Flowering Phenology and Diversity of Dicots in Desert-Shrub
Grassland,
© 2004 R.M. Chew
Abstract.
_____ The timing and magnitude of flowering
of a suite of 32 dicot annuals and perennials in a degraded grassland invaded
by desert shrubs was recorded over 13 years (1987-1999). Flowering was related to weekly rainfall and
sum of degree days in six phenological seasons: winter (germination of annuals,
dormancy of perennials), early spring (vegetative growth and limited
flowering), late spring (major spring flowering),
early summer (no, or limited flowering), late summer (major summer flowering),
fall (little or no flowering, senescence and dormancy). Each season varied relative to itself through
the years from extremely wet to extreme drought and extremely cold to extremely
warm. Each year was unique in its
pattern of six seasons. Most minimum and maximum values occurred 1994-1999.
Fourteen
species flowered only in spring, 4 only in summer, 13 spring and summer, and 1
in fall. Flowering periods overlapped, but the times of maximum bloom were
often significantly separated. The duration of a species’ flowering was a
function of the time of its first bloom.
Durations peaked in late spring and then declined; durations
progressively declined in summer.
Regression
models were constructed for each species total flowering (Nt) in relation to the independent
variables of each seasonal rainfall and temperature sum, in the current year
and previous year. Total flowerings of
18 of the 32 species were significantly predictable from rainfall and
temperature, 6 for both their spring and summer flowerings. The number of significant
seasonal variables in a predictive regression ranged from 1 to 5 with a median
of 3. The first two variables to enter a model accounted for an average of 88%
of the variability of Nt
(range 56-100%).The whole models had an average adjusted R2 of 0.93. No two species
had the same first two predictive variables. For the two predominant predictive
variables, 24 of 45 are for the current year and 21/45 for the previous year;
Precipitation is a dominant variable in 31/45 and temperature sum 14/45.
At
least 15 different phenological patterns of flowering can be proposed for the
32 plant species, based on responses such as specific time of flowering,
dependence on rain in a particular season, in the present or previous year,
biseasonal flowering, seasonal flexibility, and the nature of the species:
annual, type of perennial, evergreen, deciduous succulent, and time span
between vegetative growth and flowering. In years when there was prolonged low
moisture index, based on low precipitation and / or high temperature sum, there
was not only failure of flowering, but senescence and even death of large
proportions of some species populations. There were some hierarchies of
recovery of species.
Considering
what is now known about the genetics of flowering in Arabidopsis, it is
reasonable to speculate that the biodiversity of the dicots in this semiarid
community is a result of the interaction of the great variety of rainfall and
temperature conditions, season to season and year to year, with the many
phenotypes of the available plants, as they have assembled into the community
in the short term and further adapted in the long term.
INTRODUCTION
The
annual sequence of plant phases from germination through vegetative growth,
flowering, dispersal of seeds and senescence is one of the most striking
aspects of nature, and it entices one into the observation of nature. The utility of phenological research has
expanded until it is essential for understanding the functioning of ecosystems
(
Temperature,
moisture and photoperiod are major clues determining blooming, but there is
little documentation for individual species.
Much effort has gone into trying to relate adaptation of flowering times
to different climatic patterns (Fox, 1989), and to pollinators, predators,
pathogens, competitors and nutrients (Rathcke and Lacey, 1985). As the
latter authors state (p. 191), “In general there are many more hypotheses
about ultimate factors that may mold flowering times than there are thorough
studies that permit testing them”.
The detailed studies of single species by Fox (1989) and LeBuhn (1998) illustrate the difficulties of determining
even the proximate factors.
The
study of perennials and annuals requires different perspectives. Perennials
have a flexibility of development due to stored resources in persisting plant
parts and can remain vegetative or flower.
Each year annuals have the complexities of germination, survival of
seedlings, vegetative growth, flowering, setting seeds, and dispersal of seeds
to seed banks in the soil. Whereas
annuals in semiarid sites usually germinate after light but extended rains,
which occur in most years, perennial shrubs may germinate only after short but
heavy summer rains, followed by sufficient subsequent rain, a pattern that
occurs less frequently (Went, 1949). There are a number of studies of flowering
of annuals: in the
Long-term
data sets have led to some general conclusions: (1) flowering can be predicted
from data on rainfall and air temperature, (2) often rainfall and temperature
have opposite effects, (3) the most important events occur 1-4 mo or more before
blooming, and (4) sometimes predictive formulae cannot explain failures of
flowering due to isolated extreme conditions, e.g. droughts and
freezes, which can affect blooming for years afterwards (Turner and Randall,
1987; Fitter et al, 1995; Sparks and Carey, 1995). I will use a 13 yr data set
to test these general conclusions. Here I document the flowering times and
magnitude of blooming of annual and perennial dicots of a semiarid community
with the objectives of: (1) defining the seasonal patterns of blooming for the
species, (2) analyzing the patterns of flowering magnitude in relation to
annual variation of rainfall and air temperature, and (3) assessing the
differences of species’ phenologies as the basis for plant diversity.
