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This version implements a variety of updates to model estimation
functions after a complete validation of the package’s functionality. An
extensive suite of unit tests are implemented for checking
rFIA estimates with estimates from EVALIDator to ensure
consistency of rFIA with updates in FIADB. This validation assessment
fixed multiple bugs, particularly related to the reporting of sample
sizes that were not always consistent dependent on different filtering
criteria implemented in the estimation functions. Updates are broken
down in the following based on specific functions.
Full details on this validation are provided in the development
version of rFIA on GitHub in the
core_references/validation directory. Note that Claude Code
was used to aid in the validation assessment, with code verified by Jeff
Doser.
customPSE()customPSE() (GitHub issue #47) where a
spatial mask (or any multi-state call) spanning states with different
mostRecent evaluation years returned one row per state/year
instead of a single combined estimate, unlike
area()/tpa()/etc. on the same masked data.
Root cause: customPSE() checked db for
clipFIA(mostRecent = TRUE)’s marker after an
internal helper had already pared db down to only the named
FIA tables, which silently dropped that marker – so the step that
relabels differing per-state “most recent” years to a common year before
combining never ran. Every other estimator dispatcher checks this before
any table-paring happens, so none of them were affected.customPSE() where
nPlots_x/nPlots_y were inflated to the full
forested-plot count instead of the true number of non-zero contributing
plots, whenever x/y was a tree-based list
(e.g. the treeList = TRUE output of
tpa()/volume()). Point estimates and standard
errors were unaffected – this was purely a plot-count/reporting bug, but
it mattered because customPSE.Rd documents
nPlots_x/nPlots_y as the count of
non-zero plots, used as the degrees of freedom for a t-based
confidence interval. Root cause: sumToPlot()
(R/util.R), shared by every estimator, did not filter out
the phantom TREE_BASIS/AREA_BASIS = NA rows
that a treeList/condList output necessarily
includes for forested conditions with zero qualifying trees – each
*Starter.R file already filters these before its own
population-estimate call, but customPSE() calls
sumToPlot() directly on user-supplied data. Confirmed
against tpa()/volume()/area()
across four states (one per FIA region).dplyr::across() deprecation warning in
customPSE() (... was passed through to
.fns rather than wrapped in an anonymous function,
deprecated as of dplyr 1.1.0).tpa(), biomass(), carbon(),
volume(), growMort(),
vitalRates(), diversity(), fsi(),
area(), areaChange(),
standStruct(), and seedling() now warn when
grpBy includes SUBP. Unlike every other
grpBy variable these functions support (species, size
class, ownership group, etc.), which partition trees/conditions within
the same shared sampled area, SUBP denotes a physically
distinct sub-area of the plot – each subplot covers different ground.
Grouping by SUBP still produces a mathematically valid
partition of the plot-level per-acre estimate (each subplot’s value is
its share of the total, and the four values sum back to the ungrouped
estimate exactly), but it is not a re-weighted, subplot-local density,
since the area denominator is not re-weighted to match each subplot’s
own area. Documented in each function’s Details section
(issue #31). This warning is used to reflect that rFIA is
not designed for extracting data for each subplot and subsequently using
that data within a model-based estimator.areaDomain where the plots/conditions used to
evaluate the domain expression were hard-coded to forest land
(PLOT_STATUS_CD == 1/COND_STATUS_CD == 1),
regardless of landType. This caused area()
(and areaChange()) to silently return zero area for any
combination of a non-forest landType
(e.g. 'water', 'non-forest',
'all') with an areaDomain filter, instead of
applying the filter and returning the correctly restricted estimate.
This is a rare use case.treeType = 'dead' did not require
dead trees to meet the “standing dead” tally-tree criteria
(STANDING_DEAD_CD == 1), instead counting all trees with
STATUSCD == 2 regardless of whether they were still
standing. This inflated treeType = 'dead' estimates in
states with a meaningful number of down or broken dead trees recorded in
the tree table (e.g. North Carolina, where the estimate was roughly 4x
too high). This affects every function that supports
treeType: tpa(), diversity(),
biomass(), volume(), and fsi().
As a consequence, treeType = 'all' (which includes every
tree regardless of status) is no longer equal to
treeType = 'live' plus treeType = 'dead',
since 'all' still includes the non-standing dead trees that
'dead' now excludes.growMort() and
vitalRates()growMort() and vitalRates()
where bySizeClass = TRUE silently dropped removed
(harvested) and dead trees whose current-cycle diameter went unmeasured
– common for trees that are too decayed, broken, or fully removed to
measure at the time of remeasurement. sizeClass was
computed from the tree’s current diameter and any row with a missing
value was dropped before it was ever joined to the
growth/mortality/removal tables, even though those same tables already
provide a usable midpoint or begin diameter for exactly these trees.
