Facilitating Adaptive Forest Management under Climate Change: A Spatially Specific Synthesis of 125 Species for Habitat Changes and Assisted Migration over the Eastern United States

Abstract

We modeled and combined outputs for 125 tree species for the eastern United States, using habitat suitability and colonization potential models along with an evaluation of adaptation traits. These outputs allowed, for the first time, the compilation of tree species’ current and future potential for each unit of 55 national forests and grasslands and 469 1 × 1 degree grids across the eastern United States. A habitat suitability model, a migration simulation model, and an assessment based on biological and disturbance factors were used with United States Forest Service Forest Inventory and Analysis data to evaluate species potential to migrate or infill naturally into suitable habitats over the next 100 years. We describe a suite of variables, by species, for each unique geographic unit, packaged as summary tables describing current abundance, potential future change in suitable habitat, adaptability, and capability to cope with the changing climate, and colonization likelihood over 100 years. This resulting synthesis and summation effort, culminating over two decades of work, provides a detailed data set that incorporates habitat quality, land cover, and dispersal potential, spatially constrained, for nearly all the tree species of the eastern United States. These tables and maps provide an estimate of potential species trends out 100 years, intended to deliver managers and publics with practical tools to reduce the vast set of decisions before them as they proactively manage tree species in the face of climate change.

Full text

Article
Facilitating Adaptive Forest Management under
Climate Change: A Spatially Specific Synthesis of
125 Species for Habitat Changes and Assisted
Migration over the Eastern United States
Louis R. Iverson 1, *, Anantha M. Prasad 1, Matthew P. Peters 1and Stephen N. Matthews 1,2
1USDA Forest Service, Northern Research Station, Northern Institute of Applied Climate Science,
359 Main Road, Delaware, OH 43015, USA; anantha.prasad@usda.gov (A.M.P.);
matthew.p.peters@usda.gov (M.P.P.); matthews.204@osu.edu (S.N.M.)
2
School of Environment and Natural Resources, Kottman Hall, 2021 Coffey Road, The Ohio State University,
Columbus, OH 43210, USA
*Correspondence: louis.iverson@usda.gov; Tel.: +1-740-368-0097
Received: 6 September 2019; Accepted: 30 October 2019; Published: 6 November 2019


Abstract:
We modeled and combined outputs for 125 tree species for the eastern United States, using
habitat suitability and colonization potential models along with an evaluation of adaptation traits.
These outputs allowed, for the first time, the compilation of tree species’ current and future potential
for each unit of 55 national forests and grasslands and 469 1
×
1 degree grids across the eastern
United States. A habitat suitability model, a migration simulation model, and an assessment based
on biological and disturbance factors were used with United States Forest Service Forest Inventory
and Analysis data to evaluate species potential to migrate or infill naturally into suitable habitats
over the next 100 years. We describe a suite of variables, by species, for each unique geographic
unit, packaged as summary tables describing current abundance, potential future change in suitable
habitat, adaptability, and capability to cope with the changing climate, and colonization likelihood
over 100 years. This resulting synthesis and summation effort, culminating over two decades of work,
provides a detailed data set that incorporates habitat quality, land cover, and dispersal potential,
spatially constrained, for nearly all the tree species of the eastern United States. These tables and
maps provide an estimate of potential species trends out 100 years, intended to deliver managers and
publics with practical tools to reduce the vast set of decisions before them as they proactively manage
tree species in the face of climate change.
Keywords:
suitable habitat; migration; dispersal model; range shifts; decision-support tools; adaptive
management; DISTRIB-II; SHIFT
1. Introduction
Human-induced rapid climate change will have profound impacts [
1
,
2
], including large
impacts on the earth’s biodiversity [
3
]. A critical need towards better understanding potential
impacts and adaptation strategies for biodiversity conservation hinges on the development of good
predictive models, yet basic biological information, especially related to species interactions, dispersal,
demography, physiology, and evolution, is sorely lacking for most of the earth’s biodiversity [
4
].
Species distribution models, by themselves, often do not adhere to consistent standards for model
building, biological data incorporation, and model evaluation [
5
], and do not adequately account for
these biological attributes [
6
]. Even for well-studied species, critical information is often lacking for
the development of more realistic models, though such models are beginning to emerge for selected
Forests 2019,10, 989; doi:10.3390/f10110989 www.mdpi.com/journal/forests

Forests 2019,10, 989 2 of 25
species that provide more realism via hybrid models that combine aspects of species distribution
models and process models, and that can assist in forest management [
7
,
8
]. However, tools that help
manage the entire suite of species in the face of a changing climate are vital [9].
Our objective was to move beyond the individual evaluation of tree species or forest types by
synthesizing the disparate species-based results and tying them to unique geographic units, then
providing a comprehensive report of current and potential future configurations of forest communities
impacted by climate change. In doing so, we are likely to gain new understanding of both the
current configuration of forest communities but also be in a more robust position to evaluate how
climate change will continue to exert macroscale pressures across the continent. Our approach has
been to combine the results of a habitat suitability model (DISTRIB-II, for projecting potential future
suitable habitats), a probabilistic model (SHIFT, for estimating natural migration into projected suitable
habitat within 100 years), a literature-based set of modification factors (ModFacs, for evaluating
traits associated with adaptation), and a recent assessment of Forest Inventory and Analysis data
(FIA, www.fs.fed.us/fia) [10,11].
We use two spatial subdivisions of the eastern US in this approach to assess the potentials of
the tree species to resist, adapt, or migrate throughout this century—469 units comprising a grid of
1×1
degrees of latitude and longitude, and 55 units comprising the eastern US’ national forests and
grasslands. Within each unit, we evaluate many aspects of tree species’ potential behavior in light of
the changing climate and tabulate them in an information-packed table. Within each table, a user will
be able to assess the dynamics of the forest species in their unit, both currently as well as potentially
out to century’s end. We focus on evaluating species for their capacities to persist or migrate in the
changing climate. By persist, we mean a species’ capability to resist or adapt to the added stresses
associated with the changing climate, based on a species abundance and innate attributes (i.e., traits).
Because our models cannot detect which mechanism (resist or adapt) is in play to allow the species
to persist, we generalize to infer where conditions may allow the species to persist into the future.
With migration, we mean a species’ tendency to migrate (most often northward) to follow suitable
climate conditions into the future under the changed climate, and we do estimate migration with our
models. Though migration can also mean movement of species upslope, we cannot address that in this
study due to the coarse level of analyses.
With this combined species approach, our objective is to focus on providing tables of information
for any location in the eastern US, along with any eastern national forest or grassland. This combination
of the species distribution modeling of DISTRIB-II and the migration simulations of SHIFT allow,
for the first time, a credible picture of what tree species conditions are like now as well as potential
species changes into the future. The intention is to provide guidelines and tools enabling managers
and publics alike to engage proactively in adaptation efforts in the face of the changing climate. With
this information aggregated spatially, we are also able to map spatial trends of any of the tabulated
data from the 1
×
1 degree units or national forests and grasslands within the eastern US. This mapping
approach allows a comprehensive evaluation of a full suite of tree species and their spatial trends, both
currently and potentially into the future.
The intention is to create a set of outputs geared to assist in decision support for local and regional
forest management in the face of a changing climate. In keeping with our earlier products associated
with the Northern Institute of Applied Climate Science [
12
–
18
], this undertaking is an ambitious step
forward: to provide decision tools to managers by comprehensively synthesizing the habitat and
colonization potential of 125 tree species in the eastern United States.
2. Material and Methods
2.1. Study Area
This study encompasses the United States east of the 100th meridian. To summarize model
outputs, the study region was sliced into 469 1
×
1 degree grids (hereafter 1
×
1), which provides a

Forests 2019,10, 989 3 of 25
wall-to-wall coverage for any location in the region to assess general trends at a coarsely summarized
scale (Figure 1). In addition, we subset only the 55 national forests and grasslands (hereafter NF) to
exemplify the capability to assess any set of geographic entities within the region (Figure 1). These two
spatial units provide unique geographic footprints to (1) provide a continuous grid where transitions
and patterns in species can be quantified; and (2) the federal lands provide a spatially distributed set of
focal sites where differences and similarities can be evaluated from a management perspective.
Forests 2019, 9, x FOR PEER REVIEW 3 of 25
the capability to assess any set of geographic entities within the region (Figure 1). These two spatial
units provide unique geographic footprints to (1) provide a continuous grid where transitions and
patterns in species can be quantified; and (2) the federal lands provide a spatially distributed set of focal
sites where differences and similarities can be evaluated from a management perspective.
Figure 1. The National Forests and Grasslands (NF), superimposed on the 1 × 1 degree grid of the
eastern US. Those NF locations occupying <8000 km2 were buffered to attain at least that size, in order
to access sufficient FIA plots for reasonable model outputs. The names of the forests or grasslands to
which the numbers correspond are presented in Table 3. The 469 1 × 1 degree units are also depicted
and provide a wall-to-wall coverage. The individual files are named by their southeast boundary,
such that, for example, a GPS coordinate of 42.44°N latitude, −82.55°W longitude will be named
S42_E82.
2.2. The Synthetic Approach
The overall synthesizing approach uses the results of the previously mentioned DISTRIB-II,
SHIFT, and ModFacs model components; details on these components are provided in an online
supplement as well as previous papers (Figure 2, Supplementary File 1—Climate Change Atlas
description) [11,19–21]. DISTRIB-II provides estimates of habitat suitability (or habitat quality, HQ),
by species, to century’s end according to various scenarios of climate change. A hybrid lattice of 20 ×
20 or 10 × 10 km cells was used for DISTRIB-II modeling with the cell size determined according to
the number of forested FIA plots in each cell. SHIFT estimates colonization likelihood, CL into new
habitats, for each 1 × 1 km cell over approximately 100 years, by running the algorithm 100 times,
with each time a cell gets colonized counting as a 1% probability of colonization. ModFacs uses a
literature approach to assess 12 disturbance and 9 biological attributes of species towards their
adaptability (Adap) to deal with additional stresses likely under climate change.
Figure 1.
The National Forests and Grasslands (NF), superimposed on the 1
×
1 degree grid of the
eastern US. Those NF locations occupying <8000 km
2
were buffered to attain at least that size, in order
to access sufficient FIA plots for reasonable model outputs. The names of the forests or grasslands to
which the numbers correspond are presented in Table 3. The 469 1
×
1 degree units are also depicted
and provide a wall-to-wall coverage. The individual files are named by their southeast boundary, such
that, for example, a GPS coordinate of 42.44
◦
N latitude,
−
82.55
◦
W longitude will be named S42_E82.
2.2. The Synthetic Approach
The overall synthesizing approach uses the results of the previously mentioned DISTRIB-II, SHIFT,
and ModFacs model components; details on these components are provided in an online supplement as
well as previous papers (Figure 2, Supplementary File 1—Climate Change Atlas description) [
11
,
19
–
21
].
DISTRIB-II provides estimates of habitat suitability (or habitat quality, HQ), by species, to century’s
end according to various scenarios of climate change. A hybrid lattice of 20
×
20 or 10
×
10 km cells
was used for DISTRIB-II modeling with the cell size determined according to the number of forested
FIA plots in each cell. SHIFT estimates colonization likelihood, CL into new habitats, for each
1×1 km
cell over approximately 100 years, by running the algorithm 100 times, with each time a cell gets
colonized counting as a 1% probability of colonization. ModFacs uses a literature approach to assess
12 disturbance and 9 biological attributes of species towards their adaptability (Adap) to deal with
additional stresses likely under climate change.