METHODS
SITE____ The study site is a 9.3 ha cattle exclosure
that was established July1958 in the San Simon Valley, 8 km north of Portal,
Cochise County, Arizona. The site is 1.5
km east of the foothills of the
WEATHER____ Air temperature, 5 cm
above ground, was recorded continuously, 1980-1999, from a thermal element
inside a 76 cm long x 48 cm wide x 71 cm high shelter with shade screen sides,
with double roofs 8 cm apart. Mean daily air temperatures were calculated from
field records [(max + min) / 2] and summed over weekly periods asdegree-days
above 0o C.
I
used 0o C as the threshold for biological activity
of all plant species, although there is almost no information on thresholds for
the species of the site. The threshold for flowering may
be10o for Larreaand
some other shrubs in the
Daily
rainfall, 1980-1999, was measured in a rain gauge with an internal cylinder
graduated in 0.01 inch increments to hold 1 inch total as delivered from a 10.5
cm funnel . Overflow from the graduated cylinder went into a surrounding 10.5
cm cylinder with a capacity of 28 cm of rainfall. In 1980 and 1984-1987 rainfall was also
recorded from a tipping-bucket rain gauge.
PHENOLOGICAL SEASONS____ For analysis of
plant and weather data I divided the year into six phenological seasons. These were based retrospectively on
generalities of the data for plant growth and flowering over 13 years:
(1) winter, weeks
45-4 (calendar days 309-28), ca.
November-January, a period of dormancy of perennials and germination of spring
annuals;
(2) early spring,
weeks 5-16 (days 29-112), ca. February-mid April, a period of vegetative
growth, but before the start of blooming of most species;
(3) late spring,
weeks 17-26 (days 113-182), ca. late April-June, the period of the major spring
flowering;
(4) early summer,
weeks 27-30 (days 183-210), ca. July, generally before the start of summer
flowering and germination of summer annuals;
(5) late summer,
weeks 31-39 (days 211-273), ca. August-September, the time of major summer
flowering;
(6) fall, weeks
40-44 (days 274-308), ca. October, little or no flowering, senescence or entry
into dormancy.
TRANSECT COUNTS____ I walked a
standard rectangular path (121.8 m x 152.4 m) and counted the flowering
individuals within 2 m of the center of the path ____ an area of 0.216 ha. The number of transect counts varied from 14
a year to 22 a year, depending on the time of
first and last blooming of any species.
Counts were made every 7 to 14 days. I counted a plant as blooming
regardless of its number of flowers, which reduces the information that would
be obtained if the magnitude of blooming of individual plants were noted. To do the latter would have limited the size
of transect that could have been used and the time-span of the study.
I
originally planned to limit observations to 7 yr (1987-1993), but because of
the occurrence of unusual weather conditions, I added censuses in summer 1994,
spring 1995 and summer 1996. During continuing unusual years of 1997-1999 full,
consistent counts could not be made to establish total counts (Nt) for phenological
seasons, but there were enough counts to establish numbers for peak weeks (Np).
Counts
were compiled by weeks from wk 1 (Jan 1-7) to wk 52 (Dec 25-31). Numbers were
interpolated for weeks when no count was made. The first day of blooming was
assumed to be the first day of the week in which flowers were first observed,
and the last day of blooming as the last day of the week when flowers were last
observed. The total number (Nt) for a phenological
season involves counting some individuals more than once as flowers persist
from week to week and as new flowers are produced, but the count is still a
measure of the magnitude of flowering of a species. Usually there was one week when the number of
individuals flowering was considerably in excess of adjacent weeks (peak week, Np).
GERMINATION QUADRATS____In winter 1997 I
established eight circular 1 m2 quadrats, two randomly
positioned along each side of the rectangular walking transect. These were used to follow the number of
germinating plants, and their progression to fruiting. Each quadrat
was divided into quarters. In two randomly chosen quarters germinating
individuals were removed as counted; in the other quarters they were marked
with toothpicks and left in place for further development.
