A POPULATION VIABILITY ANALYSIS OF THE LASSICS LUPINE (LUPINUS CONSTANCEI )
HUMBOLDT STATE UNIVERSITY
By
Helen Marie Kurkjian
A Thesis
Presented to
The Faculty of Humboldt State University
In Partial Fulfillment
Of the Requirements for the Degree
Master of Science in Biology
May 2012
A POPULATION VIABILITY ANALYSIS OF THE LASSICS LUPINE (LUPINUS CONSTANCEI )
HUMBOLDT STATE UNIVERSITY
By
Helen M. Kurkjian
"We certify that we have read this study and that it conforms to acceptable standards of scholarly presentation and is fully acceptable, in scope and quality, as a thesis for the degree of Master of Science."
Approved by the Master’s Thesis Committee:
______Dr. Erik S. Jules, Major Professor Date
______Dr. Borbala K. Mazzag, Committee Member Date
______Dr. Michael R. Mesler, Committee Member Date
______Dr. John O. Reiss, Committee Member Date
______Dr. Michael R. Mesler, Graduate Coordinator Date
______Dr. Jená Burges, Vice Provost and Dean of Graduate Studies Date
ABSTRACT
A population viability analysis of the Lassics lupine ( Lupinus constancei )
Helen M. Kurkjian
The Lassics lupine (Lupinus constancei T.W. Nelson & J.P. Nelson) is a rare herb of limited distribution, with fewer than 400 reproductive individuals restricted to a single square kilometer in the Lassics Geological and Botanical Area of the Six Rivers
National Forest, California, USA. In addition to the vulnerability resulting from its extremely small population size, the Lassics lupine faces heavy seed predation by small mammals and encroachment of surrounding plant communities into its preferred habitat.
As a stop-gap measure to prevent population decline, managers began covering a large number of the reproductive plants with herbivory exclosures in 2003, but the population- level effects of seed predation and the effectiveness of this caging treatment were unknown. In this study, I used ten years of demographic monitoring data collected by the
US Forest Service to build a stage-structured matrix model, project population growth, and estimate the probability of species extinction in the next 50 years. The model included vital rate estimates for each of three main sites, as well as vital rate correlations between and within sites and years. I used a one-way life table response experiment
(LTRE) to quantify the effects of caging on the population growth rate. Finally, I used a
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regression LTRE to estimate the proportion of the population at each site that must be caged to avoid population decline. I found that in the absence of seed predation, the
Lassics lupine population growth rate would be quite robust ( λs = 1.17), but without
continued human intervention (i.e., caging), the current rate of seed predation is projected
to drive the population to extinction ( λs = 0.92). Remarkably, the LTREs revealed that in
order to best forestall population decline, plants should be caged at roughly the same
proportions as they are under current management practices. Early identification by
managers of the role of seed predation in Lassics lupine population decline was likely
instrumental in the prevention of a substantial reduction in population size. The success
of the current efforts can only be considered a deferment, however, and without further
research into the underlying causes of this untenable seed predation rate, the Lassics
lupine could be unable to recover.
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ACKNOWLEDGEMENTS
This project would not have been possible without the large and robust Lassics lupine monitoring dataset provided by the US Forest Service. Ten-year datasets on plants are unusual, because their collection requires diligent planning, assiduous data management, commitment to scientific rigor, and passion for the conservation of our flora. Everyone involved in the collection of this data deserves a great deal of thanks, especially Lisa Hoover of the US Forest Service, Dave Imper and Gary Falxa of the US
Fish and Wildlife Service, and Sydney Carothers of Humboldt State University.
Dr. Erik Jules provided indispensable guidance on my thesis, education, and career. When I advise grad students of my own, he will be the standard to which I hold myself. April Sahara and Elizabeth Wu offered research advice, emotional support, a scientific sounding board, and a damn good time, in the lab and out. It was a pleasure to work with people that I admire both personally and professionally. This was the finest lab anyone could ask for, and I’m so proud to have you all as my friends and colleagues.
My committee members, Drs. Bori Mazzag, Michael Mesler, and John Reiss, supplied thoughtful feedback and assistance on my research and writing. Dr. Robert Van
Kirk was extremely generous with his time and statistical knowledge. Many thanks to
Jade Paget-Seekins, Glenn Shelton, Alisa Muniz, Francine Arroyo, Max Cannon, and
Maryann Snake for their help in the field. Financial support for this project came from the US Forest Service, the US Fish and Wildlife Service, the California Native Plant
Society, and the Humboldt State University Department of Biological Sciences.