Forests 2019,10, 989 4 of 25
Forests 2019, 9, x FOR PEER REVIEW 4 of 25
Figure 2. Flowchart depicting workflow to produce the DISTRIB-II and SHIFT outputs. DISTRIB-II
predicts current and future habitat suitability/quality (HQ) and SHIFT calculates colonization
likelihoods (CL) and ModFacs estimates adaptability (Adap).
A focus of this paper is a tabular output for a specific 1 × 1 or NF unit that provides species-level
details on current status, potential future status, and features related to possible management such
as assisted migration; the outputs are derived from combinations of the three model components
(Figure 3). Tables, such as that produced in Microsoft Excel for the Allegheny NF (Figure 4,
Supplementary File 2—Allegheny table) and identical to those produced for the 1 × 1 degree units,
use the headers described below (in bold print) but also include, in seven accompanying excel sheets,
a much more inclusive set of data along with suggestions on how to interpret the tables, variable
descriptions, and a set of questions that can be asked of the data. A full explanation of the table
components is also provided in Supplementary File 3—Explanation of species tables. These tables
were generated for each of the 55 NF units and the 469 1 × 1 units. These tables are available for
download at https://doi.org/10.2737/Climate-Change-Atlas-Combined-v4.
Figure 3. Flowchart depicting workflow for outputs of DISTRIB-II (habitat quality, HQ) and SHIFT
(colonization likelihood, CL) to produce capability and candidate species for Infill (the number of
species that can be considered likely to expand under a RCP, and thus higher candidates for infill
planting), Likely (the species was not found in FIA plots, but there is a relatively high probability that
the species exists in the unit), or Migrate (the number of species with at least some chance of migrating
naturally into each unit, and thus higher candidates for artificial migration). HQCL is the combination
of habitat quality (HQ) and colonization likelihood (CL). %OccCol pertains to the percentage of the
unit that is either already occupied or with at least a 50% probability of becoming potentially occupied
(according to SHIFT) within 100 years. %50Col (or %2Col) pertains to the percentage of the unit not
already occupied that has at least a 50% (or 2%) probability of getting colonized within 100 years.
Figure 2.
Flowchart depicting workflow to produce the DISTRIB-II and SHIFT outputs. DISTRIB-II
predicts current and future habitat suitability/quality (HQ) and SHIFT calculates colonization likelihoods
(CL) and ModFacs estimates adaptability (Adap).
A focus of this paper is a tabular output for a specific 1
×
1 or NF unit that provides species-level
details on current status, potential future status, and features related to possible management such as
assisted migration; the outputs are derived from combinations of the three model components (Figure 3).
Tables, such as that produced in Microsoft Excel for the Allegheny NF (Figure 4, Supplementary
File 2—Allegheny table) and identical to those produced for the 1
×
1 degree units, use the headers
described below (in bold print) but also include, in seven accompanying excel sheets, a much more
inclusive set of data along with suggestions on how to interpret the tables, variable descriptions,
and a set of questions that can be asked of the data. A full explanation of the table components is
also provided in Supplementary File 3—Explanation of species tables. These tables were generated
for each of the 55 NF units and the 469 1
×
1 units. These tables are available for download at
https://doi.org/10.2737/Climate-Change-Atlas-Combined-v4.
Forests 2019, 9, x FOR PEER REVIEW 4 of 25
Figure 2. Flowchart depicting workflow to produce the DISTRIB-II and SHIFT outputs. DISTRIB-II
predicts current and future habitat suitability/quality (HQ) and SHIFT calculates colonization
likelihoods (CL) and ModFacs estimates adaptability (Adap).
A focus of this paper is a tabular output for a specific 1 × 1 or NF unit that provides species-level
details on current status, potential future status, and features related to possible management such
as assisted migration; the outputs are derived from combinations of the three model components
(Figure 3). Tables, such as that produced in Microsoft Excel for the Allegheny NF (Figure 4,
Supplementary File 2—Allegheny table) and identical to those produced for the 1 × 1 degree units,
use the headers described below (in bold print) but also include, in seven accompanying excel sheets,
a much more inclusive set of data along with suggestions on how to interpret the tables, variable
descriptions, and a set of questions that can be asked of the data. A full explanation of the table
components is also provided in Supplementary File 3—Explanation of species tables. These tables
were generated for each of the 55 NF units and the 469 1 × 1 units. These tables are available for
download at https://doi.org/10.2737/Climate-Change-Atlas-Combined-v4.
Figure 3. Flowchart depicting workflow for outputs of DISTRIB-II (habitat quality, HQ) and SHIFT
(colonization likelihood, CL) to produce capability and candidate species for Infill (the number of
species that can be considered likely to expand under a RCP, and thus higher candidates for infill
planting), Likely (the species was not found in FIA plots, but there is a relatively high probability that
the species exists in the unit), or Migrate (the number of species with at least some chance of migrating
naturally into each unit, and thus higher candidates for artificial migration). HQCL is the combination
of habitat quality (HQ) and colonization likelihood (CL). %OccCol pertains to the percentage of the
unit that is either already occupied or with at least a 50% probability of becoming potentially occupied
(according to SHIFT) within 100 years. %50Col (or %2Col) pertains to the percentage of the unit not
already occupied that has at least a 50% (or 2%) probability of getting colonized within 100 years.
Figure 3.
Flowchart depicting workflow for outputs of DISTRIB-II (habitat quality, HQ) and SHIFT
(colonization likelihood, CL) to produce capability and candidate species for Infill (the number of
species that can be considered likely to expand under a RCP, and thus higher candidates for infill
planting), Likely (the species was not found in FIA plots, but there is a relatively high probability that
the species exists in the unit), or Migrate (the number of species with at least some chance of migrating
naturally into each unit, and thus higher candidates for artificial migration). HQCL is the combination
of habitat quality (HQ) and colonization likelihood (CL). %OccCol pertains to the percentage of the
unit that is either already occupied or with at least a 50% probability of becoming potentially occupied
(according to SHIFT) within 100 years. %50Col (or %2Col) pertains to the percentage of the unit not
already occupied that has at least a 50% (or 2%) probability of getting colonized within 100 years.

Forests 2019,10, 989 5 of 25
Forests 2019, 9, x FOR PEER REVIEW 5 of 25
Figure 4. HQ and CL output table for the Allegheny National Forest, Pennsylvania, sorted in
decreasing order of current species abundance (FIAsum). Range indicates whether species is wide or
narrow (W/N) in distribution, dense or sparse (D/S) in frequency of presence within FIA plots, and
high or low (H/L) in importance value when it is found. MR refers to model reliability (see [21] for
explanation). %Cell refers to the percentage of DISTRIB-II cells with the species present. FIAiv is the
average importance value for the species when present on FIA plots. ChngCl45 or 85 presents the
change classes (increase, decrease, or no change) of habitat suitability by 2100, according to RCP 4.5
(low emissions) or 8.5 (high emissions). Adapt is a class of adaptability of the species according to the
ModFacs. Abund is an abundance class based on FIAsum. Capabil45 or 85 is the capability of the
species to cope with the climates of RCP 4.5 or 8.5 at 2100, based on abundance, change classes, and
adaptability. SHIFT45 and 85 show two classes of Infill (+ or ++) to indicate these species are currently
found rarely in the NF and likely to expand in the next 100 years, while Migrate (+ or ++) indicate that
the species did not occur on FIA plots, but SHIFT (RCP 4.5 or 8.5) did indicate potential for
colonization in the NF within 100 years. We also show two classes of Likely (+ or ++) to show, because
of signals within SHIFT outputs, that the species is likely present in the unit even though it was not
found on the FIA plots within the unit. SSO is species selection option to assist in decisions regarding
promoting the species, where 1 indicates the species is currently present and has at least a fair
capability to cope, 2 indicates the species is rare or close to the NF boundary and has a good chance
of spreading into the NF, 3 indicates the species is not recorded in FIA plots but does have some
chance of getting colonized within 100 years, and 0 indicates further evaluation may be required.
Finally, the N column simply is a counter. The table is explained fully in Supplementary File 3—
Explanation of species tables.
Figure 4.
HQ and CL output table for the Allegheny National Forest, Pennsylvania, sorted in decreasing
order of current species abundance (FIAsum). Range indicates whether species is wide or narrow (W/N)
in distribution, dense or sparse (D/S) in frequency of presence within FIA plots, and high or low (H/L)
in importance value when it is found. MR refers to model reliability (see [
21
] for explanation). %Cell
refers to the percentage of DISTRIB-II cells with the species present. FIAiv is the average importance
value for the species when present on FIA plots. ChngCl45 or 85 presents the change classes (increase,
decrease, or no change) of habitat suitability by 2100, according to RCP 4.5 (low emissions) or 8.5 (high
emissions). Adapt is a class of adaptability of the species according to the ModFacs. Abund is an
abundance class based on FIAsum. Capabil45 or 85 is the capability of the species to cope with the
climates of RCP 4.5 or 8.5 at 2100, based on abundance, change classes, and adaptability. SHIFT45 and
85 show two classes of Infill (+or ++) to indicate these species are currently found rarely in the NF and
likely to expand in the next 100 years, while Migrate (+or ++) indicate that the species did not occur on
FIA plots, but SHIFT (RCP 4.5 or 8.5) did indicate potential for colonization in the NF within 100 years.
We also show two classes of Likely (+or ++) to show, because of signals within SHIFT outputs, that the
species is likely present in the unit even though it was not found on the FIA plots within the unit. SSO
is species selection option to assist in decisions regarding promoting the species, where 1 indicates the
species is currently present and has at least a fair capability to cope, 2 indicates the species is rare or
close to the NF boundary and has a good chance of spreading into the NF, 3 indicates the species is not
recorded in FIA plots but does have some chance of getting colonized within 100 years, and 0 indicates
further evaluation may be required. Finally, the N column simply is a counter. The table is explained
fully in Supplementary File 3—Explanation of species tables.