ANALYSIS OF COUNTS____ I analyzed the
data for total seasonal counts of flowering individuals of a species (Nt) for their relationship to the independent
variables of rainfall and air temperature sum of appropriate phenological
seasons by forward multiple stepwise regression using the JMP Program, Version
3.1 (SAS Institute 1995). The variables
chosen by the stepwise procedure were made into a model by the least squares
program associated with the stepwise regression. Information from the
model-constructing program provided correlation values (R2) forNtwith each variable entered, and an adjusted correlation (AR2) for the whole model of n variables.
A model was rejected if it did not have an acceptable normality of
residuals, a constant variance of residuals, and acceptably low multiple collinearity, as indicated by the program. A model was also rejected if there was so
little variation among the Ntvalues of some years that they functioned as a “single” point
in determining the regression plot, or if there were years that were so
exceptionally high that they biased the plot.
Models were constructed for the Nt
values versus rainfall and temperature of the current year, and for the current
year plus the previous year. Models were
constructed for the years 1987-1993, and for these 7 yr plus data for spring
1995, or plus summers 1994 and 1996, depending on when a species flowered.
The
multiple stepwise regression procedure has obvious defects: (1) It can be confounded by correlations among independent
variables. (2) Because of the number of variables allowed into the selection
process when Ntis regressed
against the cur-rent plus previous year, there is the possibility, but not
necessity, of spurious correlations.
However, the procedure has the advantage that its predictions are
“transparent” suggestions of the predictability of Nt in terms of rainfall
and temperature, rather than the “less transparent” information
from procedures such as principal components analysis and lagged correspondence
analysis. The multiple stepwise procedure is retained here for its heuristic value in
suggesting hypotheses to be tested by experimentation.
Stepwise
multiple regression has been used successfully in the analysis of other
long-term data sets: (1) for shrubs in the Mojave Desert (Turner and Randall,
1987); (2) by Fitter et al. (1995), for data taken over 36 yr in one location
in England, who were able to fit regressions of first flowering date to
temperature for 219 of 243 species; (3) by Sparks and Carey (1995), who found
significant relationships with mean annual temperature and annual rainfall for
13 plant species in a data set extending over two centuries; (4) by Epstein et
al. (1997) who found relationships
explaining 67-81% of the variation in production of grasses of the US Great
Plains on the basis of mean annual temperature and rainfall; (5) by French and
Sauer (1974) in a study of the phenophases of three species of grasses.
RESULTS
RAINFALL____ Rainfall was biannual (Table 1 ). Winter and
early spring rains were gentle and widespread over a broad area and provided 35.8% of
the average annual total. Late spring
rains provided only 7.6%; early and late summer rains, predominantly
thundershowers, provided 50.8% of the total, and fall (October) was a hiatus of
lower rainfall, 5.8%. Variation in
regional and global weather patterns resulted in exceptionally wet and dry
phenological seasons in some years. The
interannual coefficient of variation ranged from 0.53 for early summer to 0.91
for late spring (Table 1). Variability resides in the
shorter time periods of the phenological seasons. Over broader time periods there is less
interannual variability, to the point where annual rainfall has a coefficient
of only 0.30. Winter-spring rainfall is
significantly greater in
Although
four of the five years of records from a tipping bucket rain gauge were before
the present study began (Table 2), they are assumed to be representative of the
pattern for the site; 66.5 % of the rain
events were less than 5 mm, but provided
21.9% of the average annual rainfall. Only
33 rains were > 20 mm, and those in July-September provided 31.4% of the
annual rainfall, versus 21.5 % by such events in all other months. The extremes
of the distribution are of special interest.
Although rains < 5 mm may not directly affect plant growth, Sala and Lauenroth (1982)
concluded they are important because they affect decomposition and nutrient
processing, which occur principally near the soil surface. They found that rains of 5 mm did directly
affect the physiology of Boutelouagracilis in
semiarid grassland for at least 2 days.
Summer storms > 22 mm can be sufficiently intense (0.25-1.32 mm / min) to cause runoff (Hawkinson,
1968). Fourteen storms of this
intensity were recorded by the tipping bucket rain gauge in five yr. Winter rains of even 70 mm were of too great
a duration to cause runoff. The light
rains of winter are made more effective for plants by the lower evaporation
then; the heavier rains of summer are made less effective by runoff.
