developing an ecological land classification for the fundy model forest, southeastern new brunswick,...
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DEVELOPING AN E C O L O G I C A L LAND CLASSIFICATION
FOR THE FUNDY MODEL FOREST,
SOUTHEASTERN NEW BRUNSWICK, CANADA
BRUCE E. MATSON and RANDAL G. POWER New Brunswick Department of Natural Resources and Energy, P.O. Box 150,
Hampton, New Brunswick, EOG 1ZO, Canada
Abstract. The methodology for developing and mapping a hierarchical Ecological Land Classifi- cation (ELC) is presented. The classification provided a systematic methodology that explained the distribution and composition of southern New Brunswick's forested landscape. The nested structure of the ELC identified and provided a hierarchical linkage between ecosystems from the size of forest stands to climate regions. This framework made the collection and analysis of data efficient and gave confidence that tree species distributions, which were central to understanding the influence of abiotic factors on the forest systems, were controlled by the factors examined at each level of the hierarchy. This ELC methodology, developed for the Fundy Model Forest, was successful in describing and mapping the Climate, Geomorphologic, and Regolith controlled forest ecosystems. Preliminary classification indicates that spatial referencing of the Site Level is achievable.
I. In troduct ion
The Fundy Model Forest (FMF) is one of Canada s ten working models of "Sus- tainable Forestry". This 419 266 hectare parcel of land in southern New Brunswick has a diverse ownership, and was selected as a Model Forest because it is repre- sentative of the forests of the Acadian Forest Region (Rowe, 1972), and the social and economic climate of the Maritime Provinces. A goal of the FMF is to produce a Sustainable Forest Management Plan by 1997. Quantification of the forest land- scape, as well as forest stand and plant species diversity has been identified as a fundamental information requirement for meeting this goal.
An early quantification of New Brunswick's forests (Loucks, 1962) provided a zonal characterization of the Maritimes based on the distribution of tree species. These Forest Zones maps (1:2 000 000 scale) provide accurate regional charac- terizations, however the application of Geographic Information Systems (GIS) to forest management planning necessitates a finer resolution. Recently, site classifi- cation guides to interpret site productivity from soil and vegetation features have been developed (Zelazny et aL, 1989). Although these classifications have been applied to resource issues in New Brunswick, forest managers and the public are asking for information that is beyond the capabilities of these systems.
In September, 1993, an Ecological Land Classification (ELC) approach was cho- sen with goals of quantifying and mapping the distribution of forest ecosystems in the FME The ELC would subsequently provide a framework for discussions
Environmental Monitoring and Assessment 39" 149-172, 1996. (~) 1996 Kluwer Academic Publishers. Printed in the Netherlands.
150 BRUCE E. MATSON AND RANDAL G. POWER
TABLE 1 Hierarchical levels, unit nomenclature, and definitions for the Ecological Land Classification frame- work for the Fundy Model Forest (adapted from van Groenewoud and Ruitenberg, 1982).
Level Unit name Principal criteria
1 Climate Region
2 Geomorphologic District
3 Regolith Section
4 Site
Regional climate defined by temperature and precipitation rela- tionships; influenced by the interaction of air masses with regional physiography and large bodies of water
Areas of bedrock formation(s) with homogeneous morphology, mineralogy and textural characteristics that provide controls on drainage, topography and nutrient supply
A quaternary deposit with homogenous parent material (i.e., mineralogy), textural characteristics (i.e., mode of deposition), depth and coarse fragment content
A segment of regolith with homogenous drainage, moisture regime, slope, aspect, elevation, and slope position that creates a distinctive vegetational response
conceming biodiversity, forest ecosystems structure, and resource management. A four-level hierarchical ecological land classification system, adapted from van Groenewoud and Ruitenberg (1982), was chosen because of its explicitly defined classification levels that isolate important ecological regulators of energy and nutri- ent flow (Table I). The four levels of the ELC system are: Climate Region, Geomor- phologic District, Regolith Section and Site. As well, the ARC/INFO Geographic Information System (GIS) operated by the New Brunswick Department of Natural Resources and Energy (DNRE) held available data layers consisting of bedrock geology (1:50 000), soils (1:50 000), and a forest inventory based upon 1:12 500 photo-interpreted and ground-verified sources.
2. Physical Description of the Fundy Model Forest
The FMF is centred on Sussex, New Brunswick (45o45 N, 65o30 W) (Figure 1). The ownership of the FMF is 63% private, 17% private- industrial, 15% public, and 5% national park. The area has a three-hundred year history of European settlement, and is characteristic of the Maritime Provinces in terms of land use and its forest-related economy. The forest was logged extensively for large ship timbers during the 1700s and large areas were cleared for agriculture in the 1800s. Fire and spruce budworm (Choristoneura fumiferana Clemens) infestation have been the primary natural disturbances in the FMF. Forests cover 80% of the FMF and are composed of red spruce (Picea rubens Sarg.), balsam fir (Abies batsamea
LAND CLASSIFICATION IN THE FUNDY MODEL FOREST 151
Fig. 1. Map showing the location of the province of New Brunswick in North America and the location of the Fundy Model Forest.
(L.) Mill.), pines (Pinus spp.), aspens (Populus spp.) and northern hardwoods that are characteristic of the Acadian Forest Region (Rowe, 1972).
Southern New Brunswick's climate of warm summers and cold winters is pre- dominantly continental in spite of the province's coastal position (Day et al., 1977). Prevailing winds blow mainly from continental North America; arctic air from the northwest dominates in winter, while tropical winds from the southwest dominate in summer (Bryson, 1966). The highest annual precipitation in New Brunswick occurs on the Fundy Mountains (1500 mm per year) (Day et al., 1977; MacMillan and Hoyt, 1981; Dzikowski et al., 1984) as a result of frequent cyclonic storms carrying moisture picked up from the Atlantic Ocean and Bay of Fundy. The South- ern Uplands on the leeward (north) of the highlands receive 400 mm less annual precipitation as a result of a rainshadow effect.