Confirmed against OR, where this undercut growMort()’s
harvested-basal-area estimate by up to 99% and its mortality estimate by
up to 25% when bySizeClass = TRUE;
bySizeClass = TRUE totals (summed back across size classes)
now match bySizeClass = FALSE exactly in both functions
(issue #40).growMort() now returns nPlots_RECR,
nPlots_MORT, nPlots_REMV, and
nPlots_GROW – separate non-zero-plot counts for
recruitment, mortality, harvest removal, and survivor growth,
respectively. The first three were already documented in
growMort.Rd but never actually implemented;
nPlots_GROW is a new addition alongside them for
consistency. Previously, only a single generic nPlots_TREE
(count of plots contributing to any of
recruitment/mortality/removal/survivor-growth combined) was returned,
which understates the degrees of freedom appropriate for a t-based
confidence interval on an individual
MORT_*/REMV_*/RECR_*/GROW_*
rate. nPlots_TREE is unchanged. Confirmed against
EVALIDator’s plot counts for the mortality/harvest-removal attributes
(exact match).growMort() where
GROW_*/CHNG_* (survivor growth / net change)
were computed incorrectly for every state variable except the default
TPA – BAA, volume, biomass, and carbon outputs
were all affected. The previous-period population total was
reconstructed using each departed tree’s midpoint measurement
(correct for the separately-reported
MORT_*/REMV_* columns) instead of its
begin measurement, which EVALIDator’s own growth-accounting
definition of “net growth” requires; a second, related bug caused
mismatched NA handling across the
RECR/MORT/REMV/GROW
columns for volume-based state variables (NETVOL,
SAWVOL, SAWVOL_BF), whose underlying FIADB
columns are undefined for some trees below merchantability thresholds.
MORT_*/REMV_* themselves, and every output
under the default stateVar = 'TPA', were unaffected. This
is likely related to the “growMort() reporting zero
survivor growth” bug fixed in v1.1.1.growMort() where state variables derived from
tree biomass (BIO_AG, BIO_BG,
BIO, CARB_AG, CARB_BG,
CARB) to now be reported in pounds instead of short
tons/acre. This is for consistency with other estimation functions
across the package.growMort() where an
areaDomain filter was evaluated against a tree’s
previous remeasurement condition rather than its current one,
understating MORT_*/REMV_*/RECR_*
estimates in states with meaningful physiographic-class turnover between
remeasurements (up to -6% in checks against EVALIDator). This is the
same bug already fixed in vitalRates().growMort() where the
SUBP_COND_CHNG_MTRX-based growth-accounting area-change
calculation hardcoded SUBPTYP == 1, silently discarding
area-change information for any condition measured on the macroplot and
understating landType = 'forest' estimates (and
nPlots_AREA) in macroplot-heavy states (e.g. Pacific
Northwest). This is the same bug already fixed in
vitalRates().vitalRates() where
SAWVOL_GROW/SAWVOL_GROW_AC (sawlog board-foot
volume growth) was computed from the same growing-stock
growth-accounting component used for the other four growth metrics
(DIA_GROW, BA_GROW, NETVOL_GROW,
BIO_GROW), rather than the sawtimber-specific component
EVALIDator’s sawlog-volume growth attributes are actually defined
against – the same distinction growMort() already makes for
its own SAWVOL/SAWVOL_BF state variables,
independent of treeType. This under- or over-stated
SAWVOL_GROW_AC by roughly 0.3-3% depending on state. Point
estimates and sampling errors for the other four growth metrics were not
affected.vitalRates() where an areaDomain
restriction (e.g. PHYSCLCD %in% 21:29) was applied to
tree-level growth using the previous measurement’s condition
instead of the current one, while the area (denominator) side already
correctly used the current condition – creating a small, state-dependent
mismatch (worse in states with more physiographic-class turnover between
remeasurements) whenever a plot’s areaDomain-relevant
classification changed between visits. Point estimates for
areaDomain-restricted calls were affected; unrestricted
calls were not.vitalRates() where
landType = 'forest' silently dropped all area-change
information for any condition whose proportion was measured on the
macroplot (COND.PROP_BASIS == 'MACR') rather than the
standard subplot – the internal area-change join hardcoded
SUBP_COND_CHNG_MTRX.SUBPTYP == 1, when the FIA Population
Estimation User Guide’s own growth-accounting methodology requires
SUBPTYP == 3 for macroplot-basis conditions. This was
invisible in states where forest conditions are exclusively
subplot-basis (confirmed for RI, NC, CO), but caused a small, consistent
undercount of BIO_GROW_AC (~-0.1%) and
nPlots_AREA (~-0.6%) in Pacific/Western states that
commonly use macroplot sampling (confirmed for OR, CA, WA). A residual,
smaller discrepancy specific to landType = 'timber' exists
with EVALIDator but is kept different in rFIA. See details
in core_references for more details.vitalRates() where
nPlots_TREE did not reflect restrictions imposed by
treeDomain at all – even a treeDomain matching
zero trees left nPlots_TREE unchanged from the unrestricted
value. Every row of a bySpecies = TRUE call reported the
same, unrestricted nPlots_TREE regardless of how common
that species actually was, defeating its use as the degrees of freedom
for a t-based confidence interval. Root cause: the tree list’s
plot-count filter depended only on whether a tree had a valid
growth-accounting record for the current
landType/treeType (via FIA’s precomputed
TREE_GRM_COMPONENT columns), not on whether the user’s
treeDomain/areaDomain indicator
(tDI) actually matched – tDI only zeroed the
growth values for non-matching trees, without excluding them from the
plot count. Point estimates and sampling errors were not affected.vitalRates() where