TEMPERATURE SUMS____ Degree day summations
for phenological seasons (Table 3) are inherently less
variable than rainfall, with coefficients of variation ranging from only 0.03
in early summer to 0.15 in winter. Most
of the variability was in the fall through early spring. Although temperature sum is less variable
than rainfall, a small change of temperature sum may be as biologically
important as a large change in rainfall (e.g.
Scifres and Brock, 1969).
CATEGORIZATION OF SEASONS_____ To ease conceptualization,
I categorized seasons with respect to rainfall as very wet (W*), wet (W), moist
(m), normal (N), dry (d), drought (D)
and extreme drought (D*), and, with respect to temperature sum, as very hot
(H*), hot (H) , warm (wm), normal (No), cool (c), cold (C), very
cold (C*) (Table 4). The categorization was based on the
percentile distributions of values for each season across the 13 years in
Tables 1 and 3: W* and H* are values >90% quartile; W and H = 75-90%; m and
wm = 62.5-75%, N and No = 62.5-37.5 %; d and c
=25-37.5%; D* and C* <10% quartile. The categorization within a season is
relative only to itself. For example:
extreme wet (W*) is 147-154 mm rain for the winter season, but 262-297 mm for
late summer. The biological effect of
extreme drought and extreme wet will depend upon the season of its
occurrence. The categorizations of Table 4 make clear the season-to-season variation within
years and the interannual variation within a season. No combination is repeated in the 13 yr
tabulated.
THE PLANT COMMUNITY____
The 32 species
that were censused (Table 5) are listed according to
three categories of maximum densities of flowering individuals. A few species that did not bloom in at least
4 yr in the period 1987-1993 are omitted. Except for Astragalus spp. and Lepidium lasiocarpum the
omitted species are annuals that were present erratically or in very small
numbers, although some of these were exceptionally abundant in 1995 (see
later). The community is predominantly Compositae (12 of the 24 species with
densities >400 stems / ha) and perennials (21 of the 32 species).
The
censused dicots are predominantly perennials: 2 shrubs (Sh,
Table 5), which were up to1.5 m high; 2 subshrubs (SSh) of < 0.5 m height; 4 suffrutescents
(SP), still smaller and only slightly woody; and 11 herbaceous perennials (HP).
Ten censused species were annuals (A).
The
times of blooming of species (Table 5) as established by
censuses are: spring only (Sp), spring and summer (Sp/Su), and summer only
(Su), with some minor cases (Sp* and Su*) when less than 5% of blooming
individuals were counted in the other season. The dominance of spring flowering
by annuals (Table 6) is almost significantly different
from random (X2 = 5.33, 0.10> P >
0.05, df = 2). There was no
difference for perennials. All the annuals listed in Table 5 germinate in
winter, and then bloom in the spring, except Eriogonum Abertianum,
which does not bloom until summer. There are few summer-germinating annuals on
the site, none of which was abundant enough to be listed in Table
5.
SPECIES BLOOMING PERIODS ____ Spring flowering begins with an herbaceous perennial with a long tap root,
Cymopterus
multinervatus, and an annual, Draba cuneifolia (Table 7).
Thirteen other spring-only species (one Sp*) are scattered throughout the
spring period, among 13 species that bloom both spring and summer. The year ends with 3 perennials (one Su*) and
one annual that bloom only in late summer and 1 shrub that blooms only in fall.
The
average beginnings of spring blooming progress from day 61 to 147. The average
duration of blooming of species in spring (Fig. 1a)
increases from day 60 to day105 and then declines to day 150. The second degree
polynomial regression for this relationship is:
Y (days duration) = -92.934 + (2.72219 x ave 1st day) - (0.01348 x ave 1st
day2), which has an F value with probability of
0.072.
The average duration of summer flowering
progresses from day 196 to day 254 (Fig. 2a) with a
significant decline from beginning to end. The 2nd degree polynomial regression
is:
Y =
1592.06 - (12.9558 x ave 1st day) + (0.02703 x ave 1st day2). P > F =
0-.046.
The mean duration of blooming was not
significantly different for annuals and perennials, but was significantly less
for cacti, which have only a few flowers per plant and flowers that persist only one or a few days.
COMMUNITY PATTERNS OF BLOOMING____ The rainfall
and temperature sum categorizations of Table 4 were used
to generalize soil moisture indices (MI) for spring and summer of each year (Table 8). Rainfall categories, as they related to the input
of water to the soil, were assigned arbitrarily values of: W* = 3, W = 2, m = 1.
