10.5061/DRYAD.SXKSN034Z
Lashley, Marcus
0000-0002-1086-7754
University of Florida
Chitwood, Michael
Oklahoma State University
Dykes, Jacob
Texas A&M University – Kingsville
DePerno, Christopher
North Carolina State University
Moorman, Christopher
North Carolina State University
Human-mediated trophic mismatch between fire, plants, and herbivores
Dryad
dataset
2022
fire phenology
trophic mismatch
nutrient pulse
Deer
plant nutrients
FOS: Biological sciences
2022-02-10T00:00:00Z
2022-02-10T00:00:00Z
en
https://doi.org/10.1111/ecog.06045
85632 bytes
5
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Trophic mismatches are commonly reported across a wide array of taxa and
can have important implications for species participating in the
interaction. However, to date, examples of trophic mismatch have centrally
focused on those induced by shifts in climate. Here we report on the
potential for humans to induce trophic mismatch by shifting the phenology
of fire. Globally, anthropogenic fire ignitions are phenologically
mismatched to that of historic lightning ignitions but the effects of this
phenological mismatch on trophic interactions are poorly understood. Using
fire records from 1980-2016 from the southeastern USA, a hotspot of
anthropogenic fire, we demonstrate that there is a temporal mismatch
between anthropogenic and lightning lit fires in this region. The peak of
anthropogenic ignitions (i.e., 45% during March and April) occurred 3
months earlier than the peak in lightning-ignited fires (i.e., 44%
occurred during June and July), a pattern consistent with reports from
several other regions and continents. We demonstrate with a field
experiment conducted at a nutrient-poor site in the southeastern U.S.,
that anthropogenic fire phenology shifts nutrient pulses in resprouting
plants so that they mismatch herbivore reproductive demands. Consequently,
plant nutrient quality in four commonly consumed forages was below the
threshold to meet lactation requirements. Neonates subsequently were more
likely to starve when born far from areas burned during the peak month of
lightning fire phenology. Our data indicate that human activities may be
an additional causative agent of trophic mismatch.
Regional fire data We downloaded wildland fire occurrence data for all
fires that occurred in 11 states in the southeastern United States for the
time period of 1980–2015 from the federal fire occurrence database
(https://wildfire.cr.usgs.gov/firehistory/data.html). The states chosen
included Alabama, Arkansas, Georgia, Florida, Louisiana, Mississippi,
North Carolina, South Carolina, Tennessee, Virginia and West Virginia. We
extracted all fires known to be caused by lightning and all fires that
were known to be prescribed, along with their respective ignition dates.
We used the start date listed for each fire to assign it to month of
ignition. We then plotted the average percentage with standard error
across years by month of anthropogenic and lightning ignited fires. Field
experiment study area We sampled forage quality and neonate survival at
Fort Bragg Military Installation (Fort Bragg), North Carolina, USA
(35.1°N, −79.2°W). The 73 469 ha area was located in the Sandhills
physiographic region of the longleaf pine Pinus palustris ecosystem. This
ecosystem and the species within evolved over millennia with relatively
frequent (3-year average), low-intensity surface fires occurring due to
lightning, and native American activities over the past 10 000–20 000
years (Outcalt 2000). For the purposes of restoring ecosystem function and
conserving endangered species, the United States Department of Defense has
managed forested stands on a 3-yr fire regime (Lashley et al. 2014b). The
parturition phenology of the local ungulate (i.e. white-tailed
deer Odocoileus virginianus) peaks in early June (Chitwood et al. 2015a).