nPlots_AREA did not reflect restrictions imposed by
landType or areaDomain at all (the same class
of bug already fixed in tpa(), area(),
carbon(), biomass(), volume(),
dwm(), invasive(), seedling(),
standStruct(), diversity(), and
vegStruct(); see above), including the same
spurious/empty-result-with-warning edge case when a restriction matched
no data. Point estimates and sampling errors were not affected.vitalRates() and
growMort() under
method = 'SMA'/'LMA'/'EMA' where
growth/mortality totals (and every ratio derived from them,
e.g. BIO_GROW_AC, MORT_TPA) were inflated by
roughly the number of remeasurement panels in the evaluation window – up
to ~9x in testing. Root cause: a shared internal utility
(sumToEU()) that combines per-panel moving-average
estimates into a single population estimate grouped the tree/growth side
of the calculation by an extra, panel-varying column that should not
have been part of the grouping key, preventing panels from actually
being combined; vitalRates()/growMort()’s
internal need to compute two related population estimates and join them
together turned this into a many-to-many join that squared the row
count. Every other estimator was unaffected in practice (a separate
summation step downstream happened to still produce the correct total),
which is why this was not caught until now. method = 'TI'
and 'ANNUAL' were not affected.fsi()fsi() where the plot-level remeasurement
interval (REMPER) was incorrectly multiplied by the
subplot/microplot/macroplot nonresponse adjustment factor before being
used as a grouping key to recombine a plot’s per-plot-basis rows back
into a single row. Because that adjustment factor differs by plot basis,
a single plot’s rows ended up with different REMPER values
and so failed to recombine, leaving the same physical plot represented
by multiple rows. Tree-density sums still totaled correctly across those
extra rows, but the subsequent area join (not keyed on
REMPER) re-attached a full copy of that plot’s forest-area
weight to each spurious row, inflating the area denominator relative to
the tree-count numerator. Values from byPlot = TRUE were
not affected, since they do not go through this code path.fsi()’s
maximum size-density curve (Bayesian quantile regression,
inst/extdata/qrLM.jag/qrLMM.jag) to match
Stanke et al. (2021): mean 7, not 6. The slope prior was already
correct.fsi()’s exclusion of disturbed/treated plots from
the maximum size-density curve calibration set to match Stanke et
al. (2021): a plot showing evidence of disturbance or
(non-natural-regen) silvicultural treatment is now excluded, rather than
requiring both simultaneously. Previously, a plot with significant
disturbance but no recorded follow-up treatment (e.g. an untreated
wildfire) was incorrectly retained in the calibration set.totals argument from fsi(). It
had no effect on the returned output (both branches of the
totals-dependent code returned identical columns) despite
being documented as adding raw population totals; every other
estimator’s totals argument works as documented;
fsi()’s did not and has been removed rather than
implemented, since
FSI/PERC_FSI/PREV_RD/CURR_RD
are already ratio quantities with no natural population-total
analog.fsi(areaDomain = ...) crashed with
"replacement has 1 row, data has 0" when the
areaDomain (or landType) restricted the
population to zero plots, instead of returning a clean empty result.
byPlot = FALSE now returns a 0-row result in this case (or
when a treeDomain matches zero trees, which previously
returned a spurious 1-row result of NaNs instead of an
empty result); byPlot = TRUE keeps all plot rows with
FSI = 0/NA, consistent with how
tpa() and vitalRates() already handle an empty
domain.dplyr::across() deprecation warning raised by
fsi(method = 'ANNUAL').vegStruct()vegStruct() to now include region-specific
GROWTH_HABIT_CDs as opposed to only including the 5 core
national vegetation growth-habit codes.vegStruct() where
byPlot = TRUE’s per-plot cover estimate
(PROP_COVER) used mean(cover, na.rm = TRUE)
across a layer/growth-habit combination’s subplot-level cover values,
which treats a subplot where that combination wasn’t recorded as a
missing observation to exclude from the average rather than a real
0%-cover observation to include (the same bug already fixed in
invasive()’s byPlot branch). This inflated
PROP_COVER by up to 4x for a combination recorded on fewer
than all 4 subplots – the normal case for patchy vegetation. Fixed by
dividing by a fixed 4 subplots instead, matching the population-estimate
branch’s own formula. byPlot = TRUE was the only affected
output; the main population-level COVER_PCT was not
affected.vegStruct() where
nPlots_AREA did not reflect restrictions imposed by
landType or areaDomain (the same class of bug
already fixed in tpa(), area(),
carbon(), biomass(), volume(),
dwm(), invasive(), seedling(),
standStruct(), and diversity(); see below),
and an areaDomain/landType restriction
matching no data could produce a spurious result instead of a clean
empty one. Point estimates and sampling errors were not affected by
either fix.method = 'SMA'/'LMA'/'EMA'/'ANNUAL'
could error with "replacement has length zero" instead of
returning a result (or the existing “bad stratification” warning, when a
stratum is genuinely too small to merge). This is not specific to
vegStruct(): the underlying shared utility that pools
too-small strata together (R/util.R, used by every
sumToEU()-based estimator) picked a stratum’s cross-year
merge partner without accounting for a stratum whose INVYR
is unknown – reachable whenever a function restricts its plot universe
before computing population weights (as vegStruct() and
invasive() do, for their P2-ancillary-protocol sampling
restrictions), which leaves some strata with no real year of their own.