N = 0, d = -1, D = -2, D* = -3 Temperature sum categories, as they related
to evaporation of soil moisture, were given half the weight of the rainfall categories:
C* = 1.5, C = 1, c = 0.5, No = 0, wm = -0.5, H = -1,
H* = -1.5. The earliest blooming of plant species in spring was compared to
moisture indices summed for winter and early spring; earliest summer blooming
was compared to MI values summed for late spring and early summer.
The
eight spring annuals bloomed earliest in years with soil moisture indices in
the middle range of values (2.5 to -1.5) (Table 8). Earliest bloomings did not occur in years
with high MI (5.0 to 3.0), nor lowest MI (-5.5). The distribution of species bloomings
was highly significantly different from random (X2 = 30, df = 7, P <
0.005). In two years with earliest
bloomings (1995, 1989) the contribution of winter to the summed MI was positive
(1.5-2.0 respectively) but early spring was negative (-1.0, -3.0). In 1988 both winter and early spring
were positive MI (0.5 and 2.0 respectively).
The earliest bloomings of 17 perennials were spread over the range of MI
values (except the lowest, -5.5) with a tendency towards medium to low values
(2.5 to -1.5), which was marginally significant (X2 = 12.9, df 7, 0.10> P < 0.05). In spring no annuals bloomed in 1990 when MI
was -5.5; 71% of perennials also failed to bloom that year.
In
summer, with data for only three species of annuals, there was no significant
difference of distribution of earliest blooming from random. Likewise for 16 perennials (in both cases X2 0.50 > P > 0.25).
When
the magnitude of flowering (Nt) of species
is compared to the moisture
This
attempt to find community patterns in flowerings is, of course, confounded by
the adaptations of the phenotypes of individual species, which are dealt with
next.
FLOWERING
CHARACTERISTICS OF INDIVIDUAL SPECIES
WEEK OF FIRST BLOOM____ The week of first bloom each year was
regressed against rainfall and temperature sum of appropriate phenological
seasons for the 14 species that had a range of variation of first flowering of
at least 5 weeks during the years of observation (Table 10). Variables were significantly predictive of
first week of spring blooming for two of three annuals; blooming was advanced
for Baileyamultiradiata by an increase in
winter precipitation, and advanced for Gilia longifora by
increase in temperature sum in early spring. Only one of nine spring-flowering
perennials,
First
summer flowering was significantly predictive for one of three summer annuals; Baileya;flowering was advanced by increase in early
summer precipitation. Five of eight perennials were significantly predictive by
summer rainfall or temperature sum (Table 10). Gutierrezia, which does most of its vegetative
growth in winter and does not bloom until early or late summer, showed
complicated predictive relation- ships; first bloom was delayed by increased
late spring rainfall and advanced by higher late spring temperature sum, but
was advanced by increased early summer rainfall and retarded by higher early
summer temperature sum.
MAGNITUDE OF TOTAL BLOOM (Nt)____ For almost all speciesthere
was considerable variation in the number of flowering individuals censused from
year to year (Tables 11a and 11b).
These detailed data are provided for the use of anyone wishing to do a
different analysis than provided herein.
For 18 species (Table 11a) the data for
1987-1994 provided significant whole models for the regression of Nt based on rainfall
and temperature sum. For nine species (Table 11b) there
was no significant relation-ship, but the data for five species show remarkable
low and high values of Nt
in the “exceptional years” 1994-1996.
For
the 18 species of Table 11a the models of Nt are highly
significant with AR2 > 0.86 in all but one
case. Models have a median of three independent variables (range 1-5). For spring bloomings (Table
12), Nt is
predicted by a mixture of variables of the current year (10 of 24) and previous
year (14/24); the latter variables range from the previous fall through the
previous early spring. Only one annual and three perennials are principally
predicted only by current year variables. Five perennials are predicted
principally by previous year variables. Precipitation is a dominant variable in
17 of 24 cases, whereas temperature sum is so in 7 of 24.
For
summer bloomings (Table 13) variables of the current
year are more often predominantly predictive (14/21) than those of the previous
year (7/21), as compared with spring data.
Precipitation is predictive in 14 of 21 cases (7+, 7-) and temperature
sum 7/21 (2+, 5-). In one case, the number of Gilia
flowering in spring is the second most predictive variable of Gilia summer Nt,
the only species for which this is true.
Spring Nt was made an independent
variable only for models of summer
flowering.
There
is no case in which two species have the same predictive relationship for the
two most significant variables. Aster hirtifolius
and Dyssodia acerosa do
share the same two variables (Table 12) but for one of
them (Pfall*) the relationship is different.
