1 LandR Biomass_core Module

1.0.0.1 Authors:

Yong Luo yluo1@lakeheadu.ca [aut], Eliot J B McIntire eliot.mcintire@nrcan-rncan.gc.ca [aut, cre], Ceres Barros ceres.barros@ubc.ca [aut], Alex M. Chubaty achubaty@for-cast.ca [aut], Ian Eddy ian.eddy@nrcan-rncan.gc.ca [ctb], Jean Marchal jean.d.marchal@gmail.com [ctb]

This documentation is work in progress. Potential discrepancies and omissions may exist for the time being. If you find any, contact us using the “Get help” link above.

1.1 Module Overview

1.1.1 Quick links

• General functioning

• List of input objects

• List of parameters

• List of outputs

• Simulation flow and module events

1.1.2 Summary

LandR Biomass_core (hereafter Biomass_core) is the core forest succession simulation module of the LandR ecosystem of SpaDES modules (see Chubaty & McIntire 2019). It simulates tree cohort ageing, growth, mortality and competition for light resources, as well as seed dispersal (Fig. 1.1), in a spatially explicit manner and using a yearly time step. The model is based on the LANDIS-II Biomass Succession Extension v.3.2.1 (LBSE, Scheller & Miranda 2015b), with a few changes (see Differences between Biomass_core and LBSE). Nonetheless, the essential functioning of the succession model still largely follows its LANDIS-II counterpart, and we refer the reader to the corresponding LBSE manual (Scheller & Miranda 2015b) for a detailed reading of the mechanisms implemented in the model.

Figure 1.1: Biomass_core simulates tree cohort growth, mortality, recruitment and dispersal dynamics, as a function of cohort ageing and competition for light (shading) and space, as well as disturbances like fire (simulated using other modules).

1.1.3 Links to other modules

Biomass_core is intended to be used with data/calibration modules, disturbance modules and validation modules, amongst others. The following is a list of the modules most commonly used with Biomass_core. For those not yet in the LandR Manual see the individual module’s documentation (.Rmd file) available in its repository.

See here for all available modules and select Biomass_core from the drop-down menu to see linkages.

Data and calibration modules:

• Biomass_speciesData: grabs and merges several sources of species cover data, making species percent cover (% cover) layers used by other LandR Biomass modules. Default source data spans the entire Canadian territory;

• Biomass_borealDataPrep: prepares all parameters and inputs (including initial landscape conditions) that Biomass_core needs to run a realistic simulation. Default values/inputs produced are relevant for boreal forests of Western Canada;

• Biomass_speciesParameters: calibrates four-species level traits using permanent sample plot data (i.e., repeated tree biomass measurements) across Western Canada.

Disturbance-related modules:

• Biomass_regeneration: simulates cohort biomass responses to stand-replacing fires (as in LBSE), including cohort mortality and regeneration through resprouting and/or serotiny;

• Biomass_regenerationPM: like Biomass_regeneration, but allowing partial mortality. Based on the LANDIS-II Dynamic Fuels & Fire System extension (Sturtevant et al. 2018);

• fireSense: climate- and land-cover-sensitive fire model simulating fire ignition, escape and spread processes as a function of climate and land-cover. Includes built-in parameterisation of these processes using climate, land-cover, fire occurrence and fire perimeter data. Requires using Biomass_regeneration or Biomass_regenerationPM. See modules prefixed “fireSense_” at https://github.com/PredictiveEcology/;

• LandMine: wildfire ignition and cover-sensitive wildfire spread model based on a fire return interval input. Requires using Biomass_regeneration or Biomass_regenerationPM;

• scfm: spatially explicit fire spread module parameterised and modelled as a stochastic three-part process of ignition, escape, and spread. Requires using Biomass_regeneration or Biomass_regenerationPM.

Validation modules:

• Biomass_validationKNN: calculates two validation metrics (mean absolute deviation and sum of negative log-likelihoods) on species presences/absences and biomass-related properties across the simulated landscape. By default, it uses an independent dataset of species % cover and stand biomass for 2011, assuming that this is a second snapshot of the landscape.

1.2 Module manual

1.2.1 General functioning

Biomass_core is a forest landscape model based on the LANDIS-II Biomass Succession Extension v.3.2.1 model (LBSE, Scheller & Miranda 2015b). It is the core forest succession model of the LandR ecosystem of SpaDES modules. Similarly to LBSE, Biomass_core simulates changes in tree cohort aboveground biomass (\(g/m^2\)) by calculating growth, mortality and recruitment as functions of pixel and species characteristics, competition and disturbances (Fig. 1.1). Note that, by default, cohorts are unique combinations of species and age, but this can be changed via the cohortDefinitionCols parameter (see List of parameters).

Specifically, cohort growth is driven by both invariant (growth shape parameter, growthcurve) and spatio-temporally varying species traits (maximum biomass, maxB, and maximum annual net primary productivity, maxANPP), while background mortality (i.e., not caused by disturbances) depends only on invariant species traits (longevity and mortality shape parameter, mortalityshape). All these five traits directly influence the realised shape of species growth curves, by determining how fast they grow (growthcurve and maxANPP), how soon age mortality starts with respect to longevity (mortalityshape) and the biomass a cohort can potentially achieve (maxB).

Cohort recruitment is determined by available “space” (i.e., pixel shade), invariant species traits (regeneration mode, postfireregen, age at maturity, sexualmature, shade tolerance, shadetolerance) and a third spatio-temporally varying trait (species establishment probability, establishprob, called SEP hereafter). The available “growing space” is calculated as the species’ maxB minus the occupied biomass (summed across other cohorts in the pixel). If there is “space”, a cohort can establish from one of three recruitment modes: serotiny, resprouting and germination.

Disturbances (e.g., fire) can cause cohort mortality and trigger post-disturbance regeneration. Two post-disturbance regeneration mechanisms have been implemented, following LBSE: serotiny and resprouting (Scheller & Miranda 2015b). Post-disturbance mortality and regeneration only occur in response to fire and are simulated in two separate, but interchangeable modules, Biomass_regeneration and Biomass_regenerationPM that differ with respect to the level of post-fire mortality they simulate (complete or partial mortality, respectively).

Cohort germination (also called cohort establishment) occurs if seeds are available from local sources (the pixel), or via seed dispersal. Seed dispersal can be of three modes: ‘no dispersal’, ‘universal dispersal’ (arguably, only interesting for dummy case studies) or ‘ward dispersal’ (Scheller & Miranda 2015b). Briefly, the ‘ward dispersal’ algorithm describes a flexible kernel that calculates the probability of a species colonising a neighbour pixel as a function of distance from the source and dispersal-related (and invariant) species traits, and is used by default.

Finally, both germination and regeneration success depend on the species’ probability of germination in a given pixel (probabilities of germination).

We refer the reader to Scheller & Miranda (2015b), Scheller & Domingo (2011) and Scheller & Domingo (2012) for further details with respect to the above mentioned mechanisms implemented in Biomass_core. In a later section of this manual, we highlight existing differences between Biomass_core and LBSE, together with comparisons between the two modules.

1.2.2 Initialisation, inputs and parameters

To initialise and simulate forest dynamics in any given landscape, Biomass_core requires a number of inputs and parameters namely:

• initial cohort biomass and age values across the landscape;

• invariant species traits values;

• spatio-temporally varying species traits values (or just spatially-varying);

• location- (ecolocation-) specific parameters;

• and the probabilities of germination given a species’ shade tolerance and site shade.

These are detailed below and in the full list of input objects. The Biomass_borealDataPrep module manual also provides information about the estimation of many of these traits/inputs from available data, or their adjustment using published values or our best knowledge of boreal forest dynamics in Western Canada.

Unlike the initialisation in LBSE¹, Biomass_core initialises the simulation using data-derived initial cohort biomass and age. This information is ideally supplied by data and calibration modules like Biomass_borealDataPrep (Links to other modules), but Biomass_core can also initialise itself using theoretical data.

Similarly, although Biomass_core can create all necessary traits and parameters using theoretical values, for realistic simulations these should be provided by data and calibration modules, like Biomass_borealDataPrep and Biomass_speciesParameters. We advise future users and developers to become familiar with these data modules and then try to create their own modules (or modify existing ones) for their purpose.

1.2.2.1 Initial cohort biomass and age

Initial cohort biomass and age are derived from stand biomass (biomassMap raster layer), stand age (standAgeMap raster layer) and species % cover (speciesLayers raster layers) data (see Table 1.5) and formatted into the cohortData object. The cohortData table is a central simulation object that tracks the current year’s cohort biomass, age, mortality (lost biomass) and aboveground net primary productivity (ANPP) per species and pixel group (pixelGroup). At the start of the simulation, cohortData will not have any values of cohort mortality or ANPP.