tpa()/area()/etc., which don’t restrict their
plot universe this way, were confirmed unaffected – their output is
unchanged.diversity()diversity() where grouping by a
TREE-table variable (bySizeClass = TRUE, or a
user-supplied grpBy referencing a TREE column,
e.g. species group) corrupted the area denominator: each forest
condition’s area was collapsed into whichever one grouping bin happened
to be encountered first, instead of correctly contributing to every bin
its trees actually belong to. This could push alpha-level Shannon’s
Equitability (Eh_a) above 1, which is mathematically
impossible under its own formula. Fixed by separating the area-only
grouping columns from the full grouping columns internally (matching
tpa()’s existing aGrpBy/grpBy
split), so a TREE-level grouping variable no longer
fragments the area total. grpBy restricted to
PLOT/COND columns (e.g. OWNGRPCD)
was not affected.diversity() where an
areaDomain/landType restriction matching no
data produced a spurious result (H = S = 0 with a
"no non-missing arguments to max" warning) instead of a
clean empty result. nPlots_AREA also did not reflect
restrictions imposed by landType or areaDomain
(the same class of bug already fixed in tpa(),
area(), carbon(), biomass(),
volume(), dwm(), invasive(),
seedling(), and standStruct(); see below).
Point estimates and sampling errors were not affected by either
fix.standStruct()standStruct() where a forest condition
with zero qualifying live trees (e.g. a young/sparse/non-stocked stand)
survives the internal tree-list join as a phantom row indistinguishable
from any other such condition on the same plot by
(PLT_CN, SUBP, TREE) alone
(SUBP/TREE are both NA). Whenever
a single plot had two or more zero-tree forest conditions,
distinct(PLT_CN, SUBP, TREE) collapsed them into one,
silently dropping every zero-tree condition’s area past the first from
its stand structural stage (“mosaic”) classification – even though that
area still correctly counted in the area total, so
COVER_PCT summed across all four structural stages fell
short of 100% (e.g. North Carolina: 99.986% instead of 100%). Fixed by
adding CONDID to the deduplication key.standStruct() where an
areaDomain/landType restriction matching no
data produced a spurious single-row 'mosaic' result (with a
"no non-missing arguments to max" warning) instead of a
clean empty result, because the internal structural-stage classification
step could still assign a fallback 'mosaic' label to a plot
with no real qualifying conditions. nPlots_AREA also did
not reflect restrictions imposed by landType or
areaDomain (the same class of bug already fixed in
tpa(), area(), carbon(),
biomass(), volume(), dwm(),
invasive(), and seedling(); see below). Point
estimates and sampling errors were not affected by either fix.seedling()seedling() where the tree list’s
distinct(PLT_CN, SUBP, SPCD) deduplication key omitted
CONDID. Unlike TREE, SEEDLING has
no per-stem ID – TPA_UNADJ is already a count
pre-aggregated to the
PLT_CN/SUBP/CONDID/SPCD
grain by FIA – so whenever a subplot straddled two conditions and the
same species had seedlings recorded under both, this silently discarded
one condition’s count entirely. Confirmed on real data (North Carolina):
this undercounted statewide seedling TPA by ~0.2%, moving it from
1228.331 to the correct 1230.528 (matching EVALIDator exactly) after the
fix. Small/simple states (e.g. Rhode Island) rarely hit the triggering
condition and were unaffected.seedling() where
nPlots_TREE counted every forest plot rather than just
plots with at least one live seedling actually recorded, because its
TREE_BASIS column (always 'MICR' for
seedlings) couldn’t detect a phantom “no seedling” row the way
tpa()’s DIA-derived TREE_BASIS
does. Point estimates and sampling errors were not affected, only the
reported plot count.seedling() where
nPlots_AREA did not reflect restrictions imposed by
landType or areaDomain (the same class of bug
already fixed in tpa(), area(),
carbon(), biomass(), volume(),
dwm(), and invasive(); see below –
seedling() was the one remaining estimator missing this
fix). Point estimates and sampling errors were not affected.invasive()REF_PLANT_DICTIONARY reference
table (used by invasive() to attach a scientific/common
name to each invasive species code) from an out-of-date snapshot to the
current version provided by FIA. The previous version only included
species-level PLANTS codes; genus-level codes (used whenever field crews
identify an invasive plant’s genus but not its exact species,
e.g. LIGUS2 for Ligustrum spp., a major invasive
shrub genus in the southeastern US) were entirely absent. Because the
name columns are part of invasive()’s internal grouping and
its final output step drops any row with a missing group value, every
genus-level species was silently dropped from the output entirely – not
just its name, but its real COVER_PCT data. This affected
up to 36% of raw invasive-species records in the states checked (North
Carolina).