Thus, because the peak nutritional demand occurs during lactation 3–6
weeks after parturition in white-tailed deer (Hewitt 2011), the peak
nutritional demand based on reproductive phenology of this herbivore in
this area is during July.In this study area, soil productivity is
particularly poor (Lashley et al. 2015b). Thus, deer may be sensitive to
shifts in resource pulse phenology. As evidence of this nutritional
burden, a relatively large portion of neonates starve on the site,
regardless of fire timing, as compared to similar studies in more
productive soil regions (Chitwood et al. 2014, 2015b). Likewise, diet
selection is relatively narrow and concentrated on obtaining exceedingly
limited quantities of phosphorus (Lashley et al. 2015b, 2016). Moreover,
predation risk is relatively high (Chitwood et al. 2014, 2015b, 2017) and
may limit female selection of the highest quality resources during
lactation (Lashley et al. 2015c). Importantly, this population does not
have access to anthropogenic subsidies as is common in other parts of
their range, so changes in starvation should be related to available
nutrition in the native plant community. Field experiment study design In
a randomized block design, we selected four upland longleaf pine forest
stands in each of 3 separate watersheds (blocks), averaging ~8 km apart,
with similar soil types (Candor Sands complex) and similar basal area
(45–60 m2 ha−1). We randomly assigned stands to each of four fire
phenologies relative to our plant sampling period (see next section):
fires ignited in June of the previous year (1 year-since-fire), and fires
ignited in the same year in February (i.e. early anthropogenic phenology),
April (i.e. late anthropogenic phenology) and June (lightning phenology).
The February fire phenology was meant to represent the onset of the
anthropogenic fire season (Brennan et al. 1998, Cox and Widener 2008). The
April fire phenology was intended to represent the end of the
anthropogenic fire season (Platt et al. 1988, Robbins and Myers
1992, Streng et al. 1993, Glitzenstein et al. 1995, Kirkman et al.
1998, Hiers et al. 2000, Knapp et al. 2009). The June fire phenology was
meant to represent the peak in lightning fires for this region (Knapp et
al. 2009). Each block contained a replicate from each treatment, and 1
year-since-fire was intended to be the control for comparison to fires
ignited in the same year because previous reports suggested that any
nutritional benefits would be lost after a single growing season (Dills
1970, Wood 1988, Carlson et al. 1993, Van de Vijver et al. 1999, Long et
al. 2008, Nichols et al. 2021). Plant sampling and analysis We selected 4
native plant species that occurred in every replicate of each fire
treatment. Because deer eat plants of several growth forms, we selected 2
trees, 1 shrub and 1 forb to ensure the plants represented responses
across functional groups. The trees collected were common
persimmon Diospyros virginiana and sassafras Sassafras albidum, the shrub
was dwarf huckleberry Gaylussacia umosa and the forb was fragrant
goldenrod Solidago odora. We selected these species because they are
common in the study area and commonly consumed by white-tailed deer
(Lashley et al. 2015b, 2016).In each month of the growing season (i.e.
May–September), we remotely established a plot center in each replicate of
each treatment using a geographic information system. We navigated to the
a priori selected plot center and collected the foliage of the nearest 10
plants of each species that were in the understory strata (i.e. <
1.5 m tall), separately bagging young leaves and the mature plant parts
not typically eaten by this herbivore (Lashley et al. 2014a). We used
previous data collected on site to determine that 10 plant samples was
robust to the expected variation in intraspecific plant nutritional value
(Lashley et al. 2015b). We separated physiologically mature and immature
plant parts because plant maturity affects quality, and we were interested
in how fire affects relative maturity of plant tissue, quality of young
leaves, as well as quality of the whole plant. We assumed that secondary
plant compounds were not significantly affecting nutritive quality based
on results presented in Jones et al. (2010) that demonstrated tannin
defensive compounds in forages consumed by herbivores in this region were
generally low. If a plant was discolored, malformed or damaged (by
herbivory or otherwise), we did not collect the plant tissues and instead
sampled the next nearest plant. To avoid biases associated with forage
handling, we followed the protocol presented by Lashley et al. (2014a) by
transporting samples within 3 hours to a convection oven and drying
forages to constant mass at 47°C. After drying samples, we measured weight
to the nearest 0.01 g of the young and mature plant parts and shipped
samples to the Clemson University Agricultural Service Laboratory, which
was certified by the United States National Forage Testing Association.The
lab performed a standard full nutrient array with chemical determination