Each pixelGroup is a collection of pixels that share the same ecolocation (coded in the ecoregionMap raster layer) and the same cohort composition. By default, an ecolocation is a combination of land-cover and ecological zonation (see ecoregionMap in the full list of inputs) and unique cohort compositions are defined as unique combinations of species, age and biomass. The cohortData table is therefore always associated with the current year’s pixelGroupMap raster layer, which provides the spatial location of all pixelGroups, allowing to “spatialise” cohort information and dynamics (e.g., dispersal) on a pixel by pixel basis (see also Hashing).

The user, or another module, may provide initial cohortData and pixelGroupMap objects to start the simulation, or the input objects necessary to produce them: a study area polygon (studyArea), the biomassMap, standAgeMap, speciesLayers and ecoregionMap raster layers (see the list of input objects for more detail).

1.2.2.2 Invariant species traits

These are spatio-temporally constant traits that mostly influence population dynamics (e.g., growth, mortality, dispersal) and responses to fire (fire tolerance and regeneration).

By default, Biomass_core obtains trait values from available LANDIS-II tables (see Table 1.5), but traits can be adjusted/supplied by the user or by other modules. For instance, using Biomass_borealDataPrep will adjust some trait values for Western Canadian boreal forests (e.g., longevity values are adjusted following Burton & Cumming 1995a), while using Biomass_speciesParameters calibrates the growthcurve and mortalityshape parameters and estimates two additional species traits (inflationFactor and mANPPproportion) to calibrate maxB and maxANPP (respectively).

Table 1.1 shows an example of a table of invariant species traits. Note that Biomass_core (alone) requires all the columns Table 1.1 in to be present, with the exception of firetolerance, postfireregen, resproutprob, resproutage_min and resproutage_max, which are used by the post-fire regeneration modules (Biomass_regeneration and Biomass_regenerationPM).

Please see Scheller & Domingo (2011) and Scheller & Miranda (2015b) for further detail.

Table 1.1: Example of an invariant species traits table (the species table object in the module), with species Abies sp. (Abie_sp), Picea engelmannii (Pice_eng), Picea glauca (Pice_gla), Pinus sp. (Pinu_sp), Populus sp. (Popu_sp) and Pseudotsuga menziesii (Pseu_men). Note that these are theoretical values.

speciesCode	longevity	sexualmature	shadetolerance	firetolerance	postfireregen	resproutprob	resproutage_min	resproutage_max	seeddistance_eff	seeddistance_max	mortalityshape	growthcurve
Abie_sp	200	20	2.3	1	none	0.0	0	0	25	100	15	0
Pice_eng	460	30	2.1	2	none	0.0	0	0	30	250	15	1
Pice_gla	400	30	1.6	2	none	0.0	0	0	100	303	15	1
Pinu_sp	150	15	1.0	2	serotiny	0.0	0	0	30	100	15	0
Popu_sp	140	20	1.0	1	resprout	0.5	10	70	200	5000	25	0
Pseu_men	525	25	2.0	3	none	0.0	0	0	100	500	15	1

1.2.2.3 Spatio-temporally varying species traits

These traits vary between species, by ecolocation and, potentially, by year if the year column is not omitted and several years exist (in which case last year’s values up to the current simulation year are always used). They are maximum biomass, maxB, maximum above-ground net primary productivity, maxANPP, and species establishment probability, SEP (called establishprob in the module). By default, Biomass_core assigns theoretical values to these traits, and thus we recommend using Biomass_borealDataPrep to obtain realistic trait values derived from data (by default, pertinent for Canadian boreal forest applications), or passing a custom table directly. Biomass_speciesParameters further calibrates maxB and maxANPP by estimating two additional invariant species traits (inflationFactor and mANPPproportion; also for Western Canadian forests). See Table 1.2 for an example.

Table 1.2: Example of a spatio-temporally varying species traits table (the speciesEcoregion table object in the module), with two ecolocations (called ecoregionGroups) and species Abies sp. (Abie_sp), Picea engelmannii (Pice_eng), Picea glauca (Pice_gla), Pinus sp. (Pinu_sp), Populus sp. (Popu_sp) and Pseudotsuga menziesii (Pseu_men). If a simulation runs for 10 year using this table, trait values from year 2 would be used during simulation years 2-10.

ecoregionGroup	speciesCode	establishprob	maxB	maxANPP	year
1_03	Abie_sp	1.000	8567	285	1
1_03	Pice_eng	0.983	10156	305	1
1_03	Popu_sp	0.737	8794	293	1
1_03	Pseu_men	1.000	17534	132	1
1_09	Abie_sp	0.112	1499	50	1
1_09	Pice_gla	0.302	3143	102	1
1_09	Pinu_sp	0.714	2569	86	1
1_09	Popu_sp	0.607	3292	110	1
1_09	Pseu_men	0.997	6020	45	1
1_03	Abie_sp	0.989	8943	225	2
1_03	Pice_eng	0.985	9000	315	2
1_03	Popu_sp	0.600	8600	273	2
1_03	Pseu_men	1.000	13534	142	2
1_09	Abie_sp	0.293	2099	45	2
1_09	Pice_gla	0.745	3643	90	2
1_09	Pinu_sp	0.500	2569	80	2
1_09	Popu_sp	0.670	3262	111	2
1_09	Pseu_men	1.000	6300	43	2

1.2.2.4 Ecolocation-specific parameters – minimum relative biomass

Minimum relative biomass (minRelativeB) is the only ecolocation-specific parameter used in Biomass_core. It is used to determine the shade level in each pixel (i.e., site shade) with respect to the total potential maximum biomass for that pixel (i.e., the sum of all maxB values in the pixel’s ecolocation). If relative biomass in the stand (with regards to the total potential maximum biomass) is above the minimum relative biomass thresholds, the pixel is assigned that threshold’s site shade value (Scheller & Miranda 2015b).

The shade level then influences the germination and regeneration of new cohorts, depending on their shade tolerance (see Probabilities of germination).

Site shade varies from X0 (no shade) to X5 (maximum shade). By default, Biomass_core uses the same minimum realtive biomass threshold values across all ecolocations, adjusted from a publicly available LANDIS-II table to better reflect Western Canada boreal forest dynamics (see Table 1.3). Biomass_borealDataPrep does the same adjustment by default. As with other inputs, these values can be adjusted by using other modules or by passing user-defined tables.

Table 1.3: Example of a minimum relative biomass table (the minRelativeB table object in the module), with two ecolocations (ecoregionGroups) sharing the same values

ecoregionGroup
1_03	0.15	0.25	0.5	0.75	0.85
1_09	0.15	0.25	0.5	0.75	0.85

1.2.2.5 Probabilities of germination

A species’ probability of germination results from the combination of its shade tolerance level (an invariant species trait in the species table; Table 1.1) and the site shade (defined by the amount of biomass in the pixel – see minimum relative biomass parameter and Scheller & Miranda 2015b). By default, both Biomass_core and Biomass_borealDataPrep use a publicly available LANDIS-II table (called sufficientLight in the module; Table 1.4).

Table 1.4: Default species probability of germination values used by Biomass_core and Biomass_borealDataPrep. Columns X0-X5 are different site shade levels and each line has the probability of germination for each site shade and species shade tolerance combination.

species shade tolerance	X0	X1	X2	X3	X4	X5
1	1	0	0	0	0	0
2	1	1	0	0	0	0
3	1	1	1	0	0	0
4	1	1	1	1	0	0
5	0	0	1	1	1	1

1.2.2.6 Other module inputs

The remaining module input objects either do not directly influence the basic mechanisms implemented in Biomass_core (e.g., sppColorVect and studyAreaReporting are only used for plotting purposes), are objects that keep track of a property/process in the module (e.g., lastReg is a counter of the last year when regeneration occurred), or define the study area for the simulation (e.g., studyArea and rasterToMatch).

The next section provides a complete list of all input objects, including those already mentioned above.

1.2.3 List of input objects

All of Biomass_core’s input objects have (theoretical) defaults that are produced automatically by the module². We suggest that new users run Biomass_core by itself supplying only a studyArea polygon, before attempting to supply their own or combining Biomass_core with other modules. This will enable them to become familiar with all the input objects in a theoretical setting.