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TABLE OF CONTENTS
ABSTRACT.……………………………………………………………………………..iii
ACKNOWLEDGEMENTS……………………………………………………………….v
TABLE OF CONTENTS…………………………………………………………………vi
LIST OF TABLES……………………………………………………………………….vii
LIST OF FIGURES……………………………………………………………………..viii
LIST OF APPENDICES………………………………………………………………….ix
INTRODUCTION………………………………………………………………………...1
METHODS
Species Description & Study Area…………………….………………………….5
Monitoring…………………………………………………………………..…….6
Seed Studies……………………………………………………………………….7
Population Projections…………………………………………………………...10
Life Table Response Experiments……………………………………………….12
RESULTS
Seed Studies……………………………………………………………………...14
Population Projections………………………………………………………...... 15
Life Table Response Experiments……………………………………………….17
DISCUSSION……………………………………………………………………………19
LITERATURE CITED…………………………………………………………………..26
APPENDICES…………………………………………………………………………...41
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LIST OF TABLES
TABLE PAGE
1 Projection matrix for a single Lassics lupine site, where sj is the probability of the survival of class j for one time step, sss is survival of seedlings over the summer, gij is the probability of transition from class j to class i in one time step, g>ij is the probability of transition from class j to any class i or greater, and fj is the mean number of seeds produced by class j……………………………………………………………………………..32
2 Mean vital rate values (±variance) at all three Lassics lupine monitoring sites. Caged and uncaged values shown where appropriate. All reproductive plants at the Red site were caged……………………………….….33
3 Elasticity matrices for each Lassics lupine monitoring site, evaluated under a scenario where all plants were caged……….……………..…………….34
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LIST OF FIGURES
FIGURE PAGE
1 A reproductive Lassics lupine individual beginning to fruit inside an herbivory exclosure (top). View from Mount Lassic main peak looking eastward toward the saddle and secondary peak, with Red Lassic in right background (bottom)………………………………………………………….….35
2 Full extent of Lassics lupine range, with monitoring sites outlined in blue, green, and red. Inset shows northern California, USA….……………..………..36
3 Spearman rank correlation coefficients for vital rate values between each pair of Lassics lupine monitoring sites, within years…………….………………37
4 Results of population projections for all Lassics lupine monitoring sites combined (row 1), the Forest site (row 2), Red site (row 3), and the Saddle site (row 4), under three caging scenarios: all plants caged (left column), no plants caged (middle), and plants caged at the actual current rate (right; see text for details). The cumulative probability of quasi- extinction for adult plants is shown in black (dashed line, 95% confidence interval), while that of the seed bank is shown in gray. All Red site plants are currently caged. Each panel includes the mean stochastic population growth rate for that projection…………………………………………………...38
5 Results of one-way LTRE, showing contribution to changes in the Lassics lupine population growth rate by each vital rate due to caging………………….39
6 Results of a regression life table response experiment (LTRE), showing the response of Lassics lupine population growth rate ( λ), reproductive value ( v), and stable stage distribution ( w) at each of the three monitoring sites, across a gradient of caging of reproductive plants………….………..……40
viii
LIST OF APPENDICES
APPENDIX PAGE
1 Linear regression forced through the origin, using number of seeds as the response and number of inflorescences as the predictor………………………....41
2 Results of t-tests comparing number of inflorescences and rosette diameter between caged/uncaged and bagged/unbagged plants in 2010 and 2011……..…42
3 AIC c table for 20 power models, using number of seeds produced per reproductive plant as the response and the parameters shown plus number of inflorescences as predictors……………………………………………………...43
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1
INTRODUCTION
The Lassics lupine (Lupinus constancei T.W. Nelson & J.P. Nelson) is a rare herb of extremely limited distribution and small population size (Nelson and Nelson
1983; Figure 1). All known Lassics lupine individuals are found within a single square kilometer in northern California, USA and the total population includes less than 400 reproductive plants (Carothers 2008; Figure 2). The circumscribed range of this species makes it extremely vulnerable to both catastrophic disturbance and slower successional changes that might otherwise be mitigated by larger spatial distributions. Despite its extreme rarity, the Lassics lupine is on neither state nor federal threatened or endangered species lists. It is, however, designated California Rare Plant Rank 1B.2 by the
California Native Plant Society (CNPS), meaning that it is “rare, threatened, or endangered in California and elsewhere” and therefore afforded some protection from unnecessary anthropogenic disturbance by the California Environmental Quality Act
(CNPS 2012).
Following the Lassics lupine’s discovery to science in 1979 (Nelson 1979), there were reports from managers that its population appeared to be declining precipitously (D.
