraphentic species.