Of the inputs listed in Table 1.5, the following are particularly important and deserve special attention:

Spatial layers

• ecoregionMap – a raster layer with ecolocation IDs. Note that the term “ecoregion” was inherited from LBSE and kept for consistency with original LBSE code, but we prefer to call them ecolocations to avoid confusion with the ecoregion-level classification of the National Ecological Classification of Canada (NECC). Ecolocations group pixels with similar biophysical conditions. By default, we use two levels of grouping in our applications: the first level being an ecological classification such as ecodistricts from the NECC, and the second level is a land-cover classification. Hence, these ecolocations contain relatively coarse scale regional information plus finer scale land cover information. The ecoregionMap layer must be defined as a categorical raster, with an associated Raster Attribute Table (RAT; see, e.g., raster::ratify). The RAT must contain the columns: ID (the value in the raster layer), ecoregion (the first level of grouping) and ecoregionGroup (the full ecolocation “name” written as <firstlevel_secondlevel>). Note that if creating ecoregionGroup’s by combining two raster layers whose values are numeric (as in Biomass_borealDataPrep), the group label is a character combination of two numeric grouping levels. For instance, if Natural Ecoregion 2 has land-cover types 1, 2 and 3, the RAT will contain ID = {1,2,3}, ecoregion = {2} and ecoregionGroup = {2_1, 2_2, 2_3}. However, the user is free to use any groupings they wish. Finally, note that all ecolocations (ecoregionGroup’s) are should be listed in the ecoregion table.

• rasterToMatch – a RasterLayer, with a given resolution and projection determining the pixels (i.e., non-NA values) where forest dynamics will be simulated. Needs to match studyArea. If not supplied, Biomass_core attempts to produce it from studyArea, using biomassMap as the template for spatial resolution and projection.

• studyArea – a SpatialPolygonsDataFrame with a single polygon determining the where the simulation will take place. This is the only input object that must be supplied by the user or another module.

Species traits and other parameter tables

• ecoregion – a data.table listing all ecolocation “names” (ecoregionGroup column; see ecoregionMap above for details) and their state (active – yes – or inactive – no)

• minRelativeB – a data.table of minimum relative biomass values. See Ecolocation-specific parameters – minimum relative biomass.

• species – a data.table of invariant species traits.

• speciesEcoregion – a data.table of spatio-temporally varying species traits.

• sufficientLight – a data.table defining the probability of germination for a species, given its shadetolerance level (see species above) and the shade level in the pixel (see minRelativeB above). See Probabilities of germination.

• sppEquiv – a data.table of species name equivalences between various conventions. It must contain the columns LandR (species IDs in the LandR format), EN_generic_short (short generic species names in English – or any other language – used for plotting), Type (type of species, Conifer or Deciduous, as in “broadleaf”) and Leading (same as EN_generic_short but with “leading” appended – e.g., “Poplar leading”). See ?LandR::sppEquivalencies_CA for more information.

• sppColorVect – character. A named vector of colours used to plot species dynamics. Should contain one colour per species in the species table and, potentially a colour for species mixtures (named “Mixed”). Vector names must follow species$speciesCode.

Cohort-simulation-related objects

• cohortData – a data.table containing initial cohort information per pixelGroup (see pixelGroupMap below). This table is updated during the simulation as cohort dynamics are simulated. It must contain the following columns:

  • pixelGroup – integer. pixelGroup ID. See Hashing.

  • ecoregionGroup – character. Ecolocation names. See ecoregionMap and ecoregion objects above.

  • speciesCode – character. Species ID.

  • age – integer. Cohort age.

  • B – integer. Cohort biomass of the current year in \(g/m^2\).

  • mortality – integer. Cohort dead biomass of the current year in \(g/m^2\). Usually filled with 0s in initial conditions.

  • aNPPAct – integer. Actual aboveground net primary productivity of the current year in \(g/m^2\). B is the result of the previous year’s B minus the current year’s mortality plus aNPPAct. Usually filled with 0s in initial conditions. See “1.1.3 Cohort growth and ageing” section of Scheller & Miranda (2015b).

• pixelGroupMap – a raster layer with pixelGroup IDs per pixel. Pixels are always grouped based on identical ecoregionGroup, speciesCode, age and B composition, even if the user supplies other initial groupings (e.g., this is possible in the Biomass_borealDataPrep data module).

Table 1.5: List of Biomass_core input objects and their description.

objectName	objectClass	desc	sourceURL
biomassMap	RasterLayer	total biomass raster layer in study area (in g/m^2), filtered for pixels covered by cohortData. Only used if P(sim)$initialBiomassSource == 'biomassMap', which is currently deactivated.
cceArgs	list	a list of quoted objects used by the growthAndMortalityDriver calculateClimateEffect function	NA
cohortData	data.table	data.table with cohort-level information on age and biomass, by pixelGroup and ecolocation (i.e., ecoregionGroup). If supplied, it must have the following columns: pixelGroup (integer), ecoregionGroup (factor), speciesCode (factor), B (integer in g/m^2), age (integer in years)	NA
ecoregion	data.table	ecoregion look up table	https://raw.githubusercontent.com/LANDIS-II-Foundation/Extensions-Succession/master/biomass-succession-archive/trunk/tests/v6.0-2.0/ecoregions.txt
ecoregionMap	RasterLayer	ecoregion map that has mapcodes match ecoregion table and speciesEcoregion table. Defaults to a dummy map matching rasterToMatch with two regions	NA
lastReg	numeric	an internal counter keeping track of when the last regeneration event occurred	NA
minRelativeB	data.frame	table defining the relative biomass cut points to classify stand shadeness.	NA
pixelGroupMap	RasterLayer	a raster layer with pixelGroup IDs per pixel. Pixels are grouped based on identical ecoregionGroup, speciesCode, age and B composition, even if the user supplies other initial groupings (e.g., via the Biomass_borealDataPrep module.	NA
rasterToMatch	RasterLayer	a raster of the studyArea in the same resolution and projection as biomassMap	NA
species	data.table	a table of invariant species traits with the following trait colums: ‘species’, ‘Area’, ‘longevity’, ‘sexualmature’, ‘shadetolerance’, ‘firetolerance’, ‘seeddistance_eff’, ‘seeddistance_max’, ‘resproutprob’, ‘mortalityshape’, ‘growthcurve’, ‘resproutage_min’, ‘resproutage_max’, ‘postfireregen’, ‘wooddecayrate’, ‘leaflongevity’ ‘leafLignin’, ‘hardsoft’. The last seven traits are not used in Biomass_core , and may be ommited. However, this may result in downstream issues with other modules. Default is from Dominic Cyr and Yan Boulanger’s project	https://raw.githubusercontent.com/dcyr/LANDIS-II_IA_generalUseFiles/master/speciesTraits.csv
speciesEcoregion	data.table	table of spatially-varying species traits (maxB, maxANPP, establishprob), defined by species and ecoregionGroup) Defaults to a dummy table based on dummy data os biomass, age, ecoregion and land cover class	NA
speciesLayers	RasterStack	percent cover raster layers of tree species in Canada. Defaults to the Canadian Forestry Service, National Forest Inventory, kNN-derived species cover maps from 2001 using a cover threshold of 10 - see https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990 for metadata	http://ftp.maps.canada.ca/pub/nrcan_rncan/Forests_Foret/canada-forests-attributes_attributs-forests-canada/2001-attributes_attributs-2001/
sppColorVect	character	A named vector of colors to use for plotting. The names must be in sim$sppEquiv[[sim$sppEquivCol]], and should also contain a color for ‘Mixed’	NA
sppEquiv	data.table	table of species equivalencies. See LandR::sppEquivalencies_CA.	NA
studyArea	SpatialPolygonsDataFrame	Polygon to use as the study area. Must be provided by the user	NA
studyAreaReporting	SpatialPolygonsDataFrame	multipolygon (typically smaller/unbuffered than studyArea) to use for plotting/reporting. Defaults to studyArea.	NA
sufficientLight	data.frame	table defining how the species with different shade tolerance respond to stand shade. Default is based on LANDIS-II Biomass Succession v6.2 parameters	https://raw.githubusercontent.com/LANDIS-II-Foundation/Extensions-Succession/master/biomass-succession-archive/trunk/tests/v6.0-2.0/biomass-succession_test.txt
treedFirePixelTableSinceLastDisp	data.table	3 columns: pixelIndex, pixelGroup, and burnTime. Each row represents a forested pixel that was burned up to and including this year, since last dispersal event, with its corresponding pixelGroup and time it occurred	NA

1.2.4 List of parameters

In addition to the above inputs objects, Biomass_core uses several parameters³ that control aspects like the simulation length, the “succession” time step, plotting and saving intervals, amongst others. Note that a few of these parameters are only relevant when simulating climate effects of cohort growth and mortality, which require also loading the LandR.CS R package⁴ (or another similar package). These are not discussed in detail here, since climate effects are calculated externally to Biomass_core in LandR.CS functions and thus documented there.