Forests 2019,10, 989 6 of 25
The FIA plot data were tabulated within each unit (either 1
×
1 or NF) to yield a ranked list of
tree species, by importance value (IV) quantified equally between total basal area and number of
stems. The actual IVs, under
FIAsum
(bolded elements are headers for the output tables for each
unit, example shown in Figure 4; see also Supplementary File 3—Explanation of species tables), were
summed for each 10
×
10 or 20
×
20 km cell within the unit and recalibrated to the standardized area of
10,000 km
2
(the approximate area of a 1
×
1 degree grid at 36
◦
N latitude), so that values are normalized
and comparisons among units can be made on species importance; they then were assigned to one of
four abundance classes (noted as
Abund
) according to these breakpoints: abundant (FIAsum >75),
common (FIAsum 5–75), rare (FIAsum >0–5), and absent (FIAsum =0). The IVs were also averaged for
only those cells (noted as
%Cell
) that contained the species to yield the importance of the species only
where it is present, not summed over the entire unit (noted as
FIAiv
). An examination of the IV for each
species across its range allowed us to assign a
Range
classification. The Range field provides a quick
indication if the species of interest is narrow or widely dispersed across North America (
N vs. W
), with
Narrow meaning the species is found across <10% of DISTRIB-II grid cells in the eastern US and Wide
>10%. The species is also categorized as found commonly (Dense if
≥
40% of FIA plots among grid
cells with IV >0) or rarely (Sparse if <40% of FIA plots have the species) within its overall distribution
among eastern US FIA records. Finally, the ecological importance or abundance for each species can be
indicated by the average importance value among the plots where the species is present, with High
if average IV
≥
6.0 (the median of mean IV across all species) and Low for values <6.0 [
21
]. We also
assigned three levels of model reliability (
MR
) [
21
]. To do so, we evaluated and combined five model
performance variables into a single rating: (1) a pseudo-R
2
obtained from the RandomForest (RF)
model; (2) a Fuzzy Kappa comparing the imputed RF map to the FIA-derived map [
22
]; (3) a tree
skill statistic of the imputed RF, after removing records with very high coefficient of variables (CV);
(4) the deviance of the CV among 30 regression trees via bagging [
23
,
24
]; and (5) the stability of the top
five variables from 30 regression trees [25].
By ratioing suitable habitat (IV) in future to suitable habitat at present, we can generate five classes
of change (noted as
ChngCl45
or
ChngCl85
, depending on representative concentration pathways
(RCP) 4.5 and RCP 8.5; Figure 3, [
21
]) according to the following break points in future:actual IV
ratio: large decrease (<0.5), small decrease (0.5–0.8), no change (0.8–1.2), small increase (1.2–2.0),
large increase (>2.0). For rare species, those occupying <10% of the region, the following break
points were used: large decrease (<0.2), small decrease (0.2–0.5), no change (0.5–4.0), small increase
(4.0–8.0), and large increase (>8.0). Ecologically, the assumption is that if the species is projected have
increased (or decreased) summed IVs in the future, conditions will be more (or less) favorable for the
species and is categorically represented by the change classes. The ModFacs assessment, based on
12 disturbance and 9 biological traits, provided a baseline rating as to the species’ adaptability (
Adap
)
to the changing climate (Figure 3, [
19
]). The disturbance factors address, based on literature surveys,
how well the species is expected to cope with additional stresses from drought, flood, invasives, wind,
fire, and the like into the future, while the biological factors address the capability of the species to
regenerate vegetatively or via seed, its shade tolerance, its edaphic and habitat specificity, and the
like. Adaptability scores were generated via weighted summations of climate-related biological and
disturbance scores and classified into low, medium, and high adaptability [
19
] (see also Supplementary
File 1—Climate Change Atlas). Notably, adaptability scores are based on the species’ attributes
across their entire range, but they may vary in their response to disturbances in certain locations;
managers may adjust individual species scores according to local knowledge. The SHIFT model, which
calculates colonization likelihood (CL) [
11
,
26
], was calibrated to approximate 50 km migration per
century, an optimistic assumption across species [
27
]. Though migration is dependent on the dispersal
characteristics of the species, and a single migration rate of 50 km/100 years is a broad assumption,
insufficient information exists to defensively assign rates to individual species. We therefore use a
historically defensible rate based on Holocene estimates [27,28].

Forests 2019,10, 989 7 of 25
2.3. Rules for Capability to Cope with a Changing Climate
Besides habitat suitability (ChngCl45 or ChgnCL85), additional information is desired to assess
the species at a more specific spatial unit—the 1
×
1 or NF. To do so, we developed a rating scheme to
include suitability, adaptability, and abundance to derive a capability for each species to withstand the
challenges (and expand with opportunities) posed by the changing climate. We calculate a five-class
capability rating (
Capabil45
or
Capabil85
) based on Abund, Adap, and ChngCl45 or ChngCl85
(Table 1). Following the capability scoring presented in Table 1, if Abund was ‘Abundant’, we enhanced
the capability by one class (e.g., a rating of ‘Good’, became ‘Very Good’ in the final class). Similarly,
if Abund was ‘Rare’, we decreased the rating by one class, and if Abund was ‘Common’, no alteration
occurred. We somewhat arbitrarily promote or demote the capability one class based on abundance.
We assume that within the large area (roughly 8000–10,000 km
2
) of landscape within each area of
interest, there will be spatial variability of climate driven by topographic or geomorphic diversity [
29
]
which will increase the probability of persistence under a changed climate. If the species is abundant
now, we assume there is a greater potential for specific habitats to be suitable and/or genetic variants
to be more resistant. We realize we do not have direct data to support these assumptions although
landscape diversity has been linked to biodiversity and resilience [
30
]; we can only logically infer the
assumption will be true for at least a portion of the species we model.
Table 1.
Initial capability class ratings defined as colored classes, depending on the change class and
adaptability of the species, and other classes for selected species. For final capability rating (Very
Good to Very Poor): if abundance was ‘Abundant’, move up one class; if ‘Rare’ move down one class;
if ‘Common’ stay in class.
Adaptability
Change Class High Medium Low
Large increase Very Good Very Good Good
Small increase Very Good Good Fair
No change Good Fair Poor
Small decrease Fair Poor Poor
Large decrease Fair Poor Very Poor
Other capability classes:
Lost All suitable habitat lost
New Habitat New habitat appearing
FIA Only
Unacceptable model for future, only FIA reported
NNIS Non-native invasive species, only FIA reported
Unknown Modeled as present, unknown
2.4. Rules for Expand, Migrate, and Likely, and Species Selection Options
We used CL in conjunction with HQ to generate HQ-CL classes of species for consideration for
planting or other forms of promotion within the areal units of analyses (Figure 3). These are included in
the fields
SHIFT45
(RCP 4.5) or
SHIFT85
(RCP 8.5) in the table outputs. ‘Infill’ represents those species
that are currently rare in the unit but have high potential to expand within its boundaries. Two classes
(Infill+and Infill++) were generated based on the capability and the percent occupancy currently, plus
those areas with at least a 50% CL within 100 years (Table 2). The ‘Likely’ classes indicate that the
species was not found in FIA plots, but that there is a relatively high probability that the species exists
in the unit, because either the species has some area with at least 50% CL (Likely+) or that it has both
HQ-CL and at least 2% of the area has at least 50% CL (Likely++). The ‘Migrate’ classes indicate that
the species was not found by FIA, but that the DISTRIB-II model indicates new habitat appears in
the unit, with either low HQ and CL (HQ-CL =1, Migrate+) or higher levels of these two components
(HQ-CL >1, Migrate++). Admittedly, this is a very liberal assessment for ‘Migrate’, as the area qualifies if
only a fraction of the area of interest has only a 2% probability of colonization within 100 years.
Species selection options (
SSO
) provides options to assist in decisions regarding promoting the
species, where 1 indicates the species is currently present and has at least a fair capability to cope with