methods to yield the percent of each sample of young and mature plant
parts that was crude protein (i.e. nitrogen × 6.25; CP), acid detergent
fiber (ADF) and neutral detergent fiber (NDF). For the same samples, we
obtained measurements for macro-nutrients phosphorus (P), potassium (K)
and calcium (Ca), and micro-nutrients magnesium (Mg), zinc (Zn), copper
(Cu), manganese (Mn), iron (Fe), sulfur (S) and sodium (Na). After
obtaining the nutritional parameters for physiologically young and mature
plant parts, we calculated the whole plant nutritional value by weighting
each sample by the relative proportion of physiologically young and mature
plant parts and their associated nutritional values.For the purposes of
understanding the effects of fire phenology mismatch on available
nutrition for white-tailed deer, we calculated the phosphorus requirements
of a lactating white-tailed deer conservatively based on the minimum
concentration needed to obtain adequate phosphorus assuming forage
abundance was not limiting maximum possible physiological intake. We used
this nutrient specifically because it was formerly deemed the limiting
nutrient on this study site (Lashley et al. 2015b). However, we recorded
the array of nutrients because it was part of a standard analysis at the
lab. We assumed a maximum daily intake for a 45 kg animal (i.e. average
adult female on site (Lashley et al. 2015b)) was 4.8% of the body weight
or 2.16 kg day−1 (dry matter), which is the reported physiologically
limited possible dry matter intake for female white-tailed deer during
peak lactation (National Research Council 2007). Our intention with this
calculation was simply to compare the forage quality in terms of
phosphorus availability in the plants following each respective fire
phenology to determine if those plants would meet the phosphorus
requirement for an average size female with one fawn in the study area. We
estimated the phosphorus concentration of the plants would need to be a
minimum of 0.025% for a lactating female which is consistent with previous
estimates (McEwen et al. 1957, Barnes et al. 1990). In JMP Pro 11.0 (SAS
Corporation, Cary North Carolina, USA), we fit general linear mixed models
with restricted maximum likelihood to evaluate the effects of fire
treatments on the proportion of biomass contributed by young leaves, the
nutritional quality of young leaves and the nutritional quality of the
whole plant. We included random effects of drainage (i.e. block) and plant
species to control for influences on nutritional quality not related to
fire. Influence of fire phenology on deer reproductive success To
determine the influence of fire phenology on deer reproductive success, we
radiotagged pregnant female white-tailed deer in winter to identify birth
site locations relative to burned areas on the landscape and measure the
subsequent survival of the neonates. Each female was fitted with a vaginal
implant transmitter (VIT) to aid in the discovery of birth sites and
hours-old neonates. We fitted each neonate with an expandable, breakaway
VHF collar that had a 4-hr motion-sensitive mortality switch. We monitored
neonates intensively (i.e. every 4–8 hrs) for the first month of life via
VHF and continued monitoring survival at reduced time intervals until
fawns reached 16 weeks (Chitwood et al. 2015a). Thus, survival of neonates
was our proxy for reproductive success in this study. When we detected a
mortality signal from the collar, we tracked to the collar to determine
cause of mortality using field evidence and, when predation was evident or
suspected, DNA swabs for residual predator saliva on the carcass and/or
radiotag (Chitwood et al. 2015a). We necropsied all carcasses to finalize
cause of mortality; individuals with no signs of predation that had lost
body mass since capture and had empty digestive tracts were classified as
starvation (Chitwood et al. 2015a). We used the birth site location of
each neonate to calculate a straight line distance to the nearest area
burned during the lightning season (i.e. June in the study area). This
allowed us to determine if proximity to areas burned in the lightning
season affected the likelihood of neonate starvation. Using a binary
logistic regression in JMP Pro 11.0, we used the straight line distance
from each birth site to the nearest area burned during the lightning
phenology to predict the probability of starvation. Our rationale for
using straight line distance to areas burned in lightning season was that
in this resource limited environment, which does not contain anthropogenic
subsidies or agriculture, the predicted pulse in available nutrients
following fire would serve as the highest quality foraging opportunity for
lactating females in this system and thus, serve as a primary means to
meet the demands of lactation (Chitwood et al. 2015a, 2017, Lashley et al.
2015b, Nichols et al. 2021). All protocols presented herein were approved
by the North Carolina Wildlife Resources Commission and the NCSU IACUC
(no. 10-143-O).