A list of useful parameters and their description is listed below, while the full set of parameters is in Table 1.6. Like with input objects, default values are supplied for all parameters and we suggest the user becomes familiarized with them before attempting any changes. We also note that the "spin-up" and "biomassMap" options for the initialBiomassSource parameter are currently deactivated, since Biomass_core no longer generates initial cohort biomass conditions using a spin-up based on initial stand age like LANDIS-II ("spin-up"), nor does it attempt to fill initial cohort biomasses using biomassMap.

Plotting and saving

• .plots – activates/deactivates plotting and defines type of plotting (see ?Plots);

• .plotInitialTime – defines when plotting starts;

• .plotInterval – defines plotting frequency;

• .plotMaps – activates/deactivates map plotting;

• .saveInitialTime – defines when saving starts;

• .saveInterval – defines saving frequency;

Simulation

• seedingAlgorithm – dispersal type (see above);

• successionTimestep – defines frequency of dispersal/local recruitment event (growth and mortality are always yearly);

Other

• mixedType – how mixed forest stands are defined;

• vegLeadingProportion – relative biomass threshold to consider a species “leading” (i.e., dominant);

Table 1.6: List of Biomass_core parameters and their description.

paramName	paramClass	default	min	max	paramDesc
calcSummaryBGM	character	end	NA	NA	A character vector describing when to calculate the summary of biomass, growth and mortality Currently any combination of 5 options is possible: ‘start’- as before vegetation succession events, i.e. before dispersal, ‘postDisp’ - after dispersal, ‘postRegen’ - after post-disturbance regeneration (currently the same as ‘start’), ‘postGM’ - after growth and mortality, ‘postAging’ - after aging, ‘end’ - at the end of vegetation succesion events, before plotting and saving. The ‘end’ option is always active, being also the default option. If NULL, then will skip all summaryBGM related events
calibrate	logical	FALSE	NA	NA	Do calibration? Defaults to FALSE
cohortDefinitionCols	character	pixelGro….	NA	NA	cohortData columns that determine what constitutes a cohort This parameter should only be modified if additional modules are adding columns to cohortData
cutpoint	numeric	1e+10	NA	NA	A numeric scalar indicating how large each chunk of an internal data.table is, when processing by chunks
gmcsGrowthLimits	numeric	66.66666….	NA	NA	if using LandR.CS for climate-sensitive growth and mortality, a percentile is used to estimate the effect of climate on growth/mortality (currentClimate/referenceClimate). Upper and lower limits are suggested to circumvent problems caused by very small denominators as well as predictions outside the data range used to generate the model
gmcsMortLimits	numeric	66.66666….	NA	NA	if using LandR.CS for climate-sensitive growth and mortality, a percentile is used to estimate the effect of climate on growth/mortality (currentClimate/referenceClimate). Upper and lower limits are suggested to circumvent problems caused by very small denominators as well as predictions outside the data range used to generate the model
gmcsMinAge	numeric	21	0	NA	if using LandR.CS for climate-sensitive growth and mortality, the minimum age for which to predict climate-sensitive growth and mortality. Young stands (< 30) are poorly represented by the PSP data used to parameterize the model.
growthAndMortalityDrivers	character	LandR	NA	NA	package name where the following functions can be found: calculateClimateEffect, assignClimateEffect (see LandR.CS for climate sensitivity equivalent functions, or leave default if this is not desired)
growthInitialTime	numeric	start(sim)	NA	NA	Initial time for the growth event to occur
initialB	numeric	10	1	NA	initial biomass values of new age-1 cohorts
initialBiomassSource	character	cohortData	NA	NA	Currently, there are three options: ‘spinUp’, ‘cohortData’, ‘biomassMap’. If ‘spinUp’, it will derive biomass by running spinup derived from Landis-II. If ‘cohortData’, it will be taken from the cohortData object, i.e., it is already correct, by cohort. If ‘biomassMap’, it will be taken from sim$biomassMap, divided across species using sim$speciesLayers percent cover values ‘spinUp’ uses sim$standAgeMap as the driver, so biomass is an output . That means it will be unlikely to match any input information about biomass, unless this is set to ‘biomassMap’, and a sim$biomassMap is supplied. Only the ‘cohortData’ option is currently active.
keepClimateCols	logical	FALSE	NA	NA	include growth and mortality predictions in cohortData?
minCohortBiomass	numeric	0	NA	NA	cohorts with biomass below this threshold (in g/m^2) are removed. Not a LANDIS-II BSE parameter.
mixedType	numeric	2	NA	NA	How to define mixed stands: 1 for any species admixture; 2 for deciduous > conifer. See ?LandR::vegTypeMapGenerator.
plotOverstory	logical	FALSE	NA	NA	swap max age plot with overstory biomass
seedingAlgorithm	character	wardDisp….	NA	NA	choose which seeding algorithm will be used among ‘noSeeding’ (no horizontal, nor vertical seeding - not in LANDIS-II BSE), ‘noDispersal’ (no horizontal seeding), ‘universalDispersal’ (seeds disperse to any pixel), and ‘wardDispersal’ (default; seeds disperse according to distance and dispersal traits). See Scheller & Miranda (2015) - Biomass Succession extension, v3.2.1 User Guide
spinupMortalityfraction	numeric	0.001	NA	NA	defines the mortality loss fraction in spin up-stage simulation. Only used if P(sim)$initialBiomassSource == 'biomassMap', which is currently deactivated.
sppEquivCol	character	Boreal	NA	NA	The column in sim$sppEquiv data.table to use as a naming convention
successionTimestep	numeric	10	NA	NA	defines the simulation time step, default is 10 years. Note that growth and mortality always happen on a yearly basis. Cohorts younger than this age will not be included in competitive interactions
vegLeadingProportion	numeric	0.8	0	1	a number that defines whether a species is leading for a given pixel
.maxMemory	numeric	5	NA	NA	maximum amount of memory (in GB) to use for dispersal calculations.
.plotInitialTime	numeric	start(sim)	NA	NA	Vector of length = 1, describing the simulation time at which the first plot event should occur. To plotting off completely use P(sim)$.plots.
.plotInterval	numeric	NA	NA	NA	defines the plotting time step. If NA, the default, .plotInterval is set to successionTimestep.
.plots	character	object	NA	NA	Passed to types in Plots (see ?Plots). There are a few plots that are made within this module, if set. Note that plots (or their data) saving will ONLY occur at end(sim). If NA, plotting is turned off completely (this includes plot saving).
.plotMaps	logical	TRUE	NA	NA	Controls whether maps should be plotted or not. Set to FALSE if P(sim)$.plots == NA
.saveInitialTime	numeric	NA	NA	NA	Vector of length = 1, describing the simulation time at which the first save event should occur. Set to NA if no saving is desired. If not NA, then saving will occur at P(sim)$.saveInitialTime with a frequency equal to P(sim)$.saveInterval
.saveInterval	numeric	NA	NA	NA	defines the saving time step. If NA, the default, .saveInterval is set to P(sim)$successionTimestep.
.sslVerify	integer	64	NA	NA	Passed to httr::config(ssl_verifypeer = P(sim)$.sslVerify) when downloading KNN (NFI) datasets. Set to 0L if necessary to bypass checking the SSL certificate (this may be necessary when NFI’s website SSL certificate is not correctly configured).
.studyAreaName	character	NA	NA	NA	Human-readable name for the study area used. If NA, a hash of studyArea will be used.
.useCache	character	.inputOb….	NA	NA	Internal. Can be names of events or the whole module name; these will be cached by SpaDES
.useParallel	ANY	2	NA	NA	Used only in seed dispersal. If numeric, it will be passed to data.table::setDTthreads and should be <= 2; If TRUE, it will be passed to parallel::makeCluster; and if a cluster object, it will be passed to parallel::parClusterApplyB.

1.2.5 List of outputs

The main outputs of Biomass_core are the cohortData and pixelGroupMap containing cohort information per year (note that they are not saved by default), visual outputs of species level biomass, age and dominance across the landscape and the simulation length, and several maps of stand biomass, mortality and reproductive success (i.e, new biomass) on a yearly basis.

However, any of the objects changed/output by Biomass_core (listed in Table 1.7) can be saved via the outputs argument in simInit⁵.