Forests 2019,10, 989 8 of 25
the changing climate—thus suited for planting; 2 indicates the species is rare or in close proximity
(SHIFT shows potential to reach the area within 100 years) to the 1
×
1 grid or NF boundary and has a
good chance of spreading into the area—thus also suited for planting but perhaps not as suited as #1;
3 indicates the species is not recorded in FIA plots but does have some chance of getting colonized
within 100 years—thus could be planted similar to ‘Migrate+or Migrate++’ mentioned above; and 0
indicates none of the above but further evaluation may be required before eliminating consideration of
the species for planting.
Table 2.
Criteria for classes of Infill, Likely, and Migrate, according to the HQ and CL outputs.
Capability and HQ-CL are determined separately for RCP 4.5 and RCP 8.5. %OccCol pertains to the
percentage of the unit that is either already occupied or with at least a 50% probability of becoming
potentially occupied (according to SHIFT) within 100 years. %2Col pertains to the percentage of the
unit not already occupied that has at least a 2% probability of getting colonized within 100 years.
Class Capability HQCL %OccCol %2Col
Infill+Fair, Poor ≥1>0–50 any
Infill++ Good, Very Good ≥1>10–50 any
Likely+New Habitat, Unknown any >0 any
Likely++ New Habitat, Unknown ≥1>2 any
Migrate+New Habitat =1 0 >0
Migrate++ New Habitat >1 0 >0
2.5. Mapping Aggregated 1 ×1 Outputs
The 1
×
1 degree units represent approximately 10,000 km
2
at 36
◦
latitude. Because of the Earth’s
spherical shape, the east–west width (and area) is smaller north of that line and larger south of that line.
For smaller units of analysis (e.g., national forests or grasslands represented here), the unit was buffered
so that a minimum of 8000 km
2
was represented for analysis (Figure 1). Thus, whenever we refer to a
NF, we refer to this buffered (if necessary to reach 8000 km
2
minimum size) area. This minimum size
was required so that an adequate number of FIA plots could be represented for each forest within the
modeling grid [
21
]. The 1
×
1 degree units also represent ~100 10
×
10 km cells or
~25 20 ×20 km cells
,
or a mixture; the buffered NFs represent at least 80 10
×
10 km cells or 20 20
×
20 km cells—these
numbers of cells represent sufficient samples for meaningful summing or averaging across the unit,
and are the reason for choosing such a coarse analysis. As mentioned above, the FIAsum values were
all standardized to equate to the 10,000 km2to enable cross-table comparisons of species importance;
comparisons could therefore be made among the 469 1 ×1 units or the 55 NF units.
By combining information across the 469 1
×
1 tables, mapping of particular characteristics could
be accomplished. To do so, counts were made of the number of species matching the criterion being
queried for each 1
×
1 degree unit and aggregated across units to populate the maps. Though any of
the tabulated variables described above could be mapped, we present below: (1) the number of total
species quantified in each unit; (2) the number of oak (Quercus spp.) species, now and potentially in the
future; (3) the number of species with at least some chance of migrating naturally into each unit under
RCP 8.5, and thus higher candidates for artificial migration (Migrate+plus Migrate++); and (4) the
number of species that can be considered likely to expand under RCP 8.5, and thus higher candidates
for infill planting (Infill+plus Infill++).
3. Results
3.1. Mapping Habitat Quality and Colonization Likelihood Outputs
The combination of HQ and CL is exemplified for eastern hemlock (Tsuga canadensis) in Figure 5.
First, the current condition is established using FIA data within the modeling grid (Figure 5a). Then
potential suitable habitat (habitat quality, or HQ) by 2100 was mapped under RCP 8.5 (Figure 5b).
The colonization likelihood (CL) map was independently created (Figure 5c), then intersected with the

Forests 2019,10, 989 9 of 25
HQ map to yield a map depicting those areas that have at least some chance of colonization within
100 years (Figure 5d). Both the HQ and CL maps were reclassified into three possible classes (low,
medium, high), with IV breakpoints of HQ at 5 (break between low and medium) and 15 (between
medium and high) and CL breakpoints at 10 and 50 percent. Merging the two outputs can produce up
to nine combinations; for eastern hemlock, there was no high HQ so only six HQ-CL combinations
are shown (Figure 5d). Finally, the combined HQ-CL information was used to identify those areas
where the species could ‘infill’, or fill in cells that have no FIA evidence that it exists now, but is closely
surrounded by the species (Figure 5, Infill inset map). Or, if the species could have some potential
to move into new habitat not previously occupied (most often to the north of the current boundary),
those areas can be noted as ‘migrate’ areas (Figure 5, Migrate inset map). We also can identify those
species that are ‘likely’ present but missed by FIA (these locations would appear within the ‘infill’
zones on the map but cannot be precisely located). Thus, the combination of HQ and CL results not
only identifies potential changes in suitable habitat under various scenarios of climate change, but also
provides, for each species present currently or potentially in the future, estimates of CL from natural
migration within 100 years.
Forests 2019, 9, x FOR PEER REVIEW 9 of 25
100 years (Figure 5d). Both the HQ and CL maps were reclassified into three possible classes (low,
medium, high), with IV breakpoints of HQ at 5 (break between low and medium) and 15 (between
medium and high) and CL breakpoints at 10 and 50 percent. Merging the two outputs can produce
up to nine combinations; for eastern hemlock, there was no high HQ so only six HQ-CL combinations
are shown (Figure 5d). Finally, the combined HQ-CL information was used to identify those areas
where the species could ‘infill’, or fill in cells that have no FIA evidence that it exists now, but is
closely surrounded by the species (Figure 5, Infill inset map). Or, if the species could have some
potential to move into new habitat not previously occupied (most often to the north of the current
boundary), those areas can be noted as ‘migrate’ areas (Figure 5, Migrate inset map). We also can
identify those species that are ‘likely’ present but missed by FIA (these locations would appear within
the ‘infill’ zones on the map but cannot be precisely located). Thus, the combination of HQ and CL
results not only identifies potential changes in suitable habitat under various scenarios of climate
change, but also provides, for each species present currently or potentially in the future, estimates of
CL from natural migration within 100 years.
Figure 5. Process flow to intersect HQ and CL for eastern hemlock. (a) Current importance value of
hemlock as determined by FIA data; (b) habitat quality (HQ) at RCP 8.5, year 2100, reclassed into low,
medium, high HQ (there was no high HQ for hemlock), at a resolution of 10 × 10 or 20 × 20 km; (c)
colonization likelihood (CL) into RCP 8.5 habitat, and reclassed into low, medium, high CL, at a
resolution of 1 × 1 km; (d) combination of (a–c), yielding locations with null to high CL on top of low
to medium HQ, as well as currently occupied cells; (infill inset) detail of locations where infilling is
primary; (migrate inset) detail of locations where migrating is primary.
3.2. Species Summaries by National Forest
Figure 5.
Process flow to intersect HQ and CL for eastern hemlock. (
a
) Current importance value of
hemlock as determined by FIA data; (
b
) habitat quality (HQ) at RCP 8.5, year 2100, reclassed into
low, medium, high HQ (there was no high HQ for hemlock), at a resolution of
10 ×10 or 20 ×20 km
;
(
c
) colonization likelihood (CL) into RCP 8.5 habitat, and reclassed into low, medium, high CL,
at a resolution of 1
×
1 km; (
d
) combination of (
a
–
c
), yielding locations with null to high CL on top of
low to medium HQ, as well as currently occupied cells; (infill inset) detail of locations where infilling is
primary; (migrate inset) detail of locations where migrating is primary.
3.2. Species Summaries by National Forest
We present our primary tabular output for one NF, the Allegheny NF (coded ‘1’ in Figure 1and
record 1 in Table 3), but similar tables have been prepared for each 1
×
1 degree area and are available

Forests 2019,10, 989 10 of 25
for download. Other units of analysis will eventually be available including national parks, watersheds,
ecoregions, states, and others. Our combination of HQ, CL, Adap, and current FIA estimates of
importance value allow a detailed presentation of (1) species importance currently; (2) the potential
changes in suitable habitat by 2100; (3) the adaptability of each species to the changing climate;
(4) the capability of each species to cope with the 2100 climate based on adaptability and abundance
currently within the NF; (5) the likelihood of each species to naturally migrate into the NF; and
(6) an assessment of the potential for the species to be used for planting or otherwise promoting within
the NF. These are all presented within an information-packed, but easily unpacked table (Figure 4).
Table 3.
Identity code (noted in Figure 1), coordinates, actual area, and buffered area (km
2
) of each
national forest or grassland. National grasslands are noted as ‘NG’; all others are national forests. Also
shown are results for number of species recorded by FIA within the buffer region, and the number of
species potentially infilling, likely, and migrating according to the RCP 8.5 scenario of climate change
(see text for explanation). For simplicity, only RCP 8.5 data are shown.
Identifier Name Longitude Latitude Area,
km2
Buffer
Area, km2
Number
Species
Number
Infill
Number
Migrate
1 Allegheny −79 41.7 2996.5 8200 43 6 11
2 Angelina −94.4 31.3 1602.8 8800 61 22 1
3 Apalachicola −84.7 30.2 2555.9 8044 64 18 1
4 Bienville −89.5 32.3 1572 8900 67 16 2
5 Black_Kettle NG −99.5 35.7 982 8196 18 2 6
6 Caddo NG −96 33.6 277.3 9100 46 13 0
7 Chattahoochee −84.2 34.7 5950.2 9700 72 24 9
8 Chequamegon −90.8 46 4209.7 11,815 40 5 9
9 Cherokee −83.1 35.8 5950.2 9600 74 35 9
10 Chippewa −94.1 47.4 6465.5 8800 34 6 11
11 Conecuh −86.6 31.1 692.9 8500 60 18 4
12 Croatan −77.1 35 1243.7 8034 62 17 2
13 Daniel_Boone −83.9 37.3 8282.7 14,100 77 10 4
14 Davy_Crockett −95.1 31.3 1602.8 8400 55 15 1
15 Delta −90.8 32.8 490.7 8000 62 30 3
16 DeSoto −89 31.1 3252.4 8700 67 18 0
17 Finger_Lakes −76.8 42.6 58.4 8300 62 11 8
18 Francis_Marion −79.8 33.3 1696.1 8221 70 24 1
19 George_Washington −79.3 38.2 7270.9 14,300 73 16 10
20 Green_Mountain −73 43.4 2543 8800 42 6 14
21 Hiawatha −86 46.2 5197.3 9549 35 7 14
22 Holly_Springs −89.2 34.7 1906 8500 70 13 4
23 Homochitto −90.9 31.5 1540.6 8400 72 17 2
24 Hoosier −86.5 38.5 2612 8100 65 11 4
25 Huron −84 44.6 2787.3 8078 39 6 12
26 Jefferson −81.2 37.1 4926.7 15,100 76 11 8
27 Kisatchie −92.8 31.7 4195.7 9800 74 12 2
28 Lyndon_B_Johnson NG −97.6 33.4 465.5 9200 32 14 1
29 Manistee −85.9 43.9 5377.5 8566 47 7 6
30 Mark_Twain −91.7 37.3 12,237.4 23,900 66 15 5
31 Midewin NG −88.1 41.4 104.3 8350 31 19 12
32 Monongahela −79.9 38.6 7270.9 10,500 61 8 11
33 Nantahala −83.6 35.2 5950.2 9700 65 17 9
34 Nicolet −88.7 45.7 3890.7 8200 39 8 6
35 Ocala −81.8 29.2 1791.6 8177 42 4 5
36 Oconee −83.5 33.4 1049.8 8700 62 10 3
37 Osceola −82.5 30.4 932.4 8400 44 12 6
38 Ottawa −89.2 46.5 3890.7 9559 36 7 11
39 Ouachita −93.9 34.6 9682.3 15,000 62 16 11
40 Ozark −93.5 35.7 9682.3 11,800 70 13 11
41 Pisgah −82.4 35.8 5950.2 10,100 70 12 12
42 Sabine −93.9 31.5 1602.8 8500 63 22 3
43 Saint_Francis −90.7 34.7 120.5 8700 63 45 3
44 Sam_Houston −95.4 30.5 2005.3 8200 63 18 1
45 Shawnee −88.9 37.5 3501 8000 73 13 5
46 Sheyenne NG −97.2 46.4 551.6 8400 9 6 7
47 Sumter −82.1 34.3 5950.2 8200 72 24 1
48 Superior −91.6 47.8 13,204.5 16,415 32 11 14
49 Talladega −86.5 33.2 3042.6 9700 68 15 7
50 Tombigbee −89.2 33.7 713.1 8700 69 20 2
51 Tuskegee −85.6 32.5 63.2 8100 70 21 3
52 Uwharrie −79.9 35.4 889.2 8200 73 18 4
53 Wayne −82 39.3 3451.8 10,800 71 13 5
54 White_Mountain −71.4 44.2 3511.8 8800 37 9 14
55 WB_Bankhead −87.3 34.2 1409.3 8900 71 0 0