Imper, personal communication ). Because a decline in Lassics lupine population size could mean that the species is moving precariously close to extinction, an inter-agency group comprised of US Forest Service (USFS), US Fish and Wildlife Service (USFWS), and CNPS biologists began monitoring the Lassics lupine in 2001, a project that continues today. Pressures from herbivory and seed predation are of particular concern to
2
the persistence and demography of the Lassics lupine. Deer, hares, and insects are
known to consume the lupine’s vegetative and floral parts, which can lead to mortality
and/or reduced reproductive output. Small mammals, probably deer mice (Peromyscus maniculatus ) and brush mice (Peromyscus boylii ), are known to remove seeds from the lupine prior to dispersal (Falxa and Cline 2007), and the managers best acquainted with the species have believed for years that seed predation could be driving Lassics lupine population decline. In 2003, following reports of predation of nearly all seeds in previous years, biologists from the inter-agency group began a program of covering a portion of reproductive individuals with herbivory exclosures during each growing season. This was intended as a stop-gap measure to stem population decline, and its effectiveness had not been evaluated.
Damage to individual plants by herbivores has been recorded across a wide range of plant taxa and many of the impacts of herbivory on survivorship, growth, and fecundity are well-known (Belsky 1986, Andersen 1987, Mutikainen and Delph 1996).
Yet surprisingly, the population-level effects of herbivory can be much more difficult to document. How a reduction in fitness resulting from the removal of flowers, fruits, seeds, or vegetative parts translates into altered population growth is poorly understood
(Maron and Crone 2006). In particular, the effects of pre-dispersal seed predation on population growth have rarely been documented (but see Louda 1983, Louda et al. 1990,
Kolb et al. 2007, Dangremond et al. 2010), and they could be harder to detect in a plant species with a long-lived seed bank, because the loss of one year’s seed production can be buffered by recruitment from the seed bank (Maron and Simms 1997, Maron and
3
Kauffman 2006). Due to the ubiquity of seed predation on the Lassics lupine, however, elucidation of its effects on population dynamics is necessary for formulating conservation approaches to increase population growth.
Management strategies aimed at increasing population growth are often most effective when they rely on empirical research for focus and direction (Crowder et al.
1994, Farrington et al. 2009, Knight et al. 2009). When the techniques used to provide that direction have a demographic cornerstone, they are generally termed population viability analysis (PVA). With some estimates that as many as 50% of all species could go extinct in the next 50 years (Pimm and Raven 2000) and the limited budgets available to conservation biologists, this type of goal-oriented science often has only one opportunity to ask the right questions and respond with appropriate methods. PVAs often share a common purpose: to assess the risk of extinction of imperiled species.
Considering the magnitude of this objective, it makes sense that PVAs are not a monolithic instrument, but a collection of customizable tools. Nearly two decades ago,
Schemske et al. (1994) outlined the information that conservation biologists should attempt to gather about endangered species: (1) is the species growing, stable, or declining? (2) which life history stages have the greatest effect on population growth? and (3) which of the biological forces affecting those life history stages have the greatest impact? Over the intervening decades, there has been mounting criticism of the ability of
PVAs to provide specific information about a population’s projected size, growth rate, or extinction risk, because the confidence intervals around these measures are sometimes so large that they are predictively useless (Beissinger and Westphal 1998, Ludwig 1999, but
4 see Brook et al. 2000, Coulson et al. 2001). In response to these criticisms, conventional wisdom has largely shifted to recommendations that PVA should be used for the relative comparison of management strategies rather than absolute measures of viability or extinction risk (Beissinger and Westphal 1998, Fieberg and Ellner 2000, Patterson and
Murray 2008). Managers, however, are left with the vexing task of assessing the status of a species, including its risk of extinction, in order to prioritize the expenditure of their financial and logistical resources.
In this study, I addressed a series of questions that are the foundation of established PVA methods, but also incorporated techniques adapted to the unique conservation concerns of the Lassics lupine. First, I used stochastic vital rate-based matrix models to address whether the Lassics lupine population is growing, stable, or declining and to estimate the likelihood that the species will go extinct in a relatively short time period (i.e., the next 50 years). These analyses were performed in a framework of comparative management scenarios, in order to contend with concerns regarding the inherent uncertainty in model projections. In addition, I used sensitivity and elasticity analyses and life table response experiments (LTRE) to determine which life history stages and vital rates have the greatest effect on the population growth rate.
Finally, I used a regression LTRE to evaluate the effect of seed predation on the population growth rate and determine the proportion of plants that should be caged at each site in order to avoid population decline.
5
METHODS
Species Description & Study Site
The Lassics lupine is a polycarpic, herbaceous perennial (Figure 1). Its basal
rosette of gray-green, hairy, long-petiolate leaves sits on a branched, woody caudex and
rarely exceeds 30 cm in diameter. As early as late May, reproductive individuals bear
one to 15 inflorescences of 10-60 perfect pink and white flowers, which are principally
pollinated by three species of bee (Crawford and Ross 2003). Each legume can
eventually mature 1-5 seeds in July or August. Ballistic seed dispersal can launch seeds
as far as four meters. Seeds often exhibit physical dormancy (Baskin and Baskin 1998,
Baskin et al. 2000) and form a persistent seed bank, though precisely how long seeds can
survive in the soil is unknown. In addition to its preference for serpentine soils, the
Lassics lupine is suspected to form specific symbiotic relationships with the soil microbes
of the Lassics (Guerrant 2007).