Southern New Brunswick contains three bedrock-controlled physiographic re- gions formed as part of the Appalachian Mountains: the Fundy Mountains, the Southern Uplands, and the New Brunswick Lowlands. The Fundy Mountains rise steeply from the Bay of Fundy to form a peneplain averaging 350 m above sea level (ASL) that stretches between the St. John and Petitcodiac Rivers. The Southem Uplands, situated to the noah of the Fundy Mountains, are a series of ridges and valleys (average relief 150 m) with a northeasterly orientation (Rampton et al., 1984). The ridges are mostly forested with northern hardwoods while the valleys
152 BRUCE E. MATSON AND RANDAL G. POWER
are cleared for agriculture. The New Brunswick lowlands is a large, flat to gently undulating, low elevational (185 m ASL) plain that is poorly drained and primarily forested.
The regolith in the FMF consists of a thin mantle of glacial moraine/veneer (0.5 to 3 m) deposited in the Wisconsinan and post-Wisconsinan ages (100 000 to 10 000 BP) (Rampton et al., 1984). Glacial transport was often less than 2 km, hence soil fertility, texture and moisture properties are highly coincident with the underlying bedrock. Podzolisation is the most common soil-forming process occurring in New Brunswick.
3. Methodology
Classification and mapping of this ELC is hierarchical because climate (Climate Regions) exerts an influence on the forest composition that cannot be explained by the lower levels. For example, forest communities developing on similar geol- ogy/landforms and soils in two different climate regions will support different compositions. If the influence of climate had not been isolated at an upper level of the classification, using the variables of geology and soils to explain forest compo- sition would be inconclusive. For these reasons, the ability of ELC to explain the forest composition depends on the accuracy achieved at each upper classification level.
Each level of the ELC is nested within the prior level of the hierarchy. Boundaries of each nested polygon are constrained by the level of the ELC immediately above. Hence, all polygons at the same level are defined directly by one common abiotic factor and indirectly by the abiotic factors defining all the higher levels. For example the Geomorphologic Districts (GD) have boundaries that do not extend beyond the Climate Region boundary.
For each level of the ELC, the abiotic variables that are being classified have to be proven to control the distribution and composition of biotic systems, namely the forests. The distribution of tree species was used to represent biotic systems at all four levels because they are readily identified through remote and ground sampling methods, and because knowledge of their distribution and silvics is well documented. For the Climate and GD levels this was accomplished in part with the GIS forest inventory. Both the Regolith and Site levels required forest sampling to acquire the necessary detail on species information.
3.1. CLIMATE REGIONS
Climate Regions are defined as areas in which the interaction of weather systems with regional physiography and large bodies of water create distinct temperature and precipitation relationships. These Regions support forest zonal communities distinct from adjacent regions. Climate Regions are subdivisions of the larger Aca-
LAND CLASSIFICATION IN THE FUNDY MODEL FOREST 153
Fig. 2. Map of southeastern New Brunswick showing the distribution of climate zones (Dzikowski et al., 1984) for agriculture based on annual growing degree days above 5~ and May to September precipitation (mm) (used with permission from W.G. Richards, 1994).
dian Forest Region (Rowe, 1972) and subsequently are the largest forest ecosys- tems within the ELC framework occurring wholly within New Brunswick. Cli- mate Regions are important because each region has unique soil-forming process- es, hydrologic cycles, and species, for example, that subsequently create distinct ecosystems.
Climate Regions were developed from the existing classification of climate (Dzikowski et al., 1984) (Figure 2), and forest zones of the Maritimes (Loucks, 1962) (Figure 3). These sources provided regional characterization of temper- ature/precipitation relationships and zonal forest communities respectively, and were deemed appropriate for the level of climatic delineation sought. Weather sta- tion data and knowledge of seasonal weather patterns and meteorologic processes were applied to support the regions suggested by Loucks (1962) and Dzikowski et al. (1984). These preliminary steps allowed the construction of a provisional map.
Forest landscape patterns from 1986 Landsat imagery and stand typing from 1982 GIS forest inventory maps (Figure 4) were overlaid with National Topograph- ic Series (Dep. of Energy, Mines and Resources, 1972) maps and the provisional map at 1:250 000 scale. A second provisional map (Figure 4) was generated by repositioning the Climate Region boundaries to correspond to the physiography and broad changes in the proportion of coniferous to deciduous forest. Weather
154 BRUCE E. MATSON AND RANDAL G. POWER
Fig. 3. Map of southeastern New Brunswick showing the distribution of four forest zones (Loucks, 1962) in the Fundy Model Forest (used with permission from the Canadian Forest Service, 1994).
station data from Environment Canada (Atmospheric Environment Service, 1993) were combined with knowledge of meteorological processes (e.g., orographic rain- fall) to generate climate profiles for each region in the second provisional map. The Climate Region boundaries were accepted if the climate profiles explained the forest compositional changes between regions. For example, the Fundy Coast which supports a cool, moist maritime climate is dominated by red spruce-balsam fir-yellow birch (Betula alleghaniensis Britton) and lacks species typical of fire disturbed landscapes (e.g., Pinus spp.). Finally, these physiographic - forest com- position relationships were subsequently tested against bedrock, soil, and land-use information to ensure their distribution was climatically controlled.
With the identification and classification of the Climate Regions completed, our efforts focused on improving the accuracy of the climate region boundaries for mapping. We assumed that the topographic placement, or presence, of selected climatically controlled species would best indicate the boundary of their respec- tive Climate Region. The tree species or species groups served essentially as ecological thermometers. GIS generated forest inventory maps depicting stands older than 50 years in age were overlaid on 1:250 000 scale National Topograph- ic Series maps (Department of Energy, Mines and Resources, 1972) to identify species/topographic relationships. The Climate Region boundaries were refined to become the elevation above sea level at which the indicator tree species became present/absent. On-ground forest surveys examining the occurrence of indicator
LAND CLASSIFICATION IN THE FUNDY MODEL FOREST 1 5 5
a b ' ~ a . t . ' . j ' 7 7 . . . - - ' . ~ ~ ~ ' . : : " �9
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c d Fig. 4. Maps of southeastern New Brunswick with selected species distributions generated from the New Brunswick Forest Inventory by ARC/INFO Geographic Informations System, along with a contour map for the same area to demonstrate species/topographic relationships for the Model Forest.
tree species across these elevational gradients validated the species/topographic relationships determined from the prior map overlay method.