tpa()/biomass()/volume()’s
analogous tree-species reference table was checked and does not have
this problem.invasive() where
byPlot = TRUE‘s per-plot cover estimate
(PROP_INV_COVER) used
mean(cover, na.rm = TRUE) across a species’ subplot-level
cover values, which treats a subplot where the species wasn’t recorded
as a missing observation to exclude from the average rather than a real
0%-cover observation to include. This inflated
PROP_INV_COVER by up to 16x for a species detected on fewer
than all 4 subplots – the normal case for patchy invasive species. Fixed
by dividing by a fixed 4 subplots instead. As part of this fix,
byPlot = TRUE now also returns a PROP_FOREST
column (the proportion of the plot meeting the land type/area domain,
i.e. CONDPROP_UNADJ-weighted forest proportion), matching
the same split already used by biomass()’s
byPlot = TRUE output
(BIO_ACRE/PROP_FOREST) –
PROP_INV_COVER itself is not weighted by plot forest
proportion. byPlot = TRUE was the only affected output; the
main population-level COVER_PCT was not affected.invasive() where
nPlots_AREA did not reflect restrictions imposed by
landType or areaDomain, instead always
reporting the plot count for the broader unrestricted land base (the
same class of bug already fixed in tpa(),
area(), carbon(), biomass(),
volume(), and dwm(); see above –
invasive() was the one remaining estimator missing this
fix). Point estimates and sampling errors were not affected.invasive() where an
areaDomain/landType restriction matching no
data could produce a spurious
"no non-missing arguments to max" warning instead of a
clean empty result, unlike every other estimation function (which this
warning class was already fixed for). Root cause: the
population-estimation branch was missing a filter to drop conditions
with no detected invasive species, present only in the (separate)
byPlot branch’s code; when every species group was empty,
the resulting phantom “no species” row(s), rather than a genuinely empty
result, bypassed the existing 0-row guard in the shared
combineMR() utility.biomass() and
carbon()biomass() no longer estimates carbon
(CARB_ACRE/CARB_TOTAL/associated SE and
variance columns have been removed from its output). Tree biomass
estimation is otherwise unchanged. Use carbon() for carbon
stock estimation, which covers the full suite of forest ecosystem carbon
pools (live and dead trees, understory vegetation, down dead wood,
litter, and soil organic matter), not just standing tree carbon.carbon() and biomass()
where nPlots_AREA did not reflect restrictions imposed by
landType or areaDomain, instead always
reporting the plot count for the broader unrestricted land base (the
same class of bug already fixed in tpa() and
area(); see above). Point estimates and sampling errors
were not affected. For carbon() specifically, this also
caused an areaDomain matching no conditions to return a row
of NaN values instead of a clean empty result, since
carbon()’s numerator is built by joining onto the same
phantom-row-containing condition list; this is now a clean 0-row result,
consistent with every other estimation function.biomass() where nPlots_TREE
over-counted plots for any component (or
byComponent) request restricted to STEM,
STEM_BARK, STUMP_BARK, BOLE,
BOLE_BARK, or BRANCH, in states with a
meaningful amount of woodland-form forest (e.g. pinyon-juniper woodland
in Arizona, Utah, and Colorado). NSVB does not model these components
for woodland species (DRYBIO_STEM/etc. are NA,
not 0, for e.g. juniper and pinyon), and while their 0 contribution to
BIO_ACRE/BIO_ACRE_SE was already handled
correctly, plots whose only tallied trees were woodland species were
still being counted toward nPlots_TREE. This inflated
nPlots_TREE by up to ~3x in woodland-heavy states
(e.g. Arizona BRANCH: 3137 reported vs. 842 actual
contributing plots); point estimates and sampling errors were not
affected.areaChange()areaChange() where a condition that was
nonsampled (COND_STATUS_CD == 5, e.g. hazardous or
denied-access) at either measurement was misclassified as a genuine
forest/non-forest (or timberland/non-timberland) land-use change event,
since the shared landTypeDomain() helper has no distinct
category for “nonsampled” – it simply isn’t forest, indistinguishable
from a real non-forest reclassification. This fabricated
diversion/reversion events that never actually occurred, and could bias
AREA_CHNG/PERC_CHNG in either direction
depending on how the affected plots happened to fall; confirmed on real
data that this flipped the sign of the reported net change in forest
area for Rhode Island. area() was not affected (a
nonsampled condition simply contributes no area there, rather than being
paired against a different point in time).area()area() where a hard-coded
PLOT_STATUS_CD == 1 filter (a leftover from code shared
with tpa(), where it is valid since trees only occur on
forest land) silently dropped every plot with no accessible forest
before land-type domain indicators were applied. This caused large
undercounts (up to two orders of magnitude) for every
landType value other than the defaults of
'forest'/'timber' (e.g. 'water',
'non-forest', 'all'), and caused the
documented byLandType = TRUE output to sum to well under
the true total land area.