Forests 2019,10, 989 11 of 25
3.3. Trends in Area and Species Counts for National Forests and Grasslands
Besides identifying the locations (labels for Figure 1) for the 55 national forest and grasslands,
Table 3presents the geographic coordinates and size information of both the NF itself and the buffer
area used in evaluating the species for this effort. Ranging from 104.3 km
2
for the Midewin NG
to 13,204.5 km
2
for the Superior NF, there is a 126-fold variation in area from smallest to largest.
To dampen this large variation and provide sufficient area for multiple FIA plots, we added area to
accumulate a minimum of 8000 km
2
surrounding each NF, so the range in buffered area was 8000 km
2
for the Delta NF to 23,900 km
2
for the Mark Twain NF (consisting of several units merged together
in the buffering process). Though some species–area curve impacts could be present, a correlation
between the buffered area and species count was only 0.14 (NS).
Tree species counts (according to FIA) for the various NFs ranged from 9 (for the Sheyenne NG
in North Dakota) to 77 (Daniel Boone NF in Kentucky), and were negatively correlated to latitude
(
r=−0.57
,p<0.001); southern NFs generally had higher species richness (Table 3). Also recorded are
the number of ‘infill’, ‘likely’, and ‘migrate’ species (either +or ++) under RCP 8.5. The table indicates
that up to eight species were likely present in the NF but missed by FIA for the Green Mountain NF,
up to 45 species (St. Francis NF) have potential to infill, or expand importance within that forest,
and up to 14 species (Green Mountain NF) have potential to migrate into this buffered NF from points
south (Table 3). The count of potential species to migrate was highly correlated with latitude (r=0.72,
p<0.001),
indicating that there is a greater potential for assisted migration of tree species into the more
northerly NFs, should that be desired.
3.4. Questions and Answers for Example NF: Allegheny NF
To simplify interpretation of the table outputs, we have developed a series of 17 questions that
can be asked of the data table. Users can peruse the questions and explanations to see what might
align with their interests. The methods to (a) extract the answers and (b) provide answers for one unit,
the Allegheny NF, are presented in Appendix A. From this example, many insights on the current and
potential state of tree species on the Allegheny NF can be gleaned. A subset of the questions are as
follows, which can be asked of any 1 ×1 degree or national forest area:
1.
How many species are known (by FIA) to be present currently, what are these species, and how
abundant are they?
2. By 2100, how might suitable habitat change for trees under low and high emission scenarios?
3. How adaptable are the tree species to conditions expected to change under climate change?
4.
How capable might the species be with regards to coping with the changing climate, and what
species may be most (or least) vulnerable?
5. Within the next 100 years, what species may be able to naturally migrate into the area?
6. What species, now rare, are suited to expand or infill in importance over the century?
7. What species are likely present in the area, though missed by FIA plots?
8.
What species might be useful for selecting as candidates for planting in the face of a
changing climate?
For the Allegheny example, we show that 43 species are currently present (6 abundant, 15 common,
and 22 rare species), though one additional species is likely there, and an additional 14 species have
new habitat appearing by end of century. Of these, 13 are likely to lose suitable habitat under RCP 8.5,
while 17 gain habitat. Still, 25 species appear to have good or very good capability to cope with the
changing climate, while the most vulnerable species appear to be red spruce, red pine, black ash, pin
cherry, quaking aspen, and white spruce. Nine species show good potential to migrate into the area
under the high emission scenario, while six additional species are marked as potentials to infill into
surrounding locations from their currently relatively uncommon status. Using the species selection
options, we found 30 species common and well suited for planting, four species uncommon but with

Forests 2019,10, 989 12 of 25
good adaptability, and 12 species potentially suited for planting, though with more risk of planting
failure. Further details are provided in Appendix Aand with the full table available as Supplementary
File 2—Allegheny table.
By providing answers to the 17 questions for any national forest or 1
×
1 degree grid, a user is
able to delve into many aspects of their forest attributes, both now and potentially into the future.
Managers and the casual user alike will have species lists showing which are abundant, common,
and rare within their area of interest. Managers will have data to support (or not) their decisions
related to adaptation to the changing climate. For example, selecting species that may be candidates
for planting may be better justified if the HQ-CL data suggest that the suitable habitats are ripe for the
species in their area of interest. This is especially true for selecting species not now identified from their
area of interest—picking species with ‘Migrate+’ or ‘Migrate++’ would seem more justified in that the
habitat will likely be present in coming decades and the species is close enough now to provide some
chance of natural migration into the area even without assistance.
3.5. Mapped Summaries for 1 ×1 Degree Grid
Tree species relative richness, as recorded on FIA plots and summed for each 1
×
1 degree
grid, shows a remarkable variation in both latitude and longitude (Figure 6A). These patterns show
maximum diversity in the southeastern portion of the U.S., with the maximum count of 82 species
found on the southern Mississippi–Alabama border. Lowest species richness was found, as expected,
in the prairie regions of North and South Dakota, where very few FIA plots were placed in forested
(mostly riparian) zones.
Forests 2019, 9, x FOR PEER REVIEW 13 of 25
Figure 6. (A) The number of species recorded by FIA by 1 × 1 degree cell across the eastern US. (B)
The number of oak (Quercus spp.) species recorded by FIA by 1 × 1 degree cell across the eastern US.
(C) The number of oak (Quercus spp.) species projected to have suitable habitat (HQ) and at least some
chance of colonization (CL) at 2100 according to RCP 4.5. (D) The number of oak (Quercus spp.) species
projected to have suitable habitat (HQ) and at least some chance of colonization (CL) at 2100 according
to RCP 8.5. Figures (C,D) use same figure legend as (B).
The number of species with potential to migrate, by 1 × 1 cell and for RCP 8.5, is depicted in
Figure 7A. There is a large north to south gradient in species numbers; the correlation with latitude
is 0.72 (p < 0.001). The Plains States and Corn Belt states also have fewer species available for
migration. The maximum number of potential species, according to this analysis, that may be
appropriate to plant in an assisted migration mode, is 19, from the Adirondack Region of New York,
with many other cells in the far north also with high numbers of species with potential to migrate in
(Figure 7A).
The number and distribution of species with potential to infill provides a major contrast to the
migration map, in that numbers can range up to 54 species, and with the highest numbers in the
highly agricultural zones (Figure 7B). The maximum 1 × 1 values appear in the southern Mississippi
floodplain, in highly agricultural zones (with few forested FIA plots so only a small total area was
sampled) near locations with relatively high species richness. The correlation between Infill85 and
Migrate85 was −0.34 (p < 0.01), further indicating the inverse relationship between the two, i.e., if the
unit is highly forested, there are less species ‘available’ for infilling as they have already been
captured by the FIA sampling.
Many other maps depicting trends among the 1 × 1 degree grids have been generated, and these
reveal patterns associated with the numbers of abundant or rare species, the numbers of species
within several common genera, the numbers of species most capable to deal with the changing
climate, the top three species within each cell, and information related to the range distribution,
among others. These maps and others will be made available for users to spatially compare among 1
× 1 degree grids relative to their areas of interest.
Figure 6.
(
A
) The number of species recorded by FIA by 1
×
1 degree cell across the eastern US.
(
B
) The number of oak (Quercus spp.) species recorded by FIA by 1
×
1 degree cell across the eastern
US. (
C
) The number of oak (Quercus spp.) species projected to have suitable habitat (HQ) and at least
some chance of colonization (CL) at 2100 according to RCP 4.5. (D) The number of oak (Quercus spp.)
species projected to have suitable habitat (HQ) and at least some chance of colonization (CL) at 2100
according to RCP 8.5. Figures (C,D) use same figure legend as (B).