3.2. GEOMORPHOLOGIC DISTRICTS
Geomorphologic Districts (GD), which form the second level of the ELC, are defined as major landforms within a Climate Region that support regolith with min- eralogic and textural characteristics distinct from adjacent GDs. GDs are important because they indicate the principal regulators of meso-scale climate influences and the supply of soil nutrients which together influence all terrestrial and aquatic ecosystems. GDs account for the pattern of landform that together comprise the physiography of a Climate Region. In southern New Brunswick, landform is cre- ated largely by bedrock. Glacial transport was often less than 2 km, hence the soil
156 BRUCE E. MATSON AND RANDAL G. POWER
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fertility, texture and moisture properties are highly coincident with the underlying bedrock. Bedrock was therefore the focus of this level of the ELC.
The identification of GDs began by examining bedrock formation maps (McLeod et al., 1994) at 1:250 000. Each bedrock formation within a Climate Region was placed on a potential fertility matrix (New Brunswick DNRE, Timber Management Branch, 1985) that ranked both its weatherability and mineralogy (Figure 5). Weatherability, on the y-axis, considered the rate at which the bedrock could supply the amount and texture of soil parent material, and was also indicative of the formation's topographic/landform influence as created over geologic time. Mineralogy (z-axis) referred to the potential of the bedrock to supply basic ele- ments (calcium, magnesium, sodium, etc.) to soil parent material. In general, the hardest rocks (e.g., felsic volcanics) have lower potential fertility and create higher elevational landforms. More weatherable rock, such as limestone, rank higher in potential fertility and create low elevational landforms.
A provisional GD map was constructed that consisted of individual formations, or combinations of formations that were ranked similarly on the potential fertility matrix. These were mapped using the original bedrock formation boundaries. With the fertility requirements of the classification addressed, the provisional map was
LAND CLASSIFICATION IN THE FUNDY MODEL FOREST 157
overlain by a 1:250 000 topographic map to ensure landform characteristics within each provisional GD were homogenous.
On the Geographic Information System, data were summarized for each of the GDs for forest cover, land use, and regolith/soil attributes. Until lower classification levels are completed, a GD was validated if patterns of tree species distribution, or percent agricultural land in absence of forest, was clearly different from adjacent districts. These forest and land use patterns are good indicators of GD boundaries because they are controlled largely by landform and fertility.
3.3. DATA COLLECTION FOR REGOLITH SECTIONS AND SITE LEVELS
The Regolith Section (RS) is the third level of the classification, and represents subdivisions of the GD. RSs are defined as quaternary deposits that are homoge- nous with respect to lithology, mode of deposition, depth, textural characteristics and coarse fragment content. Each RS supports a set of vegetative communities whose spatial distributions and species are distinct from adjacent RSs due to their uniformity of soil parent material. Because of the nature of depositional processes, RSs with similar modes of deposition tend to be located in the same topographic environment.
The Site Level subdivides RSs by elevation, aspect, slope, position on slope, soil drainage, and soil moisture into forest stand size polygons. Each site delimited by these topographic features is predicted to support a distinct vegetational complex with respect to species and successional development. Sites are named by the dominant, late successional tree species and when grouped together they form the landscape mosaic of overstory and understory plants within each RS.
The GIS forest inventory data used to classify Climate Region and GD lacks information on ground flora and detailed overstory tree species composition, hence necessitating on-site data collection. Data collection was undertaken to test two pre- dictions: (1) each RS supported a forest composition distinct from other Sections; and (2) a distinct set of Sites and their associated late successional communities could be identified for each RS.
For purposes of developing the classification methodology for the RS and Site levels, the 332 km 2 Boss Point Geomorphologic District (3.04 in Figure 6) was selected as a test area. This area was chosen because the percentage of unnatural vegetation-site relationships, such as white spruce (Picea glauca (Moench) Voss) on old fields, was low. Forest Soil Maps at the 1:50 000 scale were prepared for three RSs within the Boss Point GD: the Sunbury, Reece, and Stony Brook Regolith Sections (see Table II; Colpitts et al., in press).
Data were collected at points on transects oriented perpendicular to elevational gradients (i.e., from slope bottom to ridge top). Transects were replicated four times for each of the four cardinal aspects for each of the three Regolith types. The direction of the transects were generally within 15 degrees of the cardinal aspects, for example north transects ranged from 345 ~ > North (0 ~ < 15 ~ The sampling
158 BRUCE E. MATSON AND RANDAL G. POWER
Fig. 6. Ecological land classification map of southeastern New Brunswick showing the five Climate Regions and 43 associated Geomorphologic Districts for the Fundy Model Forest.
along cardinal aspects was supported by results from Clatterbuck and Gregory (1991) where this procedure captured the variation of community types influenced by topography. By overlaying GIS generated acetates of mature forest cover on 1:50 000 topographic maps, transects were located in areas with stands 50 years in age and older. The minimum stand age requirement would not necessarily identify late successional communities, however it was predicted that late successional species (e.g., red spruce, sugar maple (Acer saccharum Marsh), eastern hemlock (Tsuga canadensis (L.) Carr.), eastern white pine (Pinus strobus L.), etc.) would be present and subsequently captured in the understory sampling. The transect lines were then transferred to 1:12 500 scale forest inventory maps and sample plots were prelocated in each stand the transect crossed. Additional plots were inventoried when stand composition changes were identified during sampling. Transects ranged in length from 1 to 2 km and contained from 6 to 15 sample plots.
LAND CLASSIFICATION IN THE FUNDY MODEL FOREST 159
TABLE II Mode of deposition, petrology, depth, texture, drainage, and pH for the three Regolith Sections found in the Boss Point Geomorphologic District (adapted from Fahmy et al., 1986).
Regolith Mode of Petrology Depth to Texture Drainage pH Section deposition contrasting
layer
Sunbury
Reece
Ablation till grey sandstone > 100 cm
Ablation till grey sandstone 30-65 cm over lodgement red shale till
Stony Brook Lodgement till red sandstone 30-65 cm and shale
coarse rapid-well < 5.5 gravelly sandy loam
coarse to well < 5.5 medium PM1 ~ sandy loam PM2 a sandy clay loam
fine to medium well < 5.5 loam to sandy clay loam
aPM1, PM2 refer to stratified parent materials (1 = upper, 2 = lower).