landType = 'forest'/'timber' estimates were
not affected.area() where
nPlots_AREA_DEN did not reflect restrictions imposed by
landType = 'timber' or areaDomain, instead
always reporting the plot count for the broader
landType = 'forest' land base (the same class of bug as the
tpa() nPlots_AREA fix described below). Point
estimates and sampling errors were not affected.area() where landType = 'all' to
now explicitly remove nonsampled conditions from the land area
calculation (e.g. hazardous or denied-access plots). In prior versions,
nonsampled conditions were included in the count of
landType = 'all', but this resulted in the sum of the
different components when byLandType = TRUE to not sum to
the total when landType = 'all'.method = 'ANNUAL' silently summed
together every constituent panel’s population estimate into a single,
badly inflated row (with an inflated plot count to match) instead of
reporting each sampled panel-year’s own estimate separately, whenever it
was run against a database that had been restricted to a single “most
recent” evaluation (e.g. via clipFIA(mostRecent = TRUE)).
Confirmed on Rhode Island:
area(clipFIA(fiaRI, mostRecent = TRUE), method = 'ANNUAL')
previously returned a single row (labeled with the evaluation’s nominal
year) that was actually the sum of all 7 constituent panels’ area
estimates; it now correctly returns 7 separate rows, one per sampled
panel-year, matching the values obtained by running
method = 'ANNUAL' against the full, unclipped inventory
history. Root cause: a shared internal utility
(combineMR(), used to reconcile different states’ differing
“most recent” reporting years under the
'TI'/'SMA'/'LMA'/'EMA'
estimators) was also being unconditionally applied to
'ANNUAL' output, which legitimately contains multiple
distinct-year rows per state – combineMR() relabeled all of
them to the same year, causing a subsequent aggregation step to sum them
together. This affects every estimation function that supports
method = 'ANNUAL' (not just area()), since
combineMR() is shared internal utility; confirmed the
identical corruption and fix for tpa(). 'TI',
'SMA', 'LMA', and 'EMA' were not
affected, since they already returned a single row per state before
reaching this step.volume()volume() where nPlots_AREA
did not reflect restrictions imposed by landType or
areaDomain, instead always reporting the plot count for the
broader unrestricted land base (the same class of bug already fixed in
tpa(), area(), carbon(), and
biomass(); see above – volume() was the one
remaining estimator missing this fix). Point estimates and sampling
errors were not affected.volume() where
nPlots_TREE over-counted plots: (1) trees with no defined
bole volume (e.g. dead trees under 5 inches DBH, for which
VOLCFNET is never computed) were still counted, the same
class of bug just fixed in biomass() but triggered by tree
diameter rather than species; and (2) trees with a defined but
exactly-zero net volume (a full cull/defect deduction can legitimately
zero out VOLCFNET) were counted, where EVALIDator’s own
definitions require a strictly positive volume to count a tree as
contributing. Point estimates and sampling errors were not affected;
nPlots_TREE was inflated by a few percent in the states
checked (e.g. Rhode Island treeType = 'dead': 107 reported
vs. 103 actual contributing plots).dwm()dwm() where nPlots_AREA did
not reflect restrictions imposed by landType or
areaDomain, instead always reporting the plot count for the
broader unrestricted land base (the same class of bug already fixed in
tpa(), area(), carbon(),
biomass(), and volume(); see above –
dwm() was the one remaining estimator missing this fix).
Point estimates and sampling errors were not affected.dwm() where COND_DWM_CALC
was filtered by PLT_CN alone when restricting to the
current evaluation, but a single plot can appear in
COND_DWM_CALC under several different EVALIDs
(consecutive annual panels can each report the same not-yet-remeasured
plot as their most recent down woody material data), each with slightly
different evaluation/stratum-specific adjustment factors. This caused
every down woody material condition to be summed once per matching
EVALID instead of once, inflating the reported
nPlots_DWM plot count by roughly 4-5x in the states checked
(e.g. Colorado: 17775 reported vs. 3897 actual contributing plots) and,
more subtly, adding spurious phantom estimation-unit groups with no
effect on the final point estimate or standard error (their area
contribution was always NA and dropped), but real effect on
the plot count. Point estimates and sampling errors were not affected;
only nPlots_DWM was.dwm() where nPlots_DWM
counted every domain-qualifying, DWM-sampled plot regardless of whether
it actually had any down woody material of the relevant fuel type,
instead of requiring a strictly positive value (matching EVALIDator’s
own per-attribute definitions, and the same class of fix just made in
volume()). This was checked and fixed separately for the
combined default output (byFuelType = FALSE, which requires
total FWD + CWD + pile volume across all fuel types to be positive,
matching EVALIDator’s combined “Total volume of DWM” attribute) and for
byFuelType = TRUE’s individual fuel-type rows (each of
which now requires only its own fuel type’s volume – or biomass, for
DUFF/LITTER, which have no volume equivalent –
to be positive, matching each fuel type’s own EVALIDator attribute).