“EXCEPTIONAL” YEARS”_____ In early
1994 it was sensed that the year was unusually dry__flowering
by spring species was zero or nearly so (Table 11a, b). Censusing of Nt was then extended to summer 1994, spring 1995
and summer 1996. For spring 1994 and 1996 no censuses were necessary, except
for a few species, since there was no or very little flowering; The symbol (*)
represents a “census” of zero in these cases Limited observations of phenology continued
through 1999. In comparison to
1987-1993, 1994-1999 were exceptional in one way or another. Five of the six
minimum values of rainfall for the six phenological seasons occurred in
1994-1999 (Table 1). The lowest total annual rainfall
occurred in 1994. Every seasonal maximum for temperature sum occurred
1994-1999, with the highest annual total being in 1999 (Table
3). Minimum rainfalls together with
maximum temperature sums suggest low soil moisture 1994-1999 except as
ameliorated by the second highest late summer rainfall in 1995. The coincidence
of certain plant phenologies with the different precipitation and temperature
sum patterns of 1994-1999 are very suggestive of cause and effect.
Year
1994 was distinctive in having warm to very hot
weather (wm, H, H*, Table 4) from winter through fall.
Annual rainfall was a minimum in 1994; only early spring and late summer were
normal rainfall (N) while other seasons were dry (d or D). There were several negative consequences for
flowering: (1) There was a failure of spring flowering
more complete than after the 1990 spring drought (62 mm total for winter
through late spring for 1990, Table 1, compared to 101 mm
for 1994. Larrea tridentata was
the only species that bloomed sufficiently to be censused, and unexpectedly had
its maximum spring Nt in1994 (Table 11a). (2) Progressive leaf stress and senescence and
some mortality of subshrubs was observed May through July. (3) Although rains
began July 7, recovery of shrubs delayed their blooming by an average of 29
days (range 10-50 d) beyond their average beginning times. A first summer census was not needed until
August 24. (4) Success of summer
flowering was varied (Tables 11a, 11b). Only a few seedlings of the biseasonal
annuals, Baileya and Gilia longiflora survived from winter to bloom. Conversely, five biseasonal perennials had an
average or above average summer Nt so that
the community aspect was dominated by the flowers of Zinnia, Parthenium
and Menodora. Summer-only
perennials varied: two bloomed near average; GutierreziaNtwas only 4% of its summer
maxi-mum (to be expected because of its winter-spring vegetative growth),
whereas fall-blooming Flourensia had its
maximum bloom. (5) Although Gutierrezia appeared to have greened-up normally through
early spring, by Nov. 425 of 472 individualswere
dead. Although even moderate mortality
of stems had not been observed previously for subshrubs, by Nov. 15.6% of Zinnia appeared dead; 25.5% of Dalea appeared
completely dead and 50% had some dead stems.
In
1995 weather was
unusual in the way in which seasonal temperature sums enhanced or counteracted
seasonal rainfalls, as they could affect soil moisture from winter through
fall: (Table 4): WNo, NH, D*C, NC, WH, DH*. Annuals
and most perennials responded in opposite ways. Winter rainfall was the second
highest value (136 mm) and was equitably distributed over 13 of 14 weeks. Species that were keyed to winter rainfall
were very successful: (1) Seven annuals had spring flowerings that ranked first
for all years. (2) Four annuals were present that had not been censused before,
two of which had not been recorded for the site (Table 14).
(3) Eleven annuals bloomed 2.2 wk earlier than on average (range 1-4 wk). (4)
Two early flowering species, the geophytes Cymopterus
and Dichelostema had their maximum spring Nt along with Dyssodia acerosa, which showed an ability to respond to
rainfall regard-less of season. Spring/summer perennials bloomed very poorly,
with five species failing to bloom, or almost so (Tables
11a, 11b). Consequently no census was made in
summer.
In 1996: The drought, very warm (D,H*) of fall 1995 continued as a stress into the (D,H) of winter 1996, and dry, warm (d,wm) of early spring, even more severe than 1994. The above average rainfall (W) of late spring was partly offset by the maximum temperature sum (W,H*), which gave way to a normal early summer. Shrubs (Larrea, Flourensia) and subshrubs (Dalea, Parthenium, Zinnia) showed leaf stress until they began to green-up at the end of early spring. Almost all species had negligible spring blooms in 1996, as in 1994. The years differed in the varied responses of summer flowerings (Table 11a). <