Forests 2019,10, 989 13 of 25
Diving further into genus-level analyses, the oaks (Quercus spp.) show highest richness in the far
south central portion of the country, maxing out at 21 species; lowest oak richness was in both the
northwestern and the northeastern portions of the study area, as well as southern Florida (Figure 6B).
To demonstrate potential changes under climate change, we also present projected counts of the
number of oaks with suitable habitat and colonization potential under RCP 4.5 (Figure 6C) and RCP 8.5
(Figure 6D). In comparison with the current oak distribution (Figure 6B), the future (~2100) models
show potentials for subtle decreases in oak species counts in the south and slight increases towards the
north. This trend is more prominent with RCP 8.5 (Figure 6D).
The number of species with potential to migrate, by 1
×
1 cell and for RCP 8.5, is depicted in
Figure 7A. There is a large north to south gradient in species numbers; the correlation with latitude is
0.72 (p<0.001). The Plains States and Corn Belt states also have fewer species available for migration.
The maximum number of potential species, according to this analysis, that may be appropriate to plant
in an assisted migration mode, is 19, from the Adirondack Region of New York, with many other cells
in the far north also with high numbers of species with potential to migrate in (Figure 7A).
Forests 2019, 9, x FOR PEER REVIEW 14 of 25
Figure 7. (A) The number of species that may potentially migrate into each 1 × 1 degree cell (from
elsewhere), within 100 years across the eastern US (Migrate+ plus Migrate++), under RCP 8.5. (B) The
number of species with potential to infill (Infill+ plus Infill++) into each 1 × 1 degree cell, within 100
years, across the eastern US.
4. Discussion
This work brings together two lines of effort that our group has pursued for over two decades:
that of the projected changes in habitat suitability under the changing climate [19,20,23,25,31–40] and
that of the potential colonization likelihood into suitable habitat over the course of a century
[11,26,41–43]. Advances in each line were achieved, and often necessitated, by enhancements or
updates in data, climate projections, software, and computing capabilities. By combining these lines
of effort for most of the tree species in the eastern US, we provide a mechanism to evaluate, with
more reality imbedded, the potential outcomes at end of century. The intention of this work is to
assist managers, both at the scale of regions or the entire eastern US as well as individual national
forests or locations within 1 × 1 degree units, in adapting to a changing climate. We hope that the
information outlined here can be used to assist in the silvicultural treatments available to managers,
such as plantation, assisted migration, thinning, or natural regeneration, to help forests mitigate and
adapt to the impacts of climate change [44]. This is the focus of the Northern Institute of Applied
Climate Science, and the Climate Change Response Framework (www.forestadaptation.org) [45]. As
laid out by Millar and Stephenson [46], adaptation pathways lead to resistance, resilience, or response
(or facilitation, or transition). A national experiment is testing these approaches at various sites across
the continent [47], and the data provided here can help in ascertaining species appropriate for each
of these pathways, anywhere in the eastern United States. However, caution is advised when
applying these results because no model can capture the full range of complexity acting on species
through uncertain times, and although species suggested here as potentials for assisted migration
may be adapted climatically to future climate conditions, other factors such as competition or
herbivory may override other traits and restrain regeneration success [48] (see also Supplementary
File 1—Climate Change Atlas). Additional caveats more specific to these results include the
averaging over large land areas (>8000 km2), the assumption of an average 100-year migration rate
for all species of 50 km in uninterrupted forest, and the use of projected future:actual IVs as a proxy
for future importance of the species. Thus the need exists for multiple, adaptive strategies, and
interpretation and species selections by local experts, as we move into this changing, but uncertain
future [49,50].
4.1. Applications for Summary Tables
This indices-based, tabular approach, while quite formidable upon first glance, is a
comprehensive way to combine the outputs from 125 species models plus 23 non-modeled species
(but with FIA information) into succinct, spatially explicit summaries for users of the data as a tabular
Figure 7.
(
A
) The number of species that may potentially migrate into each 1
×
1 degree cell (from
elsewhere), within 100 years across the eastern US (Migrate+plus Migrate++), under RCP 8.5.
(
B
) The number of species with potential to infill (Infill+plus Infill++) into each 1
×
1 degree cell,
within 100 years, across the eastern US.
The number and distribution of species with potential to infill provides a major contrast to the
migration map, in that numbers can range up to 54 species, and with the highest numbers in the
highly agricultural zones (Figure 7B). The maximum 1
×
1 values appear in the southern Mississippi
floodplain, in highly agricultural zones (with few forested FIA plots so only a small total area was
sampled) near locations with relatively high species richness. The correlation between Infill85 and
Migrate85 was
−
0.34 (p<0.01), further indicating the inverse relationship between the two, i.e., if the
unit is highly forested, there are less species ‘available’ for infilling as they have already been captured
by the FIA sampling.
Many other maps depicting trends among the 1
×
1 degree grids have been generated, and these
reveal patterns associated with the numbers of abundant or rare species, the numbers of species within
several common genera, the numbers of species most capable to deal with the changing climate, the top
three species within each cell, and information related to the range distribution, among others. These
maps and others will be made available for users to spatially compare among 1
×
1 degree grids
relative to their areas of interest.

Forests 2019,10, 989 14 of 25
4. Discussion
This work brings together two lines of effort that our group has pursued for over two decades: that
of the projected changes in habitat suitability under the changing climate [
19
,
20
,
23
,
25
,
31
–
40
] and that
of the potential colonization likelihood into suitable habitat over the course of a century [
11
,
26
,
41
–
43
].
Advances in each line were achieved, and often necessitated, by enhancements or updates in data,
climate projections, software, and computing capabilities. By combining these lines of effort for most
of the tree species in the eastern US, we provide a mechanism to evaluate, with more reality imbedded,
the potential outcomes at end of century. The intention of this work is to assist managers, both at
the scale of regions or the entire eastern US as well as individual national forests or locations within
1×1 degree
units, in adapting to a changing climate. We hope that the information outlined here can be
used to assist in the silvicultural treatments available to managers, such as plantation, assisted migration,
thinning, or natural regeneration, to help forests mitigate and adapt to the impacts of climate change [
44
].
This is the focus of the Northern Institute of Applied Climate Science, and the Climate Change Response
Framework (www.forestadaptation.org) [
45
]. As laid out by Millar and Stephenson [
46
], adaptation
pathways lead to resistance, resilience, or response (or facilitation, or transition). A national experiment
is testing these approaches at various sites across the continent [
47
], and the data provided here can help
in ascertaining species appropriate for each of these pathways, anywhere in the eastern United States.
However, caution is advised when applying these results because no model can capture the full range
of complexity acting on species through uncertain times, and although species suggested here as
potentials for assisted migration may be adapted climatically to future climate conditions, other factors
such as competition or herbivory may override other traits and restrain regeneration success [
48
]
(see also Supplementary File 1—Climate Change Atlas). Additional caveats more specific to these
results include the averaging over large land areas (>8000 km
2
), the assumption of an average 100-year
migration rate for all species of 50 km in uninterrupted forest, and the use of projected future:actual IVs
as a proxy for future importance of the species. Thus the need exists for multiple, adaptive strategies,
and interpretation and species selections by local experts, as we move into this changing, but uncertain
future [49,50].
4.1. Applications for Summary Tables
This indices-based, tabular approach, while quite formidable upon first glance, is a comprehensive
way to combine the outputs from 125 species models plus 23 non-modeled species (but with FIA
information) into succinct, spatially explicit summaries for users of the data as a tabular substitute for
the multiple individual maps. In this way, users can glean comprehensive information about the tree
species currently present, or potentially present in the future, in their area of interest. Though our group
and other groups have been modeling potential species changes of tree habitats for quite some time,
this approach gives a condensed summary of species’ current and potential future status for any area of
interest. Beyond estimates of habitat suitability changes or range boundary extensions, this work adds
reality in that natural species migrations will greatly lag the changing climatic conditions [
42
,
51
–
54
].
Estimates for end-of-century habitats and potential shifts provide information that managers can use
now as they ready their forests for end-of-century climates.
The 1
×
1 summary tables are available for download or from the authors for any geographic
location in the eastern US. The 469 1
×
1 degree files provide a wall-to-wall coverage and are named by
their southeast boundary, such that, for example, a GPS coordinate of 42.44
◦
N latitude,
−
82.55
◦
W
longitude will be named S42_E82. Refer also to Figure 1for assistance in locating the appropriate
table(s). Tables for the national forests and grasslands are also available. We intend to make tables
available as well for national parks, watersheds, ecoregions, and other locations; the purpose is to
provide a wealth of sortable information for managers, researchers, landowners, and interested publics.
The set of questions and answers for the Allegheny NF (see Appendix A) can be asked of any of
the tables and represents some of the ways in which users may apply the data. Users interested in the
current biodiversity status in their area of interest may glean the number, abundance, area occupied,

Forests 2019,10, 989 15 of 25
and identity of tree species that have been reported by FIA, or likely to be there even if not reported by
FIA. The Range field provides a quick indication if the species of interest is narrow or widely dispersed,
and how common or ecologically important it is within its range. Users interested in how species
may generally react to the changing climate in their area of interest would focus more on the habitat
suitability (ChgCl45 or ChgCl85) and modification factors (Adap).
The capability analysis, which incorporates habitat suitability, abundance, and adaptability,
provides an assessment of the species’ capability to withstand and cope under the expected conditions
modeled for RCP 4.5 or 8.5. Thus, users can have a better idea of how the species may fare to 2100 in
their particular area of interest.
Users interested in planting in the face of the changing climate also have resources available from
the tables, in fields SHIFT45, SHIFT85, and SSO. Those users wanting to “hedge their bets” on planting
species already reported by FIA in the area but quite uncommon (i.e., rare), can use the Infill markers
to identify candidate species. Because of the hit or miss nature of sampling trees in highly agricultural
regions like the Corn Belt or Mississippi floodplain, many of the Infill species listed for those areas will
be quite common in the fencerows, woodlots, or riparian regions of the area, just missed by the few
FIA plots landing on forests. Thus, an extra level of screening or targeted sampling may be required
for these areas to account for the fewer plots, keeping in mind also that the model reliability varies
among species represented on the tables. Of course, these highly agricultural zones will likely remain
highly agricultural and the potential to Infill naturally is highly improbable into those regions. In these
cases, managers could choose among the Infill species should they wish to afforest some of these
agricultural areas.
The Migrate classification reveals those species that FIA did not report for the area, but the habitat
will likely be suitable by 2100 and the species has some, often very limited, potential to migrate into
the area within 100 years. The assumption is if the species could have any chance of migrating into
the area naturally, it is likely a better candidate for assisted migration than if the species has potential
future suitable habitat but the sources for natural migration are long distances away. Thus, we would
encourage managers to look at these ‘Migrate’ species first if they wish to assist in transitioning
their area for adaptation to climate change. Forestry assisted migration has long been practiced by
silviculturalists [
55
], though it must be practiced with care [
50
]. The Infill and Migrate classes, along
with the SSO classes, provide some guidance for species selection, but again, we emphasize that these
lists are a starting place for decision making, in need of expert interrogation.
Further information regarding species selection options (SSO) is also provided via 0–3 scoring.
Some species will be common already in the area of interest, and have qualities—of fair, good, or very
good capability class—that provide the expectation that the species will also do fine even under the
changed climate of 2100 (SSO =1). An additional set of species may be present or near the area of
interest currently, are usually quite rare, yet are in a position to potentially expand over time (SSO =2).
Because these species are ‘rare’, it is a similar, but a more restrictive set of species as compared to ‘Infill’
mentioned above as they can be ‘common’ species. A third set of species (SSO =3) are those not present
currently according to FIA (‘new habitat’ capability class) but may actually be there (‘likely’ species)
or have a least a minimal potential to colonize over 100 years (‘migrate’ species). Users can tighten
the selection criteria by using another variable available in the long file, %2Col, which only counts
1×1 km cells
as colonized if the SHIFT model outputs at least 2% of the area of interest as colonized
over 100 years (i.e., the cell gets colonized at least 2 times over the course of 100 runs from SHIFT).
For example, one could consider only those species that have at least 5% of the area with at least a
2% probability of colonization (%2Col >5, see long definitions on spreadsheet, e.g., Supplementary
File 2—Allegheny table).
We emphasize that these analyses are only to be used as general guidelines for species selection.
The models are built from FIA data across the eastern US, and for certain species, local influences
(e.g., lake effects) will override the general tendencies across the entire eastern US. The models also
necessarily are built from coarse-level data and are unable to zero in on special or rare habitats, or may