A two-person field crew established 310 plots in 43 transects during the 16 week field season.
At each plot the site variables of slope, aspect, slope position, surface topogra- phy, and rockiness were recorded. Soil pits were dug and field analysis identified texture(s), drainage, depth to restricting layer, horizonation, and signs of distur- bance (e.g., charcoal to indicate past fires). Elevation for each plot was determined from 1:50 000 topographic maps and later added to the field tally sheet.
Within each plot, standard field survey procedures were implemented. Basal area for each tree species was recorded with a 2 BAF metric glass prism and an ocular estimate of crown closure was estimated. For each tree species recorded in the prism sweep, a representative dominant/codominant tree was measured for age, diameter at breast height (1.3 m), and total tree height. Within three circular 4 m 2 (r = 1.13) plots, shrubs, herbs, mosses and grasses were recorded as present, by species and by percent coverage of dominant species (those species comprising 5 % coverage and greater) through ocular estimation. The three plots were laid in line, perpendicular to the slope, with one plot on prism centre and the others 10 m to each side. Species were recorded in two height categories, one including shrubs and ferns and the second including all other lower (generally 20 cm and less) ground vegetation. The number of individual seedlings of each tree species was recorded for each 4 m 2 plot. A sweep of the general area was used to identify and record other plant species present. Records were made on signs of stand disturbance, origin, maturity and health (presence of insects and disease).
160 BRUCE E. MATSON AND RANDAL G. POWER
3.4. ANALYSIS OF REGOLITH SECTION DATA
Analysis of the field data focused first on testing the prediction that each RS sup- ported a distinct set of tree species or community types or topographic arrangement of communities. For each transect, a toposequence of communities was generat- ed by listing the dominant overstory and understory tree species from each plot against the elevation from which they were sampled. This allowed us to visually assess the patterns of tree species and community types for each cardinal aspect on each RS. Each RS was validated if it supported a forest composition distinct from adjacent RSs in that GD. This would then corroborate the use of the Forest Soil Maps (Colpitts et al., in press) as the third level of the ELC.
3.5. ANALYSIS OF SrrE DATA
The purpose of the analysis of site data was to identify the discreet set of vegetative communities that occurred within each RS, and to identify the site variables (e.g., slope, aspect, elevation, soil moisture, etc.) that control the community distribution. The Climate Regions, GDs and RSs, by definition should have already isolated the factors that regulate the major energy and nutrient systems that ultimately control the species composition at the Site Level. Therefore analysis is done using only site data and for each RS separately. The Sunbury RS was selected to demonstrate the methodology for the Site Level analysis.
The community toposequences from the RS validation process were used to form preliminary sites, composed of similar overstory tree species and similar topographic variables. For example, all transects on north aspects were integrated on paper and communities that were similar to those described in the publication 'Forest Cover Types of the United States and Canada' (Eyre, 1980) were recorded. This was aided by sorting the tree species data on computer to identify prominent species-topographic relationships. This allowed us to corroborate ecological rela- tionships identified in the field, and to provide a basis for evaluating output from the more complex cluster analysis and ordinations of site data.
A clustering of overstory tree species using Dual Nomination Analysis (Wuest, pers. comm.) was undertaken with the 136 sample plot data from the Sunbury RS. This analysis was designed to provide clusters of sample plots that had similar overstory composition, and would subsequently provide the vegetative basis (i.e., community types) for the Site Level classification. The environmental site variables (e.g., slope, elevation, etc.) were then summarized for the plots contained within each community cluster. An Analysis of Variance (ANOVA) of the environmental variable compared the variation between clusters versus within clusters to test the strength of the community-site relationships.
LAND CLASSIFICATION IN THE FUNDY MODEL FOREST 161
4. Results
4.1. CLIMATE REGIONS
Five Climate Regions were identified and mapped at 1:250 000 (Figure 6). The positioning of these regions generally corresponded to those proposed by previous authors (Figures 2 and 3), however the resolution of the mapping was greatly increased through the use of Landsat and GIS based forest inventory. Resolution improvement was first achieved by repositioning the boundaries to correspond to the physiographic patterns and associated proportions of coniferous/deciduous forest. Each Climate Region was supported by climate, physiographic and forest composition information required to meet the ELC climatic classification criteria (Table III). The number of tree species present in each Region was indicative of the magnitude of forest compositional differences. The Fundy Coast was the least diverse, containing 11 tree species and stands dominated by red spruce, balsam fir and yellow birch. In contrast, the Southern Uplands contained 30 tree species: tolerant hardwood associations including sugar maple, beech, yellow birch, white ash (Fraxinus americana L.), and ironwood (Ostrya virginiana (Mill.) Koch), fire- dependent species such as pines, red oak (Quercus rubra L.) and aspens, as well as softwood communities with black spruce (Picea mariana (Mill.) B.S.P.), white spruce, red spruce, cedar (Thuja occidentalis L.), and larch.
The five regions were compared to those of the Loucks (1962) and Dzikowski et al. (1984). The delineation of the Southern Uplands Climate Region by Loucks (1962) was the only discrepancy. Dzikowski et al. (1984) did not differentiate the Southern Uplands because weather stations in the Uplands (Sussex, 21 m ASL) and the Lowlands (Moncton, 71 m ASL) report similar climatological character- istics. However, the Sussex station is in a broad, low elevational valley, and does not represent the climatic variability associated with the 120 m (+) relief. Loucks recognized the influence of higher elevations on the forest (Zones, 2a and 3b) (Fig- ure 3) and distinguished them from the Lowlands Region. Loucks' (1962) Zones 2a and 3b were grouped for the ELC because differences in forest composition, temperatures and precipitation were of a magnitude that would be recognized at the GD level. Field surveys in the 2C (Figure 2) region of Dzikowski et al. (1984) could not find tree species present that would indicate a climate wanner than the rest of the Fundy Highlands (3D). The presence of white ash, ironwood, eastern hemlock and possibly eastern white pine may have supported a growing degree day classification similar to that of the Southern Uplands, and therefore a separate region. However these species were not found, and 2C (Figure 2) was included in the Highlands.