Point estimates and sampling errors were not affected.dwm() under method = 'SMA',
'LMA', 'EMA', and 'ANNUAL' where
the down woody material numerator was post-stratified using each plot’s
original stratum assignment (from COND_DWM_CALC), while the
area denominator used the strata produced after small strata were merged
(needed for variance estimation of annual panels). Plots moved into a
neighboring stratum were therefore assigned to different strata in the
numerator and denominator. Under the moving-average methods, numerator
rows with no matching denominator stratum/panel/group were silently
dropped, so down woody material totals were underestimated, and totals
across grpBy groups did not sum to the ungrouped total
(e.g. Colorado, areaDomain = PHYSCLCD %in% 21:29,
method = 'SMA': 13.75 vs. 14.47 billion cubic feet after
the fix). The numerator now uses the same (merged) strata as the
denominator. Estimates with method = 'TI' (the default)
were not affected.tpa()tpa() where nPlots_AREA did
not reflect restrictions imposed by landType = 'timber' or
areaDomain, instead always reporting the plot count for the
broader landType = 'forest' land base. Point estimates and
sampling errors were not affected, but nPlots_AREA is
documented as the recommended degrees of freedom for constructing
t-based confidence intervals, so an inflated count understated the true
margin of error for any landType = 'timber' or
areaDomain-restricted estimate.treeDomain/areaDomain
matching no data, combined with the default
mostRecent = TRUE behavior, produced a spurious
"no non-missing arguments to max" warning instead of a
clean empty result. This affected all estimation functions (not just
tpa()), since the underlying cause was in a shared internal
utility.tpa.Rd why tree records with a missing
diameter (DIA) – most often standing dead trees revisited
after death, common in western US inventories – are excluded from
tpa() estimates regardless of treeType: these
records also lack TPA_UNADJ (FIA’s per-acre expansion
factor, which itself depends on DIA to determine subplot
design), so they have no valid per-acre weight under FIA’s design-based
estimator and are excluded from EVALIDator’s estimates for the same
reason. Confirmed treeType = 'dead' still matches
EVALIDator to full precision in OR, where this affects a large fraction
of dead-tree records (issue #32).method = 'EMA' silently accepted a
lambda outside its documented (0,1) range and
returned degenerate weights instead of an error – NaN for
every panel at the exact boundaries (lambda = 0 or
1), a negative weight for lambda < 0, or an
inverted (oldest-weighted-highest) recency ordering for
lambda > 1. lambda is now validated up
front with a clear error message. This affected every estimation
function (not just tpa()), since the underlying weighting
logic is a shared internal utility used by all of them, plus
fsi() and customPSE().method = 'ANNUAL' did not correctly
select the best FIA evaluation to draw a panel’s standalone estimate
from, when that panel is a constituent of more than one evaluation’s
multi-panel window (common: e.g. panel year 2009 is part of both a 2013
evaluation’s 5-panel window and a 2014 evaluation’s window). The
comparison across candidate evaluations was accidentally a no-op, so
which one’s numbers ended up in the output was arbitrary (whichever came
first internally) rather than the one with the most plots, as intended.
This affected every estimation function using
method = 'ANNUAL', since the underlying selection logic is
a shared internal utility.plotFIA()plotFIA() where
animate = TRUE errored with
could not find function "transition_manual" for any
non-spatial (time-series) summary, since
transition_manual() was called unqualified rather than as
gganimate::transition_manual() – gganimate is
a Suggests dependency only, never attached by
library(). The analogous call in the spatial (choropleth
map) branch was already correctly qualified. This is the same class of
bug fixed in v1.1.1, which only fixed the spatial branch’s call.plotFIA() where saving an animated plot
(animate = TRUE with
savePath/fileName) always errored with
could not find function "anim_save", for the same reason as
above – anim_save() is now called as
gganimate::anim_save().min.year argument, which was documented
(“earliest year to be included in animation”) but had no effect on the
returned plot. animate = TRUE now drops years before
min.year from the animation; static plots are
unaffected.writeFIA()writeFIA() where 16-digit CNs (e.g.,
CN, PLT_CN, PREV_PLT_CN) were
silently rounded to 15 significant digits on writing (e.g.,
1097572188290487 was written as
1097572188290490), since data.table::fwrite()
writes doubles with at most 15 significant digits. Because recent FIADB
CNs have 16 digits, reading tables with readFIA(),
modifying them, and saving them with writeFIA() broke the
joins between the saved tables and any unmodified ones (e.g., COND rows
no longer matched their PLOT and TREE rows), dropping recent plots from
all subsequent estimates. CN columns are now written as exact
integers.rFIA estimates to EVALIDator and whether they passed, along
with a history of results across test runs..dots argument from all calls to
dplyr::group_by(), which resulted in an error with the
latest version of dplyr (see #54).bit64 package.N from the return output of all model fitting
functions as this was not always being properly calculated when
different filters were applied. Additionally, we updated our recommended
approach for calculating confidence intervals and so this value is no
longer part of that recommended calculation.variance from all estimation
functions, with the exception of fsi(). Previous
documentation was misleading in that it said valid confidence intervals
cannot be constructed from the sampling errors. This is not strictly
true. The sampling error is a function of the variance/standard error,
and so the sampling error can be used to calcualte confidence
intervals when manipulated appropriately. See the note in all model