Table 1.7: List of Biomass_core output objects and their description.

objectName	objectClass	desc
activePixelIndex	integer	internal use. Keeps track of which pixels are active
activePixelIndexReporting	integer	internal use. Keeps track of which pixels are active in the reporting study area
ANPPMap	RasterLayer	ANPP map at each succession time step (in g /m^2)
cohortData	data.table	data.table with cohort-level information on age, biomass, aboveground primary productivity (year’s biomass gain) and mortality (year’s biomass loss), by pixelGroup and ecolocation (i.e., ecoregionGroup). Contains at least the following columns: pixelGroup (integer), ecoregionGroup (factor), speciesCode (factor), B (integer in g/m^2), age (integer in years), mortality (integer in g/m^2), aNPPAct (integer in g/m^2). May have other columns depending on additional simulated processes (i.e., cliamte sensitivity; see, e.g., P(sim)$keepClimateCols).
ecoregionMap	RasterLayer	map with mapcodes match ecoregion table and speciesEcoregion table. Defaults to a dummy map matching rasterToMatch with two regions
inactivePixelIndex	logical	internal use. Keeps track of which pixels are inactive
inactivePixelIndexReporting	integer	internal use. Keeps track of which pixels are inactive in the reporting study area
lastFireYear	numeric	Year of the most recent fire year
lastReg	numeric	an internal counter keeping track of when the last regeneration event occurred
minRelativeB	data.frame	define the relative biomass cut points to classify stand shade
mortalityMap	RasterLayer	map of biomass lost (in g/m^2) at each succession time step
pixelGroupMap	RasterLayer	updated community map at each succession time step
regenerationOutput	data.table	If P(sim)$calibrate == TRUE, an summary of seed dispersal and germination success (i.e., number of pixels where seeds successfully germinated) per species and year.
reproductionMap	RasterLayer	Regeneration map (biomass gains in g/m^2) at each succession time step
simulatedBiomassMap	RasterLayer	Biomass map at each succession time step (in g/m^2)
simulationOutput	data.table	contains simulation results by ecoregionGroup (main output)
simulationTreeOutput	data.table	Summary of several characteristics about the stands, derived from cohortData
species	data.table	a table that has species traits such as longevity, shade tolerance, etc. Currently obtained from LANDIS-II Biomass Succession v.6.0-2.0 inputs
speciesEcoregion	data.table	define the maxANPP, maxB and SEP change with both ecoregion and simulation time
speciesLayers	RasterStack	species percent cover raster layers, based on input speciesLayers object. Not changed by this module.
spinupOutput	data.table	Spin-up output. Currently deactivated.
summaryBySpecies	data.table	The total species biomass (in g/m^2 as in cohortData), average age and aNPP (in g/m^2 as in cohortData), across the landscape (used for plotting and reporting).
summaryBySpecies1	data.table	No. pixels of each leading vegetation type (used for plotting and reporting).
summaryLandscape	data.table	The averages of total biomass (in tonnes/ha , not g/m^2 like in cohortData), age and aNPP (also in tonnes/ha) across the landscape (used for plotting and reporting).
treedFirePixelTableSinceLastDisp	data.table	3 columns: pixelIndex, pixelGroup, and burnTime. Each row represents a forested pixel that was burned up to and including this year, since last dispersal event, with its corresponding pixelGroup and time it occurred
vegTypeMap	RasterLayer	Map of leading species in each pixel, colored according to sim$sppColorVect. Species mixtures calculated according to P(sim)$vegLeadingProportion and P(sim)$mixedType.

1.2.6 Simulation flow and module events

Biomass_core itself does not simulate disturbances or their effect on vegetation (i.e., post-disturbance mortality and regeneration). Should disturbance and post-disturbance mortality/regeneration modules be used (e.g., LandMine and Biomass_regeneration), the user should make sure that post-disturbance effects occur after the disturbance, but before dispersal and background vegetation growth and mortality (simulated in Biomass_core). Hence, the disturbance itself should take place either at the very beginning or at the very end of each simulation time step to guarantee that it happens immediately before post-disturbance effects are calculated.

The general flow of Biomass_core processes with and without disturbances is:

1. Preparation of necessary objects for the simulation – either by data and calibration modules or by Biomass_core itself (during simInit and the init event⁶);

2. Disturbances (OPTIONAL) – simulated by a disturbance module (e.g., LandMine);

3. Post-disturbance mortality/regeneration (OPTIONAL) – simulated by a regeneration module (e.g., Biomass_regeneration);

4. Seed dispersal (every successionTimestep; Dispersal event):

   • seed dispersal can be a slow process and has been adapted to occur every 10 years (default successionTimestep). The user can set it to occur more/less often, with the caveat that if using Biomass_borealDataPrep to estimate species establishment probabilities, these values are integrated over 10 years.
   • see Scheller & Domingo (2012) for details on dispersal algorithms.

5. Growth and mortality (mortalityAndGrowth event):

   • unlike dispersal, growth and mortality always occur time step (year).
   • see Scheller & Mladenoff (2004) for further detail.

6. Cohort age binning (every successionTimestep; cohortAgeReclassification event):

   • follows the same frequency as dispersal, collapsing cohorts (i.e., summing their biomass/mortality/aNPP) to ages classes with resolution equal to successionTimestep.
   • see Scheller & Miranda (2015b) for further detail.

7. Summary tables of regeneration (summaryRegen event), biomass, age, growth and mortality (summaryBGM event);

8. Plots of maps (plotMaps event) and averages (plotAvgs and plotSummaryBySpecies events);

9. Save outputs (save event).

… (repeat 2-9) …

1.2.7 Differences between Biomass_core and the LANDIS-II Biomass Succession Extension model (LBSE)

1.2.7.1 Algorithm changes

Upon porting LBSE into R, we made six minor modifications to the original model’s algorithms to better reflect ecological processes. This did not significantly alter the simulation outputs and we note that these changes might also have been implemented in more recent versions of LBSE.

First, for each year and community (i.e., ‘pixel group’ in Biomass_core, see below), LBSE calculates the competition index for a cohort sequentially (i.e., one cohort at a time) after updating the growth and mortality of other cohorts (i.e., their biomass gain and loss, respectively) , and with the calculation sequence following cohort age in descending order, but no explicit order of species. This sorting of growth and mortality calculations from oldest to youngest cohorts in LBSE was aimed at capturing size-asymmetric competition between cohorts, under the assumption that older cohorts have priority for growing space given their greater height (Scheller pers. comm.). We felt that within-year sequential growth, death and recruitment may be not ecologically accurate, and that the size-asymmetric competition was being accounted for twice, as the calculation of the competition index already considers the competitive advantage of older cohorts (as shown in the User’s Guide, Scheller & Miranda 2015b). Hence, in Biomass_core growth, mortality, recruitment and the competition index are calculated at the same time across all cohorts and species.

Second, the unknown species-level sorting mechanism contained within LBSE (which changed depending on the species order in the input species list file), led to different simulation results depending on the input species list file (e.g., Table 1.8 and Fig. 1.2). The calculation of competition, growth and mortality for all cohorts at the same time also circumvented this issue.

Figure 1.2: Differences in total landscape aboveground biomass when using two different input species orders for the same community. These simulations demonstrate how the sequential calculation of the competition index, combined with a lack of explicit species ordering affect the overall landscape aboveground biomass in time when using different input species orders (see Table 1.8). In order to prevent differences introduced by cohort recruitment, species’ ages at sexual maturity were changed to the species’ longevity values, and the simulation ran for 75 years to prevent any cohorts from reaching sexual maturity. The bottom panel shows the difference between the two simulations in percentage, calculated as \(\frac{Biomass_{order2} - Biomass_{order1}}{Biomass_{order2}} * 100\)

Third, in LBSE the calculation of total pixel biomass for the purpose of calculating the initial biomass of a new cohort included the (previously calculated) biomass of other new cohorts when succession time step = 1, but not when time step was > 1. This does not reflect the documentation in the User’s Guide, which stated that “Bsum [total pixel biomass] is the current total biomass for the site (not including other new cohorts)” (Scheller & Miranda 2015b), when the succession time step was set to 1. Additionally, together with the lack of explicit ordering, this generated different results in terms of the biomass assigned to each new cohort (e.g., Table 1.9 and Fig. 1.3). In Biomass_core the initial biomass of new cohorts is no longer calculated sequentially (as with competition, growth and mortality), and thus the biomass of new cohorts is never included in the calculation of total pixel biomass.

Figure 1.3: Differences in the biomass assigned to new cohorts, summed for each species across pixels, when using two different input species orders for the same community and when the succession time step is 1. These simulations demonstrate how the different summation of total cohort biomass for a succession time step of 1 and the lack of explicit species ordering affect simulation results when changing the species order in the input file (see Table 1.9). Here, initial cohort ages were also set to 1. Values refer to the initial total biomass attributed to each species at the end of year 1.

Fourth, in LBSE, serotiny and resprouting could not occur in the same pixel following a fire, with serotiny taking precedence if activated. We understand that this provides an advantage to serotinous species, which could perhaps be disadvantaged with respect to fast-growing resprouters. However, we feel that it is ecologically more realistic that serotinous and resprouter species be able to both regenerate in a given pixel following a fire and allow the competition between serotinous and resprouting species to arise from species traits. Note that this change was implemented in the Biomass_regeneration and Biomass_regenerationPM modules, since post-disturbance effects were separated background vegetation dynamics simulated by Biomass_core.