As part of the field reconnaissance to validate the species-elevation relationships that were used to map each region, it was determined that the Climate Regions could be mapped to a 1:50 000 scale. Where the original boundary, drawn at 1:250 000, crossed the mouth of valleys that incised edges of the Fundy Highlands, indicator
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leva
tion
275
-300
m;
stro
ng p
atte
rn
of t
oler
ant
in g
row
ing
seas
on;
avg.
rel
ief
120-
220
m;
hard
woo
ds o
n ri
dget
ops
sM,
Be,
yB
, rM
; fr
ost
pock
ets
in v
alle
ys;
mix
edw
ood
alon
g sl
opes
and
con
ifer
co
oler
, m
oist
er r
idge
tops
do
min
ated
val
leys
- r
S, b
F, w
S, e
ll,
wP,
rP,
jP,
bS,
tA
, lt
A;
exte
nsiv
e ag
ricu
ltur
e in
val
leys
New
m
oist
ure
defi
cit
in g
row
ing
rela
tivel
y fla
t, ge
ntly
B
runs
wic
k se
ason
; sh
ort
grow
ing
seas
on
roll
ing,
tow
ele
vati
on;
Low
land
s m
ax.
elev
atio
n 18
5 m
; av
g.
reli
ef 4
0-75
m;
gene
rall
y po
or d
rain
age
30 t
ree
spec
ies
dom
inan
ce o
f co
nife
rous
for
ests
of
rS
, bS
, bF
, w
ith
rM,
wB
, tA
for
min
g m
ajor
ity
of m
ixed
sta
nds;
dom
inan
ce o
f fi
re s
peci
es w
P, r
P, j
P, t
A,
wB
; eC
e, e
ll,
eL a
lso
pres
ent;
rari
ty o
f sM
, B
e, y
B,
wA
s, I
long
est
grow
ing
seas
on a
nd
area
of
gent
ly r
olli
ng te
rrai
n;
32 t
ree
spec
ies
high
est
grow
ing
degr
ee d
ays
max
ele
vati
on 1
20 m
; ab
unda
nce
of G
reat
Lak
es-
in p
rovi
nce;
lar
ge l
ake/
rive
r av
g. r
elie
f 45
m
St.
Law
renc
e sp
ecie
s su
rfac
es
exte
nd f
rost
fre
e si
M,
aE,
gAs,
wA
s, B
u, s
M,
Be
seas
on;
war
mes
t su
mm
er
ell+
iO
+ rP
, w
P te
mpe
ratu
res
reco
rded
Gra
nd L
ake
north
bou
ndar
y de
line
ates
are
as
whe
re r
elie
f is
gre
ater
tha
n 12
0 m
and
w
here
tol
eran
t ha
rdw
oods
dec
reas
e in
abu
ndan
ce
shar
es
boun
dary
wit
h So
uthe
rn U
plan
ds
whe
re c
onif
ers
incr
ease
in
abun
danc
e an
d w
here
rel
ief
is l
ess
than
120
m
boun
dary
out
line
s ro
ughl
y a
max
. 5-
10 k
m a
rea
from
wat
ers
of
Gra
nd L
ake
and
Sain
t Jo
hn R
iver
' S
pec
ies
code
s: s
M =
sug
ar m
aple
, B
e =
A
mer
ican
bee
ch,
wP
= e
a.,,;
tem
whi
te p
ine,
rP
= r
ed p
ine,
jP
= j
ack
pin
e, e
H =
eas
tern
hem
lock
, rS
= r
ed s
pruc
e, w
S =
wh
ite
spru
ce,
bF
= b
alsa
m f
ir.
eCe
= e
aste
rn w
hite
ced
ar,
eL =
eas
tern
lar
ch,
yB
= y
ello
w b
irch
, w
B =
whi
te b
irch
, rM
= r
ed m
aple
, tA
= t
rem
blin
g as
pen+
ItA
= l
arge
-too
th a
spen
, w
As
= w
hite
ash
. 1
= i
ronw
ood,
siM
-
silv
er m
aple
, aE
= A
mer
ican
elm
, gA
s =
gre
en a
sh,
rO =
red
oak
. B
u =
but
tern
ut
t"3
t'n
.m
o Z > Z 131
>.
Z > t'-' P
LAND CLASSIFICATION IN THE FUNDY MODEL FOREST 163
T A B L E IV
Climatic data '~ summary for climate regions within the Fundy Model Forest, the locations of meteo- rologic stations and their elevation above sea level.
Climate parameters
Climate Region Fundy Fundy Southern New Brunswick Grand Coast Highlands / ' Uplands Lowlands Lake Saint John A Wolfe Lake Sussex Moncton A Fredericton A ( 103mAS L ) (305mASL) ( 2 1 m A S L ) ( 7 1 m A S L ) ( 1 6 m A S L )
mean summer e temp (~ 13.8 13.1 15.1 14.7 15.5
mean max. summer temp (~ 18.7 18.9 21.3 20.7 22.9
mean rain. summer temp (~ 8.9 7.2 8.8 8.7 9.1
mean summer rainfall (mm) 566 520 442 443 455
pot. evapotranspiration (mm) 431 427 477 460 484
annual GDD e > 5~ 1526 ~ 1600 d 1720 1649 1760
mean summer wind dir S nd nd SW SW
mean summer wind speed (km/h) 15.6k nd nd 15.6 12.2
no. days with fog/year 102 nd nd 59 4 l
no. frost free days/year d 130 I l0 90-1 i0 90 130
aAtmospheric Environment Service climate normals 1960--1991. bPeriod of record 1976-1990. CMay to September means. dDzikowski et al., 1984. ~Growing Degree Days.
species were found in the valleys, maintaining these topographic relationships. The resolution of mapping using species was successful because strong relief and strong climatic gradients in southern New Brunswick result in species relationships occurring over short distances (1 to 2 km).
The use of tree species distributions (from the New Brunswick GIS forest inventory) to indicate abiotic influences was limited in areas where agriculture was prevalent. The Grand Lake Climate Region could not be refined for this reason, and Loucks' (1962) delineation for this area was retained. This Region has the warmest climate in New Brunswick and the forests support species with more southern affinities: bur oak (Quercus macrocarpa Michx.), green ash (Fraxinus pennsylvanica var. subintegerrima (Vahl) Fern.), silver maple (Acer saccharinum L.), butternut (Juglans cinerea L.), white elm (Ulmus americana L.) and red oak. However these species are not identified in the GIS inventory. Forest sampling for the lower levels of the classification will have to provide data to better delimit this region.