Forests 2019,10, 989 16 of 25
have low model reliability (MR). Therefore, decision makers should use local knowledge to select
species, even if they are not coded 1, 2, or 3 on the Species Selection Option (SSO), as they may still
be suited for particular niches in a project area that helps meet overall objectives. Conversely, each
species coded 1, 2 or 3 for SSO should be evaluated in the local context as they may not be suitable for
particular sites.
Important also is consideration of the context of migration, and the role of migration, assisted or
not, in overall biodiversity management under climate change. Species will be moving but not all will
be perceived as valuable to the communities into which they establish. Though not so problematic
for the 125 tree species presented here, an evaluation of each species for persecuting, protecting,
or ignoring under the changing climate is warranted [56].
The spreadsheets available for the NFs and 1
×
1 degree grids have a total of eight worksheets
for each unit which enable the user to better interpret the dense amount of information contained
therein. These include the short-table and definitions (discussed above), questions to ask of the data,
interpretation help, and some references. Also included is a long table with definitions, which includes
all the short table fields but also a large number of fields that are available for users to delve into the
actual numbers used to derive many of the categorical fields in the short table (note: this information
is also presented in Supplementary File 3—Explanation of tables).
4.2. Mapped Summaries for 1 ×1 Degree Grids
The mapped summaries shown here, Figures 6and 7, provide examples of the kind of outputs
made possible by this wall-to-wall analysis. Any of the fields discussed above can be mapped to derive
spatial patterning across the eastern US. Some patterns are expected, such as the species richness
pattern (Figure 6A), which shows diminishing richness moving westward and northward. This pattern
has been explored previously (e.g., [
57
,
58
]), with richness in concert with energy and moisture patterns.
Concomitant with overall richness is the pattern of oak species richness, a very diverse genus of
up to 21 species per 1
×
1 degree grid in the south central states of Louisiana, Mississippi, and Alabama.
Oaks are a foundational species in much of the eastern forests [
59
], and an extremely important genus
for wildlife and botanical diversity [
60
,
61
] as well as a valuable timber resource [
62
]. For example,
Tallamy and Shropshire [
63
] reported that more than 500 Lepidopteran species in the mid-Atlantic
region use oaks. It is unfortunate that oaks, almost throughout their eastern North American range,
are experiencing a regeneration problem whereby they are being replaced by more mesophytic species
like maples (Acer spp.) that are more tolerant to shade but less tolerant to fire (that now rarely
occurs) [
64
–
66
]. Though silvicultural treatments of repeated fire and partial harvest are tools that have
been shown to promote oaks [
67
,
68
], these tools are difficult (i.e., expensive) to use across large swaths
of land, especially when the majority of eastern forestlands are in private ownership. In contrast,
models of climate change, such as those reported by our group since 1998 [
20
,
25
,
36
] and others [
69
–
71
]
have consistently reported that oaks as a group should do well in a warmer and more drought-prone
future. Nonetheless, it is imperative to conduct the silviculture to maintain a thriving oak component
now so that propagules are available into the future [
72
] and enable the perpetuation of the rich oak
diversity revealed in this 1 ×1 degree examination.
The number of species with potential to migrate reveals the dramatic but expected trend of more
potential species for migrations north vs. south (Figure 7A). Obviously, there are fewer species to draw
from locations further south in the southern latitudes; the Gulf of Mexico marks the end of terrestrial
habitat. In contrast, the northern locations are somewhat cumulative with regards to possible sources
of species to the south, in that the species-rich sections of the south also have less fragmented corridors
for northward movement [
73
] and some species have potentials for long-distance dispersal (up to
500 km within the SHIFT algorithm). The highly fragmented forests within the Plains and Corn Belt
of the Midwest also reduce the potential for species to migrate, i.e., the SHIFT model does not have
many propagule sources when most of the land is under cultivation. Also noticeable when viewing
the juxtaposition of the national forests (Figure 1) with the potential ‘migrate’ species of Figure 7A,

Forests 2019,10, 989 17 of 25
is the probable large influence of the NFs in providing possible sources of species for migration.
Higher number of species with a potential to migrate are found in proximity to NFs in especially the
southern Appalachians, the southern tier of NFs across Missouri, Indiana, Illinois, and Ohio, and the
Northwoods of Minnesota, Wisconsin, and Michigan.
Species may also expand, or ‘infill’ in suitable locations within their current distribution. With up
to 54 species depicted in 1
×
1 degree grids in some southern locations, and with very high numbers
also in the highly agricultural zones (Figure 7B), there is a major contrast with the migration map. These
values are high because forests are sparse and forested FIA plots are infrequent so that either (1) FIA
missed the disparate locations that have the species; or (2) land-use conversion and fragmentation
have eliminated the species from that unit. Managers can select from these lists for species potentially
capable of dealing with the changing climate and are already in the vicinity albeit in low numbers.
Of course, any species identified via these tables must be vetted by local experts as to their
suitability according to local ecological conditions. Because of the scale of analysis (8000 km
2
for
national forests or 10,000 km
2
for 1
×
1 degree grids), there will always be large variations in soils,
topography, land use, and hydrology within the unit that precludes or includes species as candidates.
The online Climate Change Atlas website (www.fs.fed.us/atlas), as well as many other documents and
web sites, also include ecological characteristics of species to help sort out the applicability of species
for particular planting sites.
5. Conclusions
This study is a synthetic effort of over two decades of research and focuses on combinations of
tree species occupying, currently or potentially in the future, each national forest and each
1×1 degree
grid throughout the eastern US. We have combined 125 modeled species, both with regard to
potential changes in suitable habitat and capability, but also with regard to the potential to migrate
or infill naturally (according to historic migration rates) over the next 100 years. This resulting
summarization effort provides an enormous data set, here described for 469 1
×
1 degree grids and
55 national forests and grasslands (and we are compiling them for hydrologic units, ecoregions, states,
and national parks as well). These data are available from authors and available for download at
https://doi.org/10.2737/Climate-Change-Atlas-Combined-v4. We emphasize that these tables and maps
are only the first-line estimate of potential species trends, intended to provide the managers with
some tools to reduce the vast set of decisions before them as they proactively manage in the face of
climate change.
Supplementary Materials:
The following are available online at http://www.mdpi.com/1999-4907/10/11/989/
s1, Supplementary File 1—Climate Change Atlas, Supplementary File 2—Allegheny table, Supplementary
File 3—Explanation of tables.
Author Contributions:
All four authors cooperated on conceptualization, methodology, validation, formal
analysis, investigation, data curation, visualization, review and editing, and preparing proposals for funding.
L.R.I. prepared the original draft, and supervised and administered the overall project beginning in 1995.
Funding:
This research was funded by the USDA Forest Service, Northern Research Station, the Northern Institute
of Applied Climate Science, and the USDA Climate Hubs as part of their appropriated funding.
Acknowledgments:
The authors are indebted to the hundreds of FIA staffresponsible for acquiring and processing
data to make it available to researchers like ourselves. Funding for this project was provided by the USDA
Forest Service Northern Research Station; this research did not receive any specific gran from funding agencies
in the public, commercial, or not-for-profit sectors. Thanks to the reviewers of earlier drafts: Andrew Maday,
Bryce Adams, Patricia Leopold, and Chris Swanston. Special thanks are due the reviewers of the original submitted
manuscript; your comments were invaluable in strengthening the paper.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.

Forests 2019,10, 989 18 of 25
Appendix A
Appendix A.1. Questions and Answers for Example NF: Allegheny NF
An assessment of the output data for the Allegheny NF provides several observations both on
current and potential future species. We present them in the form of answers to questions that may be
asked of the data, then the way to extract the answer (a), and the answer for the Allegheny NF (b).
These questions are all presented within the excel tables (on a sheet labeled ‘Questions of tables’)
accessible with the online database. Though an extensive list of questions, it exemplifies the breadth of
information possible to be gleaned for any areal unit via these tables; we have grouped the questions
according to learning about the current status, the potential change in suitable habitat, and the potential
change in migration status over 100 years.
Appendix A.1.1. Current Status of Species
1. Of the species modeled, how many are known (by FIA) to be present in this NF?
a.
The column
N
counts the species in this table. Some are present and some are modeled
to have suitable habitat appear by 2100. Those with
%Cell
>0 are known by FIA to be
present. Some others may be present but were not found on FIA plots.
b. A total of 43 species identified by FIA for this NF.
2. What species are present in this area (according to FIA)?
a.
Look at
Common Name
or
Scientific Name
, those with
%Cell
>0 have FIA indication
of presence.
b. Red maple, black cherry, sugar maple, and American beech top the list of 43 species.
3.
How common is each species, when it is found, i.e., it may be common only in certain areas
within the unit, like bottomlands or plantations (according to FIA)?
a. FIAiv shows the average importance of the species, when it is found.
b.
Eastern hemlock ranks 5th in overall importance across the NF (FIAsum), but 4th in
importance when considering only cells where it is located—it has some particular habitats
that it prefers within the NF.
4. How abundant is each species, taken across the region of interest (according to FIA)?
a. Abund classifies FIAsum into: Abundant, Common, Rare, Absent
b. The Allegheny NF has 6 abundant species, 15 common species, and 22 rare species.
Appendix A.1.2. DISTRIB-II Model Outputs for Year 2100
5. How much confidence do we have in the models?
a. ModRel gives classes of model reliability: Low, Medium, High
b.
The Allegheny NF table shows the following species ratings: 18 high, 24 medium, and 15
low model reliability. Three others are labeled FIA, meaning that we only provide
information from FIA, and no models were developed because too few data were available
for acceptable models.
6.
What do the models suggest may happen to habitat suitability for the species by the year 2100,
according to a high (or low) emissions scenario?