Fifth, in Biomass_core, species shade tolerance values can have decimal values to allow for finer adjustments of between-species competition.

Sixth, we added a new parameter called minCohortBiomass, that allows the user to control cohort removal bellow a certain threshold of biomass. In some simulation set-ups, we noticed that Biomass_core (and LBSE) were able to generate many very small cohorts in the understory that, due to cohort competition, were not able to gain biomass and grow. However, because competition decreases growth but does not increase mortality, these cohorts survived at very low biomass levels until they reached sufficient age to suffer age-related mortality. We felt this is unlikely to be realistic in many cases. By default, this parameter is left at 0 to follow LBSE behaviour (i.e., no cohorts removal based on minimum biomass).

1.2.7.2 Other enhancements

In addition to the sixth changes in growth, mortality and regeneration mentioned above, we enhanced modularity by separating the components that govern vegetation responses to disturbances from Biomass_core, and implemented hashing, caching and testing to improve computational efficiency and insure performance.

1.2.7.2.1 Modularity

Unlike in LBSE, post-disturbance effects are not part of Biomass_core per se, but belong to two separate modules, used interchangeably (Biomass_regeneration and Biomass_regenerationPM). These need to be loaded and added to the “modules folder” of the project in case the user wants to simulate forest responses to disturbances (only fire disturbances at the moment). Again, this enables higher flexibility when swapping between different approaches to regeneration.

Climate effects on growth and mortality were also implemented a modular way. The effects of climate on biomass increase (growth) and loss (mortality) were written in functions grouped in two packages. The LandR R package contains default, “non-climate-sensitive” functions, while the LandR.CS R package contains the functions that simulate climate effects (CS stands for “climate sensitive”). Note that these functions do not simulate actual growth/mortality processes, but estimate modifiers that increase/decrease cohort biomass on top of background growth/mortality. Biomass_core uses the LandR functions by default (see growthAndMortalityDrivers parameter in the full parameters list). Should the user wish to change how climate effects on growth/mortality are calculated, they can provide new compatible functions (i.e., with the same names, inputs and outputs) via another R package.

1.2.7.2.2 Hashing

Our first strategy to improve simulation efficiency in Biomass_core was to use a hashing mechanism (Yang et al. 2011). Instead of assigning a key to each pixel in a raster and tracking the simulation for each pixel in a lookup table, we indexed pixels using a pixelGroup key that contained unique combinations of ecolocation and community composition (i.e., species, age and biomass composition), and tracked and stored simulation data for each pixelGroup (Fig. 1.4). This algorithm was able to ease the computational burden by significantly reducing the size of the lookup table and speeding-up the simulation process. After recruitment and disturbance events, pixels are rehashed into new pixel groups.

Figure 1.4: Hashing design for Biomass_core. In the re-coded Biomass_core, the pixel group map was hashed based on the unique combination of species composition (‘community map’) and ecolocation map, and associated with a lookup table. The insert in the top-right corner was the original design that linked the map to the lookup table by pixel key.

1.2.7.2.3 Caching

The second strategy aimed at improving model efficacy was the implementation of caching during data-driven parametrisation and initialisation. Caching automatically archives outputs of a given function to disk (or memory) and reads them back when subsequent calls of this function are given identical inputs. All caching operations were achieved using the reproducible R package (McIntire & Chubaty 2020).

In the current version of Biomass_core, the spin-up phase was replaced by data-driven landscape initialisation and many model parameters were derived from data, using data and calibration modules (e.g., Biomass_borealDataPrep). To avoid having to repeat data downloads and treatment, statistical estimation of parameters and landscape initialisation every time the simulation is re-run under the same conditions, many of these pre-simulation steps are automatically cached. This means that the pre-simulation phase is significantly faster upon a second call when inputs have not changed (e.g., the input data and parametrisation methods), and when inputs do change only directly affected steps are re-run (see main text for examples). When not using data modules, Biomass_core still relies on caching for the preparation of its theoretical inputs.

1.2.7.2.4 Testing

Finally, we implemented code testing to facilitate bug detection by comparing the outputs of functions (etc.) to expected outputs (Wickham 2011). We built and integrated code tests in Biomass_core and across all LandR modules and the LandR R package in the form of assertions, unit tests and integration tests. Assertions and unit tests are run automatically during simulations (but can be turned off) and evaluate individual code components (e.g., one function or an object’s class). Integration tests evaluate if several coded processes are integrated correctly and are usually run manually. However, because we embedded assertions within the module code, R package dependencies of Biomass_core, such as the LandR R package and SpaDES, they also provide a means to test module integration. We also implemented GitHub Actions continuous integration (CI), which routinely test GitHub hosted packages (e.g., LandR) and modules. CRAN-hosted packages (e.g., SpaDES) are also automatically tested and checked on CRAN.

Finally, because Biomass_core (and all other LandR modules) code is hosted in public GitHub repositories, the module code is subject to the scrutiny of many users, who can identify issues and contribute to improve module code.

1.2.7.3 Performance and accuracy of Biomass_core with respect to LBSE

In the recoding of Biomass_core, we used integration tests to ensured similar outputs of each demographic process (namely, growth, mortality and recruitment) to the outputs from its counterpart in LBSE. Here, we report the comparisons of the overall simulation (i.e., including all demographic processes) between LBSE and Biomass_core using three randomly generated initial communities (Tables 1.10-1.12). The remaining input parameters were taken from a LANDIS-II training course (Tables 1.13-1.16), and contained species attributes information of 16 common tree species in boreal forests and 2 ecolocations. We ran simulations for 1000 years, with a succession time step of 10 and three replicates, which were enough to account for the variability produced by stochastic processes. Seed dispersal was set as “ward dispersal”.

The results suggested that Biomass_core had a good agreement with LBSE using the three randomly generated initial communities (Fig. 1.5), with very small deviations for LBSE-generated biomasses. Notably, the mean differences between LBSE and Biomass_core were 0.03% (range: -0.01% ~ 0.13%), 0.03% (range: -0.01% ~ 0.11%) and 0.05% (-0.02% ~ 0.15%) for each initial community, respectively (right panels in Fig. 1.5 of this appendix).

Figure 1.5: Visual comparison of simulation outputs for three randomly generated initial communities (left panels) and difference between those outputs (right panels). The % difference between LBSE and Biomass_core were calculated as \(\frac{Biomass_{LBSE} - Biomass_{Biomass_core}}{Biomass_{LBSE}} * 100\)

To examine how running time changed with map size, we ran simulations using maps with increasing number of pixels, from 22,201 to 638,401 pixels. All maps were initialised with a single ecolocation and 7 different communities. Simulations were run for 120 years using a succession time step of 10 and replicated three times. To eliminate the effect of hardware on running time, we used machines that were all purchased at the same time, with equal specifications and running Windows 7. Each simulation ran on 2 CPU threads with a total RAM of 4000 Mb.

For both LBSE and Biomass_core, the simulation time increased linearly with number of pixels, but the increase rate was smaller for Biomass_core (Fig. 1.6a). This meant that while both models had similar simulation efficiencies in small maps (< 90,000 pixels), as map size increased Biomass_core was ~2 times faster than LBSE (maps > 100,000 pixels; Fig. 1.6a). Biomass_core also scaled better with map size, as LBSE speeds fluctuated between 19 to 25 seconds per 1,000 pixels across all map sizes, while Biomass_core decreased from 21 to 11 seconds per 1,000 pixels from smaller to larger maps (Fig. 1.6b).

Figure 1.6: Simulation efficiencies of LBSE and Biomass_core with increasing map size, in terms of a) mean running time across repetitions (left y-axis) and the ratio LBSE to Biomass_core running times (right y-axis and blue line), and b) running time scalability as the mean running time per 1000 pixels.

1.3 Usage example

1.3.1 Set up R libraries

options(repos = c(CRAN = "https://cloud.r-project.org"))
tempDir <- tempdir()

pkgPath <- file.path(tempDir, "packages", version$platform, paste0(version$major,
    ".", strsplit(version$minor, "[.]")[[1]][1]))
dir.create(pkgPath, recursive = TRUE)
.libPaths(pkgPath, include.site = FALSE)

if (!require(Require, lib.loc = pkgPath)) {
    remotes::install_github(paste0("PredictiveEcology/", "Require@5c44205bf407f613f53546be652a438ef1248147"),
        upgrade = FALSE, force = TRUE)
    library(Require, lib.loc = pkgPath)
}

setLinuxBinaryRepo()

1.3.2 Get the module and module dependencies

We can use the SpaDES.project::getModule function to download the module to the module folder specified above. Alternatively, see SpaDES-modules repository to see how to download this and other SpaDES modules, or fork/clone from its GitHub repository directly.