Meteorologic data (Table IV) supported the delineation and classification of each of the five Climate Regions. As an example, growing season moisture deficits (Thornthwaite, nd) were calculated from these data to demonstrate temperature-
164 BRUCE E. MATSON AND RANDAL G. POWER
moisture relationships to the forest compositions. The Fundy Highlands, with a 93 mm surplus of growing season (May to September) moisture, does not support fire-dependent species due to the cool, moist climate and lack of significant wildl- fires. The Southern Uplands with a 35 mm moisture deficit, has a well documented fire regime and contains abundant fire-adapted species. Because the Highlands con- tain abundant, well to rapidly drained, coarse textured soils that normally support Pinus spp., climate was isolated as a factor controlling forest composition.
In addition to recorded weather station data, the application of non-numerical meteorologic knowledge was fundamental to understanding the climatic patterns in southern New Brunswick. The integration of this knowledge with station data allowed us to assess the potential impact of weather parameters not measured (e.g., fog is known to provide significant moisture inputs to coastal forest systems). Secondly, we could verify the temperature/moisture relationships in areas such as the Fundy Highlands Region that lacked 30 year normal climate data. Finally, we could assess the area of influence of climate parameters based on the predicted interaction of weather systems with the physiography.
4.2. GEOMORPHOLOGIC DISTRICTS
Forty-three Geomorphologic Districts ranging in size from 10 to 480 km 2 were delineated from 71 bedrock formations (Figure 6). GDs created by more fertile bedrock, such as calcareous mudstones in the lower right of the fertility grid, occupied valley positions and less rugged topography because of their increased weatherability. In contrast, the least weatherable bedrock types, such as felsic volcanics ranked in the upper left of the potential fertility grid, formed the highest elevation (ridges and uplands) and most rugged topography in the FME
The identification of GDs based on relative fertility, prior to reference with topographic maps, was successful in explaining the differences in forest compo- sition and land uses within the Climate Regions. This occurred because landform and fertility, as controlled by bedrock, are inextricably linked. For example, GDs composed of igneous rocks or conglomerate supported the highest percentages of tolerant hardwood forest and the lowest percentages of agricultural land. This relationship occurred because the higher elevations supported generally warmer and better drained sites that are required for sugar maple in this part of North America. Agricultural use is limited due to the lower fertility and shallower soils of the less weatherable bedrock, and the higher relief and rugged terrain that is less trafficable for farm machinery. Either landform or fertility was isolated as the factor controlling forest pattern, and subsequently used to validate the delineation of each GD.
In several cases, the validation of GD boundaries was achieved using the rela- tionship between tree species and bedrock influence on soil properties, not landform properties. For example, the distribution and proportion of eastern white cedar cor- responded to bedrock formations that have calcareous mineralogies. GDs formed
LAND CLASSIFICATION IN THE FUNDY MODEL FOREST 165
on limestone (e.g., GDs 1.12 and 1.13, Figure 6) supported high proportions of cedar on upland sites, whereas mildly calcareous formations (e.g., GD 3.17) supported cedar along seepages where the water would support the high fertility requirement of this species. In other cases, stands containing white pine, red pine (Pinus resinosa Ait.) and jack pine (Pinus banksiana Lamb.) occurred in greater proportions on quartzose sandstone/conglomerate because of the coarse, well drained and acidic nature of the soils that developed. In the Southern Uplands Climate Region, these coarse soils accentuate disturbance by fire and provide dry sites where pine species have a competitive advantage.
The GD classification proved effective in explaining the variation in forest composition within each Climate Region. The Southern Uplands Climate Region, in particular, is diverse topographically and supports over 30 species of trees in many communities. The GD classification and mapping divided the complex pattern of forest communities into homogenous landscape units, and also identified how the landform and fertility differences controlled the species composition. Nineteen GDs, almost 1/2 of the 43 identified for all five Climate Regions, occurred in the Southern Uplands, attesting to its diversity.
The igneous and metamorphic formations did not show the range of fertility, and the subsequent vegetative response, that was found with sedimentary for- mations. We predicted that felsic and mafic volcanics, the extremes of potential fertility for igneous bedrock in the FMF, would support different tree species and community types due to fertility differences. Evaluation of stand types from the GIS forest inventory and subsequent field reconnaissance could not identify tree species changes that would support this prediction. Bedrock in this climate Region has undergone much deformation, weathering, and transport through glaciation (Rampton et al., 1984) and supports soils with little variation or association with any particular rock type. Most soils reveal a morphology consisting of many lithologies and similar degrees of stoniness, depth, and land capability. Therefore, changes in the proportion of tolerant hardwoods and red spruce-balsam fir forests became the dominant factor delineating the six Districts in the Fundy Highlands, placing more emphasis on the morphological control on forest ecosystems. GDs were subsequently identified that contained igneous formations of various litholo- gies. The influence of this potential fertility difference on factors such as tree growth or ground vegetation will have to be investigated at lower levels of the classification.
4.3. REGOLITH SECTIONS
Field sampling for the Boss Point GD yielded information that proved that the RSs support different forest compositions. These conclusions were drawn based on comparison of tree species and community types on each RS. Sunbury RS associ- ations consistently supported white birch-beech communities on elevations above 165 m; sugar maple was generally absent. In contrast, upland tolerant hardwood
166 BRUCE E. MATSON AND RANDAL G. POWER
TABLE V
Species-site relationships for common tree species on the Sunbury Regolith Section.