fitting functions documentation for further details on how to do
this.fsi() were
too precise. This has been updated to better reflect the amount of
uncertainty in the associated estimates. Confidence intervals are now
calculated with the number of plots used to inform the given FSI
estimate, not the number of plots within all estimation units that
encompass the population of interest. Consider the case where we
calculate FSI for an individual species. Because FSI is a measure of
change over time, only plots where the species was present at for at
least one time point go into informing the FSI estimate. The previous
use of all plots, even those without the species of interest,
substantially inflated the sample size.plotFIA() that errored when
including error bars. Also fixed code to remove a warning message in
simple time series plots.fsi() function. Some of
these functions fixed some common errors that could be encountered under
specific circumstances where the function broke, which happened as a
result of updates to FIADB since the last time this function underwent a
major update. An additional update fixes an important bug where the
scaleBy function would not always work as was reported. In
particular, under certain situations (namely byPlot = TRUE)
the subsequent calculations of relative density did not use the
level-specific intercepts and slopes that were estimated in the
regression model, and instead the overall mean was used (i.e.,
equivalent to if scaleBy was not specified). This could
lead to sub-optimal accuracy of the relative density calculations, and
in subsequent FSI outputs. Apologies for any problems this may have
caused.biomass() function to fix a bug in reported
estimates when component = 'TOTAL'. There was a mismatch in
what was reported between the documentation and the function output. The
estimate provided simply added up all biomass across the different
components provided by biomass(), which did not make much
sense since the different components are not mutually exclusive. This is
now fixed such that component = 'TOTAL' provides biomass
estimates equal to the sum of ROOT, STEM, STEM_BARK, BRANCH, and FOLIAGE
components. Apologies for the inconvenience this error may have
caused.PLOTGEOM database table within the grpBy
argument. Also changed readFIA() to by default read in
PLOTGEOM as one of the common database tables. Thanks to
Jacob Fraser for the suggestion here.dtplyr 1.3.2 that led to an error in
areaChange()fiaRI object to reflect recent changes in
the FIA Database. These changes resulted in the package functions
successfully working with the previous version of fiaRI but
not working for actual user data when pulling data from recent versions
of the FIA Database.sf) objects with the following functions:
tpa(). Changes in recent versions of the sf
package led to errors when attempting to return a spatial object. This
bug is now fixed.area() and
areaChange() that resulted in incorrect area (or area
change) estimates being reported when specifying treeDomain
and grpBy (when using grouping variables from TREE). In the
previous version, the filters were not properly applied, and so area
estimates did not adequately represent the filtering conditions and
often just provided the same values as if treeDomain was
not specified. Estimates now provide correct results that are more
inline with intuition. For example, if specifying
treeDomain = SPCD == 121 [i.e., longleaf pine], the
previous area() function would essentially ignore this and
return area of all forest plots. Now, area() will return
the estimate of land area where at least one longleaf pine tree occurs.
Further, the estimate of percent area will be the percentage of total
land area (which is determined by landType) that contains
longleaf pine.biomass(). Previous versions
were not compatible with updates in FIADB and the new National Scale
Volume and Biomass (NSVB) estimators. The function is now updated and
returns biomass and carbon estimates using the NSVB procedure.findEVALID() to return the correct evaluation
IDs. Previous versions had an incorrect join that resulted in
additional, incorrect EVALIDs being returned for a given set of
criteria. This function should only be used by users familiar with FIA
and desiring to use FIA data for use outside of rFIA, as
rFIA is built in a way that users do not need to directly
interact with EVALIDs.dwm() when byPlot = TRUE to set
the YEAR column equal to the year each plot was measured
(MEASYEAR), which may differ slightly from its associated
inventory year (INVYR). This is what all other
rFIA functions do and what was reported in the manual, but
the YEAR returned prior to this version was actually the
inventory year.growMort() that resulted in estimates
of mean annual survivor growth and mean annual net change reporting as
0.growMort() calculation of
removals and the description of it in the manual. Removal estimates
provided by growMort() do NOT include stems that grow
beyond the 5-inch diameter threshold and then are subject to harvest or
natural mortality before the remeasurement period. In other words,
rFIA recruitment does not include trees corresponding to
FIA growth components of CUT2 and MORTALITY2.standStruct() documentation that
incorrectly said the lower diameter for Pole class was set at 11cm while
it is in fact set at 12.7cm (5in).plotFIA() regarding the
error bars produced when se = TRUE. These are 95%
confidence intervals, not 68% confidence intervals.vegStruct() on reporting of
estimates by canopy layer and growth habit.REF_SPECIES table from FIADB, which provides access to the
CARBON_RATIO_LIVE attribute for using the NSVB
species-specific carbon fractions.method = 'EMA'.writeFIA() to allow users to write database
tables by state when only a subset of the table is originally read into
R. This currently requires either the PLOT or COND tables to be read
in.plotFIA() that led to an error in
animated plots when gganimate was not loaded (note that
gganimate still needs to be installed).These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.