After downloading the module, it is important to make sure all module R package dependencies are installed in their correct version. SpaDES.project::packagesInModules makes a list of necessary packages for all modules in the paths$modulePath, and Require installs them.

Require(paste0("PredictiveEcology/", "SpaDES.project@6d7de6ee12fc967c7c60de44f1aa3b04e6eeb5db"),
    require = FALSE, upgrade = FALSE, standAlone = TRUE)

paths <- list(inputPath = normPath(file.path(tempDir, "inputs")),
    cachePath = normPath(file.path(tempDir, "cache")), modulePath = normPath(file.path(tempDir,
        "modules")), outputPath = normPath(file.path(tempDir,
        "outputs")))

SpaDES.project::getModule(modulePath = paths$modulePath, c("PredictiveEcology/Biomass_core@master"),
    overwrite = TRUE)

## make sure all necessary packages are installed:
outs <- SpaDES.project::packagesInModules(modulePath = paths$modulePath)
Require(c(unname(unlist(outs)), "SpaDES"), require = FALSE, standAlone = TRUE)

## load necessary packages
Require(c("SpaDES", "LandR", "reproducible", "pemisc"), upgrade = FALSE,
    install = FALSE)

1.3.3 Setup simulation

Here we setup a simulation in a random study area, using any species within the LandR::sppEquivalencies_CA table that can be found there (Biomass_core will retrieve species % cover maps and filter present species). We also define the colour coding used for plotting, the type of plots we what to produce and choose to output cohortData tables every year – note that these are not pixel-based, so to “spatialise” results a posteriori the pixelBroupMap must also be saved.

Please see the lists of input objects, parameters and outputs for more information.

times <- list(start = 0, end = 30)

studyArea <- Cache(randomStudyArea, size = 1e+07)  # cache this so it creates a random one only once on a machine

# Pick the species you want to work with – using the naming
# convention in 'Boreal' column of
# LandR::sppEquivalencies_CA
speciesNameConvention <- "Boreal"
speciesToUse <- c("Pice_Gla", "Popu_Tre", "Pinu_Con")

sppEquiv <- sppEquivalencies_CA[get(speciesNameConvention) %in%
    speciesToUse]
# Assign a colour convention for graphics for each species
sppColorVect <- sppColors(sppEquiv, speciesNameConvention, newVals = "Mixed",
    palette = "Set1")

## Usage example
modules <- as.list("Biomass_core")
objects <- list(studyArea = studyArea, sppEquiv = sppEquiv, sppColorVect = sppColorVect)

successionTimestep <- 10L

## keep default values for most parameters (omitted from
## this list)
parameters <- list(Biomass_core = list(sppEquivCol = speciesNameConvention,
    successionTimestep = successionTimestep, .plots = c("screen",
        "object"), .plotInitialTime = times$start, .plots = c("screen",
        "png"), .saveInitialTime = times$start, .useCache = "init",
    .useParallel = FALSE))

outputs <- data.frame(expand.grid(objectName = "cohortData",
    saveTime = unique(seq(times$start, times$end, by = 1)), eventPriority = 1,
    stringsAsFactors = FALSE))

1.3.4 Run simulation

simInitAndSpades is a wrapper function that runs both simInit (which initialises all modules) and spades (which runs all modules, i.e., their events), to which pass all the necessary setup objects created above.

Below, we pass some useful reproducible options that control caching ("reproducible.useCache") and where inputs should be downloaded to ("reproducible.destinationPath").

opts <- options(reproducible.useCache = TRUE, reproducible.destinationPath = paths$inputPath,
    spades.useRequire = FALSE)
graphics.off()
mySim <- simInitAndSpades(times = times, params = parameters,
    modules = modules, objects = objects, paths = paths, outputs = outputs,
    debug = TRUE)

Figure 1.7: Biomass_core automatically generates simulation visuals of species dynamics across the landscape in terms of total biomass, number of presences and age and productivity (above), as well as yearly plots of total biomass, productivity, mortality, reproduction and leading species in each pixel (below).

1.4 Appendix

1.4.1 Tables

Table 1.8: Input order and processing order (as determined by LBSE) for the same community used to assess the impact of sequential calculation of the competition index, combined with a lack of explicit species ordering. The input order was the order of species in the initial communities table input file. The processing order was the order used in the simulation, which was obtained from Landis-log.txt when CalibrateMode was set to ‘yes’. Species starting ages are also shown.

Input order 1				Input order 2
Community	Input order	Age	Processing	Community	Input order	Age	Processing
1	abiebals	20	poputrem	1	pinustro	20	thujocci
1	acerrubr	20	querelli	1	poputrem	20	tiliamer
1	acersacc	20	pinuresi	1	acerrubr	20	querelli
1	betualle	20	pinustro	1	pinubank	20	querrubr
1	betupapy	20	tiliamer	1	betualle	20	betupapy
1	fraxamer	20	tsugcana	1	piceglau	20	fraxamer
1	piceglau	20	querrubr	1	pinuresi	20	tsugcana
1	pinubank	20	thujocci	1	acersacc	20	abiebals
1	pinuresi	20	acersacc	1	querelli	20	acerrubr
1	pinustro	20	betualle	1	querrubr	20	pinubank
1	poputrem	20	abiebals	1	thujocci	20	pinustro
1	querelli	20	acerrubr	1	tiliamer	20	poputrem
1	querrubr	20	piceglau	1	tsugcana	20	pinuresi
1	thujocci	20	pinubank	1	abiebals	20	acersacc
1	tiliamer	20	betupapy	1	betupapy	20	betualle
1	tsugcana	20	fraxamer	1	fraxamer	20	piceglau

Table 1.9: Input order and processing order (as determined by LBSE) for the same community used to assess the impact of setting the succession time step to 1, combined with a lack of explicit species ordering. The input order was the order of species in the initial communities table input file. The processing order was the order used in the simulation, which was obtained from Landis-log.txt when CalibrateMode was set to ‘yes’. Species starting ages are also shown.

Input order 1				Input order 2
Community	Input order	Age	Processing	Community	Input order	Age	Processing
1	abiebals	1	poputrem	1	pinustro	1	thujocci
1	acerrubr	1	querelli	1	poputrem	1	tiliamer
1	acersacc	1	pinuresi	1	acerrubr	1	querelli
1	betualle	1	pinustro	1	pinubank	1	querrubr
1	betupapy	1	tiliamer	1	betualle	1	betupapy
1	fraxamer	1	tsugcana	1	piceglau	1	fraxamer
1	piceglau	1	querrubr	1	pinuresi	1	tsugcana
1	pinubank	1	thujocci	1	acersacc	1	abiebals
1	pinuresi	1	acersacc	1	querelli	1	acerrubr
1	pinustro	1	betualle	1	querrubr	1	pinubank
1	poputrem	1	abiebals	1	thujocci	1	pinustro
1	querelli	1	acerrubr	1	tiliamer	1	poputrem
1	querrubr	1	piceglau	1	tsugcana	1	pinuresi
1	thujocci	1	pinubank	1	abiebals	1	acersacc
1	tiliamer	1	betupapy	1	betupapy	1	betualle
1	tsugcana	1	fraxamer	1	fraxamer	1	piceglau

Table 1.10: Randomly generated community combination no. 1 used in the recruitment comparison runs.

Community	Species	Age 1	Age 2	Age 3	Age 4	Age 5	Age 6	Age 7
0	betupapy	1	37	45	46	85	NA	NA
0	piceglau	27	73	153	256	270	NA	NA
0	pinustro	157	159	181	220	223	303	307
0	querrubr	80	102	127	152	206	227	NA
1	acerrubr	3	91	126	145	NA	NA	NA
1	acersacc	138	144	276	NA	NA	NA	NA
1	betualle	24	106	136	149	279	NA	NA
1	piceglau	27	67	70	153	NA	NA	NA
1	pinubank	3	10	24	31	71	NA	NA
1	querelli	92	224	234	NA	NA	NA	NA
1	thujocci	73	146	262	NA	NA	NA	NA
2	fraxamer	108	118	137	147	204	NA	NA
2	piceglau	40	128	131	159	174	NA	NA
2	pinustro	78	156	237	245	270	NA	NA
2	querelli	67	97	186	292	NA	NA	NA
2	tiliamer	70	103	121	152	178	180	245
3	acerrubr	5	83	125	126	127	NA	NA
3	pinuresi	1	25	42	49	76	79	103
3	poputrem	4	9	62	NA	NA	NA	NA
3	querelli	101	104	167	226	NA	NA	NA
3	tsugcana	37	135	197	404	405	NA	NA
4	acerrubr	15	29	63	70	105	133	NA
4	piceglau	67	132	189	NA	NA	NA	NA
4	tsugcana	21	26	110	146	341	462	463
5	acerrubr	128	137	145	147	NA	NA	NA
5	acersacc	241	245	261	277	NA	NA	NA
5	querrubr	23	72	120	142	188	NA	NA
5	tiliamer	4	68	98	118	139	197	NA
6	betualle	5	23	31	249	NA	NA	NA
6	pinubank	67	70	89	NA	NA	NA	NA
6	querelli	194	217	257	NA	NA	NA	NA

Table 1.11: Randomly generated community combination no. 2 used in the recruitment comparison runs.