Tree Species Controlling Site variable(s) # of plots species within site variables
# of plots species was recorded
Trembling Aspen 60 to 185 m ASL, all aspects 34/47 (72%)
Large-toothed Aspen 300 to 185 m ASL, N and W aspects 28/34 (82%)
White Pine 60 to 180 m ASL, N aspects mostly 43/57 (75%) American Beech _> 165 m ASL to crest (260 max.) 37/44 (84%)
Sugar Maple _ 165 m ASL to crest (260 max.) 18/23 (78%)
Hemlock slopes _> 20% 11/20 (55%)
Red Pine sand and loamy sand textured soils 15/24 (63%)
Red Spruce all positions 84/84
White Birch all positions 100/100
Red Maple all positions 104/104
communities on the Reece RS consisted of associations containing sugar maple and beech. However, the proportion of tolerant hardwood communities on the Reece RS was less because the hilltop elevations were generally 50 m lower than the Sunbury RS. The occurrence of sugar maple and beech on Reece RS soils is attributed to the higher moisture regimes and increased fertility due to the basal undertill and finer textures. Both RSs are derived from the grey, acidic, coarse textured sandstone, however the coarse sands of the Sunbury RS may be too infertile and dry for sugar maple. The drier soils of the Sunbury RS may have also supported more frequent and more intense fires which would reduce the abundance of sugar maple.
The third RS, Stony Brook, supported tree species compositions not found on any other area sampled in the Boss Point GD. Black spruce and balsam fir were abundant on the compact, heavier texture and moister soils. These results satisfied the regolith clarification criteria, however it raised the question as to whether the Stony Brook RS should have been included within the Boss Point GD. The Stony Brook RS is more abundant on Districts 3.05 and 4.01 located on the north side of the Boss Point. This is a mapping problem that will be more readily addressed when on-site sampling for the adjacent GDs is complete. This scenario demonstrated very well how the lower levels of the classification can be used to help refine upper level boundaries.
4.4 . SITE RESULTS
The preliminary analysis using simple data sorting techniques identified significant species-site relationships. Table V lists these relationships for 10 of the 20 species that were sampled in the Sunbury RS. In the second column opposite the species, the site variables are those that define the species distribution in this area. The third
LAND CLASSIFICATION IN THE FUNDY MODEL FOREST 167
column lists the strength of this species-site relationship expressed as the number of plots for which the relationship held true, over the total number of plots within which the species was recorded (the total number of plots sampled was 136). For example, trembling aspen was found at elevations below 185 m in the Sunbury RS where elevations range from 65 to 275 m. This relationship held true in 34 of 47, or 72%, of the plots for which trembling aspen was recorded. For american beech (Fagus grandifolia Ehrh.), 37 of the 44 plots (84%) occurred above 165 m and were all located on upper to hill crest positions. The strength of the species-site correlations were the first testament to the success of the three upper classification levels to isolate factors that control the distribution of forest ecosystems.
These tree species-site relationships, when combined with the community toposequences, resulted in the identification of nine preliminary Sites (commu- nities). Each Site had a distinct overstory vegetation and limited overlap with adjacent Sites with respect to the site variables. The division of Sites around the 165 m ASL was the strongest relationship affecting almost all communities, in particular because trembling aspen was found below this elevation, whereas white birch was the major intolerant hardwood above this elevation. Given the homogenous soils and moderate relief for this RS, the strong relationship between vegetation and topographic variables was expected. The coarse sandy to loamy sand soils, about 1 m deep, were very consistent in moisture regime, with only 4 of 136 plots (3%) having soil mottles indicating increased moisture. Therefore, species distributions along elevational gradients are more likely influenced by Site Level meso-climatic factors (i.e., diurnal and seasonal air temperature regimes as influenced by topography) and their effect on soil temperatures. For a similar area in the Southern Uplands Climate Region, the length of the frost-free season is 80 days in valleys and 150 days on upper slopes (Reed and Smith, nd).
The clustering of sample plots based on overstory tree species composition yielded 10 communities for the Sunbury RS. The dominant tree species for each cluster matched closely the preliminary communities, in effect supporting both processes of Site identification. Through summarizing the site variables for the plots contained in each cluster, the relationships of species-site once again became apparent and were invaluable for deciphering the computer analysis. For example, clusters 2 and 3, in which red spruce and red maple were co-dominants, appeared similar at first assessment, however cluster 2 had significant amounts of trembling aspen and cluster 3 had trace amounts. From the preliminary analysis of data we know aspen is found below 185 m, and this elevational division was what distinguished the two clusters. Cluster 2 is a 60 to 185 m elevation red spruce- trembling aspen-red maple-white pine community, whereas cluster 3 is a 185 m to hill-crest red spruce-white birch-red maple community.
The clusters showed patterns of tree species and the number of community assemblages that were expected from the Sunbury RS. The ANOVA demonstrated that the between cluster variation with respect to topographic positioning of clusters was also significant. However, too much variation existed within clusters to satisfy
168 BRUCE E. MATSON AND RANDAL G. POWER
the resolution required by the authors. For example, in Cluster 2, described in the previous paragraph, 4 plots out of the total of 18 plots contained species (sugar maple or ironwood or striped maple (Acerpensylvanicum)) that are not associated with red spruce-aspen-red maple-white pine at lower elevations. The analysis of field data to construct the Site Level of the ELC, however, has demonstrated that the community-site relationships are definable with a resolution that will subsequently permit mapping with further investigation.
5. Discussion
The ELC is not simply an ecosystem identification and mapping process. It pro- vides an understanding of ecosystem form and function by linking the abiotic and biotic components of each system. Understanding more than the mapping units is crucial to getting the most out of the ELC in resource management. Therefore, associated with each climate Region are silvicultural and meteorological processes that influence that Climate Region and all organisms associated with subsequent levels.
GIS technology was invaluable for integrating data sources with different map scales, and improving the efficiency at which sorting and integration of data pro- ceeded. However, the resolution of levels remains contingent upon the detail of the original data sources. The New Brunswick forest inventory groups sugar maple, red maple, yellow birch, and beech on upland sites as Tolerant Hardwoods (TH). Problems arose in delineating the Fundy Coast Climate Region boundary because the absence of sugar maple and beech below 275 m ASL was the delineating cri- teria. On-site sampling was therefore required to check the species composition of tolerant hardwood stands located in the Fundy Coast Region (Figure 4). With these relationships defined, the ELC can be integrated with the forest inventory to refine the species compositions of TH stands occurring on the Fundy Coast Climate Region.