Forests 2019,10, 989 19 of 25
a. ChngCl45
and
ChngCl85
provide, for RCP 4.5 (low emissions) and RCP 8.5 (high emissions)
respectively, the potential change in suitable habitat for the species by year 2100. It is
important to note that a change in suitable habitat does not mean the species importance
will actually change in that area by 2100, only that the habitat is expected to increase,
decrease, or remain unchanged in suitability for that species over time.
b.
The Allegheny NF table shows 15 species with large or very large decrease in suitable
habitat according to RCP 8.5, but also 4 species each for small decrease, small increase,
or no change, 13 species for large increase, and 14 species with new habitat appearing by
century’s end under high emissions.
7.
According to literature, how adaptable are the species to the direct and indirect impacts of a
changing climate?
a. Adapt
shows a color-coded rating (Low—pink, Medium—yellow, High—green) of a
compilation of modification factors that estimate the adaptability of the species across
their range.
b. For Allegheny NF, 15 species are classed as highly adaptable, 35 as medium, and 8 as low
in adaptability.
8.
How capable might the species be for coping with the changing climate, within the region of
interest, under a low (or high) emissions scenario?
a. Capabil45
(or
Capabil85
) gives an estimate of the species capability to cope, based on its
change in habitat suitability (
ChngCl45
or
ChngCl85
), its
Adapt
, and its
Abund
in the
region of interest. If the species is abundant locally, it is assumed to also be in refugia and
niches somewhat buffered from the climate impacts. Ranks are coded Very Good,Good,Fair,
Poor,Very Poor,FIA only (no model, no Capability assigned), NNIS (no model, non-native
invasive species), Unknown (insufficient data to model), and New Habitat (potential to
migrate into the region).
b.
Under a scenario of RCP 8.5, the Allegheny table shows 7 species with very good and 18
with good capability, 6 with fair capability, and 3 with poor and 6 with lost capability, along
with 14 with new habitat, 2 non-native invasive species, and 1 species with only FIA data.
9.
What species might be considered most (or least) vulnerable to the changing climate in this area?
a.
Those species listed under
Capabil45
(or
Capabil85
) as Poor,Very Poor, or Lost will be
vulnerable according to our assessments; those listed as Very Good,Good, or Fair are treated
as less vulnerable.
b.
Most vulnerable (lost) species for the Allegheny NF under RCP 8.5 are red spruce, red
pine, black ash, quaking aspen, pin cherry, and white spruce. Least vulnerable (very good)
are: chestnut oak, northern red oak, white oak, white ash, black oak, eastern white pine,
and American basswood. Of course, white ash is under severe threat of emerald ash borer,
which the models could not adequately consider.
Appendix A.1.3. SHIFT Model Outputs for Year 2100
10.
Natural migration of the species has been modeled with SHIFT—it predicts where the species
may migrate within the next 100 years that is currently unoccupied by the species. Assuming a
generous migration rate of ~50 km/century, where is the species likely to colonize?
a. SHIFT45
or
SHIFT85
highlights those species that have the best chance of migrating into
the region within 100 years. Those species labeled ‘Migrate+’ have both new suitable habitat
and some probability of colonization within 100 years, while those labeled ‘Migrate++’
have a stronger potential for colonization according to the models.

Forests 2019,10, 989 20 of 25
b. Species with potential to migrate into the Allegheny NF within 100 years (under RCP 8.5)
include: post oak, shortleaf pine, sweetgum, sycamore, and eastern redcedar (all Migrate++),
flowering dogwood, sourwood, Virginia pine, and eastern redbud (all Migrate+). Under
RCP 4.5, only sycamore and sourwood appear on the list.
11.
What species are relatively rare in the region, but are poised to expand or infill within the region
under the changing climate?
a. SHIFT45
or
SHIFT85
labels those relatively rare species that could expand within the
region as ‘Infill+’ or ‘Infill++’, depending on the strength of the modeled indicators.
b.
The table for the Allegheny shows mockernut hickory, bitternut hickory, scarlet oak,
and shagbark hickory as the best candidates for infilling (Infill++), followed by green ash
and American elm (Infill+). Of course, these last two species would need to be resistant
varieties to withstand current plagues against them.
12.
What rare species are likely present in the region based on our model outputs, but the forest
inventories missed them?
a. SHIFT45
or
SHIFT85
labels those relatively rare species are likely present within the region
as ‘Likely+’ or ‘Likely++’, depending on the strength of the modeled indicators.
b.
The data predict that black walnut, not found in FIA plots, are now somewhere on the
Allegheny NF or its buffer.
13.
What species might be useful for selecting as candidates for planting in the face of a
changing climate?
a. SSO
is the species selection option to assist in decisions regarding promoting the species,
where 1 indicates the species is currently present and has at least a fair capability to cope,
2 indicates the species is rare or close to the NF boundary and has a good chance of
spreading inside the NF, 3 indicates the species is not recorded in FIA plots but does have
some chance of getting colonized within 100 years into the NF, and 0 indicates further
evaluation may be required. Generally, managers might consider selecting species first
with SSO =1, then SSO =2, then SSO =3, then SSO =0.
b.
For the Allegheny NF, 30 species present now have at least a fair capability to cope with the
changing climate (SSO =1); the models suggest these species are most suited for planting
in the area. Three species are rare inside the NF (bitternut and mockernut hickory, slippery
elm) and have potential to expand and could be suited for planting (SSO =2) (green ash is
also listed but of course would not be recommended due to emerald ash borer). Twelve
additional species are marked as having some chance (>0% chance, SSO =3) of naturally
migrating there within 100 years and could be suited for planting, though with more risk
of failure as compared with SSO 1 or 2. The remaining 14 species have SSO =0, and are
generally not suited for planting, though they still can be considered as candidates for
planting under certain local conditions or manager preference.
14. What portion of the area has at least a 2% chance of getting colonized over 100 years? (note: these
variables are only on the long file available as Supplementary File 2—Allegheny table)
a. %2Col
estimates, for any species, the percentage of the region of interest with at least 2%
chance of colonization, and is not already occupied according to FIA. One cutoffwe use for
potential planting guidelines is that the species requires at least 5% of the region of interest
to have >2% chance of being colonized; often coded “Migrate+” or “Migrate++” under
SHIFT45/SHIFT85, and coded ‘3’ under SSO.

Forests 2019,10, 989 21 of 25
b.
The long form of the Allegheny table lists 49 species with at least 5% of the NF having
at least a 2% chance of getting colonized over 100 years. These include species that are
already present and potentially spreading to unoccupied locations within the NF.
15.
What portion of the area has greater than 0% chance of getting colonized over 100 years? (note:
these variables are only on the long file available as Supplementary File 2—Allegheny table)
a. %0Col
provides the percentage of the area, not already occupied according to FIA, that has
>0% chance of getting colonized.
b.
For the Allegheny NF, 54 species (out of 60) show some area with at least some chance of
getting colonized over 100 years. These also include species that are already present and
potentially spreading to unoccupied locations within the NF.
16.
What portion of the area has at least a 50% chance of getting colonized over 100 years? (note:
these variables are only on the long file available as Supplementary File 2—Allegheny table)
a. %50Col
provides the percentage of the area, not already occupied according to FIA, that
has >50% chance of getting colonized.
b.
Within the Allegheny, 41 species have at least some territory with >50% chance of
colonization. These also include species that are already present and potentially spreading
to unoccupied locations within the NF.
17.
What species may have the highest (or at least some) chance of migrating and finding suitable
habitat within 100 years (under low and high emissions)? (note: these variables are only on the
long file available as Supplementary File 2—Allegheny table)
a. HQCL45
(low emissions) and
HQCL85
(high emissions) are indices that use a weighted
average calculation (based on habitat quality and colonization likelihood), to assign a score
for the potential to migrate into suitable habitat—the higher the value, the greater the chance,
with values 1 or greater indicating the presence of both suitable and colonizable habitats.
b.
Within the Allegheny NF, of the 14 species with new habitat (question #8), 12 species have
HQCL85 greater than or equal to 1.0 (indicating the presence of both suitable habitat and
colonizable habitat), while for HQCL45, the number is only 3 species.
By providing answers to these 17 questions for any national forest or 1
×
1 degree grid, a user
is able to delve into many aspects of their forest attributes, both now and potentially into the future.
Managers and the casual user alike will have species lists showing which are abundant, common,
and rare within their area of interest. Managers will have data to support (or not) their decisions
related to adaptation to the changing climate. For example, selecting species that may be candidates
for planting may be better justified if the HQ-CL data suggest that the suitable habitats are ripe for
the species in their area of interest. This is especially true for selecting species not now identified
from their area of interest–picking species with ‘Migrate+’ or ‘Migrate++’ would seem more justified
in that the habitat will likely be present in coming decades and the species is close enough now to
provide some chance of natural migration into the area even without assistance. Of course, any species
identified via these tables must be vetted by local experts as to their suitability according to local
ecological conditions. Because of the scale of analysis (8000 km
2
for national forests or 10,000 km
2
for
1×1 degree
grids), there will always be large variations in soils, topography, land use, and hydrology
within the unit that precludes or includes species as candidates. The online Climate Change Atlas
website (www.fs.fed.us/atlas), as well as many other documents and web sites, also includes ecological
characteristics of species to help sort out the applicability of species for particular planting sites.

Forests 2019,10, 989 22 of 25
References
1.
IPCC Summary for Policymakers. In Global Warming of 1.5
◦
C. An IPCC Special Report on the Impacts of Global
Warming of 1.5
◦
C Above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the
Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts
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