Community	Species	Age 1	Age 2	Age 3	Age 4	Age 5	Age 6	Age 7
0	acerrubr	22	26	30	40	47	145	146
0	betualle	23	41	43	120	209	227	270
0	fraxamer	25	90	119	173	185	282	NA
0	pinuresi	48	53	70	121	157	NA	NA
0	pinustro	5	82	126	298	352	NA	NA
0	querrubr	2	30	34	74	77	162	245
1	acerrubr	2	39	43	84	116	127	143
1	pinubank	34	57	75	NA	NA	NA	NA
1	querelli	108	202	218	243	NA	NA	NA
1	querrubr	5	117	131	186	189	246	NA
1	tiliamer	10	19	46	80	133	148	231
1	tsugcana	31	48	190	246	330	NA	NA
2	pinubank	11	37	38	47	67	93	NA
2	querrubr	11	48	57	177	180	228	236
2	tiliamer	28	42	78	79	223	250	NA
2	tsugcana	140	202	372	381	451	NA	NA
3	acersacc	48	107	262	265	NA	NA	NA
3	betupapy	4	12	45	65	83	96	NA
3	poputrem	13	20	37	75	90	NA	NA
3	querelli	72	90	104	115	116	265	278
3	tiliamer	20	21	56	98	237	NA	NA
3	tsugcana	86	224	425	429	NA	NA	NA
4	fraxamer	77	133	181	NA	NA	NA	NA
4	pinustro	13	37	67	220	287	293	375
4	querrubr	27	48	89	97	NA	NA	NA
4	thujocci	91	244	305	390	NA	NA	NA
5	abiebals	86	95	119	121	127	158	NA
5	betualle	83	113	136	161	216	231	NA
5	betupapy	10	38	64	NA	NA	NA	NA
5	piceglau	16	63	70	102	NA	NA	NA
6	acerrubr	8	34	112	NA	NA	NA	NA
6	betupapy	1	31	57	61	74	80	91
6	fraxamer	63	100	108	140	196	294	NA
6	pinubank	15	19	44	47	51	80	NA
6	thujocci	78	146	163	213	214	228	NA
6	tsugcana	47	108	387	389	449	NA	NA

Table 1.12: Randomly generated community combination no. 3 used in the recruitment comparison runs.

Community	Species	Age 1	Age 2	Age 3	Age 4	Age 5	Age 6	Age 7
0	pinubank	7	26	32	37	48	85	90
0	pinuresi	11	103	109	179	188	197	NA
0	querrubr	89	139	180	206	NA	NA	NA
1	betupapy	36	39	45	49	66	68	NA
1	piceglau	13	165	254	NA	NA	NA	NA
1	pinubank	3	19	54	64	76	NA	NA
1	poputrem	22	59	93	NA	NA	NA	NA
1	thujocci	68	98	274	275	363	378	NA
1	tiliamer	13	20	105	124	248	NA	NA
1	tsugcana	36	90	142	NA	NA	NA	NA
2	fraxamer	11	241	279	NA	NA	NA	NA
2	piceglau	16	42	129	177	200	244	NA
2	pinustro	200	342	384	NA	NA	NA	NA
3	abiebals	31	57	61	92	108	162	183
3	piceglau	126	255	261	267	NA	NA	NA
3	poputrem	28	41	57	NA	NA	NA	NA
3	querrubr	83	91	144	173	184	238	NA
3	thujocci	6	66	68	204	NA	NA	NA
4	fraxamer	12	110	266	270	NA	NA	NA
4	pinustro	174	270	359	379	NA	NA	NA
4	poputrem	4	7	18	24	63	76	NA
4	tiliamer	126	136	197	NA	NA	NA	NA
4	tsugcana	49	91	128	194	411	487	NA
5	abiebals	35	53	108	114	147	174	195
5	acerrubr	1	2	101	145	NA	NA	NA
5	pinubank	14	15	38	40	59	69	83
6	acerrubr	4	46	117	NA	NA	NA	NA
6	betualle	36	41	116	213	253	NA	NA
6	betupapy	4	6	76	NA	NA	NA	NA
6	pinuresi	43	68	85	171	NA	NA	NA
6	querrubr	84	86	113	185	193	223	228
6	tiliamer	13	106	181	199	246	NA	NA

Table 1.13: Invariant species traits table used in comparison runs.

Species	Longevity	Sexualmature	Shadetolerance	Seeddistance_eff	Seeddistance_max	Mortalityshape	Growthcurve
abiebals	200	25	5	30	160	10	0.25
acerrubr	150	10	4	100	200	10	0.25
acersacc	300	40	5	100	200	10	0.25
betualle	300	40	4	100	400	10	0.25
betupapy	100	30	2	200	5000	10	0.25
fraxamer	300	30	4	70	140	10	0.25
piceglau	300	25	3	30	200	10	0.25
pinubank	100	15	1	20	100	10	0.25
pinuresi	200	35	2	20	275	10	0.25
pinustro	400	40	3	60	210	10	0.25
poputrem	100	20	1	1000	5000	10	0.25
querelli	300	35	2	30	3000	10	0.25
querrubr	250	25	3	30	3000	10	0.25
thujocci	400	30	2	45	60	10	0.25
tiliamer	250	30	4	30	120	10	0.25
tsugcana	500	30	5	30	100	10	0.25

Table 1.14: Minimum relative biomass table used in comparison runs. X0-5 represent site shade classes from no-shade (0) to maximum shade (5). All ecolocations shared the same values.

Ecolocation	X0	X1	X2	X3	X4	X5
All	0	0.15	0.25	0.5	0.8	0.95

Table 1.15: Probability of germination for species shade tolerance and shade level combinations (called sufficient light table in LBSE and sufficientLight input data.table in LandR Biomass_core) used in comparison runs.

Shadetolerance	0	1	2	3	4	5
1	1	0	0	0	0	0
2	1	1	0	0	0	0
3	1	1	1	0	0	0
4	1	1	1	1	0	0
5	0	0	1	1	1	1

Table 1.16: Species ecolocation table used in comparison runs. SEP stands for species establishment probability, maxB for maximum biomass and maxANPP for maximum aboveground net primary productivity. Values were held constant throughout the simulation.

Ecolocation	Species	SEP	maxANPP	maxB
1	abiebals	0.90	886	26580
1	acerrubr	1.00	1175	35250
1	acersacc	0.82	1106	33180
1	betualle	0.64	1202	36060
1	betupapy	1.00	1202	36060
1	fraxamer	0.18	1202	36060
1	piceglau	0.58	969	29070
1	pinubank	1.00	1130	33900
1	pinuresi	0.56	1017	30510
1	pinustro	0.72	1090	38150
1	poputrem	1.00	1078	32340
1	querelli	0.96	1096	32880
1	querrubr	0.66	1017	30510
1	thujocci	0.76	1090	32700
1	tiliamer	0.54	1078	32340
1	tsugcana	0.22	1096	32880

1.5 References

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1. in LBSE the initialisation consists in “iterat[ing] the number of time steps equal to the maximum cohort age for each site”, beginning at 0 minus t (t = oldest cohort age) and adding cohorts at the appropriate time until the initial simulation time is reached (0) (Scheller & Miranda 2015b).↩︎

2. usually, default inputs are made when running the .inputObjects function (inside the module R script) during the simInit call and in the init event during the spades call – see ?SpaDES.core::events and SpaDES.core::simInit↩︎

3. in SpaDES lingo parameters are “small” objects, such as an integer or boolean, that can be controlled via the parameters argument in simInit.↩︎

4. https://github.com/ianmseddy/LandR.CS↩︎

5. see ?SpaDES.core::outputs↩︎

6. simInit is a SpaDES function that initialises the execution of one or more modules by parsing and checking their code and executing the .inputObjects function(s), where the developer provides mechanisms to satisfy each module’s expected inputs with default values.↩︎