The nested classification and mapping structure of the ELC presents ecosystem information in a format that is intuitive. By accurately defining a Climate Region, for example, we determine that all GDs within are homogenous with respect to certain climate variables, and that GD boundaries do not extend past the Climate Region boundary. The nested structure allows users to examine the forests support- ed by GDs within a climate Region, and know that their compositional differences are controlled by landform-fertility relationships, and not climate. The nested struc- ture, as applied to all classification levels, identifies and provides the hierarchical linkage between ecosystems from the size of forest stands up to climate regions.
The nested structure also provided a framework that made the collection and analysis of data very efficient. The specific data required to test the significance of forest patterns for each level were identified from the classification criteria. For example, the validation of GDs was based on the distribution of selected indicator
LAND CLASSIFICATION IN THE FIJNDY MODEL FOREST 169
species whose silvics were indicative of landform-fertility relationships associated with the GD level. The collection of comprehensive site data would have greatly surpassed the data requirements for this level of classification. The nested structure gave us confidence that species distributions were controlled by the factors being studied at each level of the hierarchy, and simple analysis procedures prevailed.
Developing an accurate ELC product was achieved more through trying to reject the predicted ecological relationships than trying to support them. For Climate Region delineation, the tree species distributions were tested against bedrock, soil and land use variables to ensure that they were climatically controlled. Pines are virtually absent from the Fundy Highlands, however the region supports large areas of igneous derived coarse loamy, well drained upland soils that support tolerant hardwoods. Because soils of similar composition support pines in the Southern Uplands, climate is the only factor capable of controlling this distribution. It was considered unlikely that the lack of Pinus spp. in the Fundy Highlands was an artifact of logging or agricultural practices, especially given the regenerative capabilities of white pine after most types of disturbances. The fact that very few white pine do exist on abnormal xeric sites along south facing canyon ridges and associated active colluvial deposits supports the conclusion that climate is controlling pine distribution. Only on these sites are the soils warmed sufficiently by sunlight and there is mineral soil exposure to allow for seedling establishment, features that are normally provided after fire in other Climate Regions.
Tree species distribution was pivotal to understanding and validating the influ- ence of abiotic factors on forest systems. This was based on the prediction that plants, as the ultimate integrators of environment influences, are representative of the environments in which they grow. This assumption has been made for larger areas (e.g., Forest Regions of Canada (Rowe, 1972)), however this ELC process has demonstrated its successful application to lower level ecosystems. Within the Boss Point GD, tree species requiring moderate moisture or fertility, such as white ash, eastern white cedar, black spruce, ironwood, larch (Larix laricina (Du Roi) Koch), yellow birch, silver maple, are absent or rare. Although these species are charac- teristic and common in the same Climate Region, the coarse and infertile nature of RSs in the Boss Point GD restrict their occurrence. Hence, the silvics of trees provide many well documented (Burns and Honkala, 1990a, 1990b) species-site relationships that support this biotic-abiotic linkage.
Our system of ELC does not include map scale as part of the classification criteria. Including scale tends to place emphasis on the size of the ecosystems, rather than the magnitude of the abiotic factors controlling them. Mapping scale is primarily a function of the steepness of the environmental gradients, therefore use of different scales for similar classification levels should be expected in other jurisdictions. This should facilitate the application of this land classification method and the subsequent comparison of results between jurisdictions.
The application of field data to refine and validate polygon boundaries was fundamental to providing quality delineations. The data also served to identify
170 BRUCE E. MATSON AND RANDAL G. POWER
ecological relationships previously unknown or invisible using remote methods. White pine, red pine, and jack pine were chosen as the indicator species for the boundary between the Fundy Highlands and the Southern Uplands Climate Regions because they are widely distributed and are identified in the GIS inventory. The silvics of these species were used as indicative of Climate Regions with growing season moisture deficits and fire regimes with the frequency and intensity required to maintain fire-adapted species. During field sampling it was recognized that trembling and large-toothed aspens followed the same relationship as pines (absent above 225 m ASL). Because aspens are much more widely distributed than pines, this aided validation of Climate Region boundaries immensely. The coincidence of the pine and aspen distributions along the 225 m contour strengthens the prediction that climate is in fact controlling their distribution. These pine-aspen relationships could not be tested beforehand because the GIS forest inventory does not distinguish between species of intolerant hardwoods, and white birch is present in both the Highlands and Uplands Climate Regions. Field sampling strengthened the accuracy of the mapped-actual relationships that will promote the application of the ELC for practical application.
6. Conclusions
The ELC methodology developed for the Fundy Model Forest was successful in describing and mapping Climate, Geomorphologic and Regolith-controlled for- est ecosystems. The systematic procedure ensured that each map polygon was described and classified according to the criteria established for the appropriate lev- el. The classification proceeded from the top down (Climate Region to Site Level). Five Climate Regions were mapped and described in terms of climate, physiogra- phy, and their unique forest composition. Geomorphologic Districts explained the variability of forest composition within these regions and demonstrated the signif- icance of bedrock in controlling the southern New Brunswick landscape. Regolith Sections were mapped using existing soil maps, and a preliminary classification indicates that spatial referencing of the Site Level is also achievable.
The nested structure of the ELC, as applied to all classification levels, identifies and provides the hierarchical linkage between ecosystems from the size of Climate Regions to forest stands. The nested structure also provided a framework that made the collection and analysis of data very efficient, and gave confidence that species distributions were controlled by the factors being studied at each level of the hierarchy. The distribution of tree species was pivotal to understanding and validating the influence of abiotic factors on forest systems. Ultimate refinement of the ELC will come when the Site Level Classification is completed for each Climate Region.
LAND CLASSIFICATION IN THE FUNDY MODEL FOREST 171
Acknowledgements
This project was funded by the Ecological Land Classification Initiative of Nat- ural Resources Canada Green Plan, and supported by Richard Sims and Taumey Mahendrappa of Natural Resources Canada. Thanks to Vince Zelazny, Mark Col- pitts, Mar tha Gorman , and Bryce Mclnnes of New Brunswick ' s Depar tment of Natural Resources and Energy; Judy Loo, Natural Resources Canada; Don Logan, Dave Baardsen and Todd Beech of NB DNRE, Region 3; Walter Emerich, J.D. I rving Ltd; Art Ruitenberg, Ma lcom McLeod, and Susan Johnson, NB D N R E Minerals Branch. Special thanks to Mike Haire and Francois Levesque for their contribution during the field season.
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