All known Lassics lupine individuals are found on two mountaintops in the North
Coast Ranges of California, USA (Figure 2). This range spans the Humboldt-Trinity
County border, with one occurrence on each mountain. The first occurrence wraps around the north slope of the two peaks of Mount Lassic and across the saddle between them. The tops of these peaks are mostly bare of woody vegetation, while at lower elevations the south slope is principally covered by chaparral and the north slope by
Jeffrey pine/incense-cedar forest (Figure 1). Historical photo analysis indicates that over
6 the last 80 years the forest has encroached upon the bald habitat preferred by the Lassics lupine (Carothers 2008). The second occurrence is on the neighboring peak of Red
Lassic and is completely covered by Jeffrey pine/incense-cedar forest. This occurrence contains only about 15% of the individuals in the species. Both occurrences are found within the Lassics Geological and Botanical Area of the Six Rivers National Forest, which is managed by the USFS and is partially within a designated wilderness area.
Monitoring
The backbone of this PVA was an extensive monitoring dataset collected jointly by the USFS, CNPS, and the USFWS. Permanent monitoring transects were established at three sites and representatives of every Lassics lupine life history stage were marked.
The Saddle monitoring site was established in 2001 on the saddle between the two peaks of Mount Lassic, and has very little canopy cover. In the same year, the Red site was established on the north side of Red Lassic, under a closed forest canopy. The Forest monitoring site, established in 2006, is downslope of the Saddle site within the forest boundary and is mostly covered by closed-canopy Jeffrey Pine. Three times per year, in approximately June, July, and August, each individual was re-visited and its stage class, rosette diameter, level and source of herbivory sustained, and number of inflorescences were recorded. Stage classes were dictated entirely by life history: seed, seedling, vegetative adult, reproductive adult, and dormant adult. Seedling, vegetative, and reproductive classes were directly observable in the field. Individuals were retroactively
7
classed as dormant if they were observed alive after one or more years presumed dead.
Between 2001 and 2011, 6242 total observations were made of 940 individuals.
Seed Studies
In order to estimate key vital rates that could not be calculated from the
monitoring data, I undertook several seed studies. The objectives of these studies were:
(1) to estimate the number of seeds produced by reproductive plants in years past, (2) to
gain insight into the factors (internal and external) that most influence the number of
seeds produced by an individual plant, (3) to estimate the effect of seed predation on
fertility, and (4) to estimate the survival of seeds in the seed bank and the rate at which
seeds germinate from the seed bank.
In 2010 and 2011, I took advantage of the management policy of covering a portion of reproductive plants with herbivory exclosures during the growing season.
These cages are made of 1-3 cm wire mesh and are capable of excluding most birds and mammals, but not insects (Figure 1). Approximately 30% of Saddle plants, 50% of
Forest plants, and 100% of Red plants are caged each year. I marked a randomly-chosen subset of caged (2010 n = 57, 2011 n = 35) and uncaged (2010 n = 64, 2011 n = 34) plants. I covered all inflorescences found on a portion (2010 n = 41, 2011 n = 19) of the marked caged plants with plastic mesh bags after pollination and before fruit maturation.
After the fruits of the bagged inflorescences matured, I counted the number of seeds produced. For each plant, I also measured morphometric characteristics (number of
8 inflorescences, number of leaves, rosette diameter) and microsite characteristics (slope, aspect, litter depth, proximity to forest and chaparral edges). To evaluate any bias toward caging or bagging larger plants, I performed t-tests comparing the rosette diameter and number of inflorescences of caged and uncaged plants and bagged and unbagged caged plants.
To estimate the mean number of seeds produced by reproductive plants in years past, I performed a linear regression using number of seeds produced per plant as the response and number of inflorescences as the predictor. I forced the model through the origin, in order to take into account the fact that all reproductive individuals produced seeds. I estimated the model parameters using the 2010 data and tested them using the
2011 data by building prediction intervals around the model fits of the actual 2011 measurements. I used this model to estimate the annual seed production of all reproductive plants observed during the monitoring effort.
To discern which morphometric and microsite characteristics best predicted the number of seeds produced by reproductive plants, I used Akaike’s Information Criterion corrected for small sample size (AIC c) to evaluate a set of models, which included twenty power models and used number of seeds produced per plant as the response variable. All models included number of inflorescences as a continuous predictor and one, two, or three of the following additional predictors: rosette diameter, slope, aspect, distance to forest edge, distance to chaparral edge, and distance to nearest tree, shrub, or rock.
Aspect measurements were transformed according to the methods of Beers et al. (1966), such that A’ = sin(A+45) + 1 , where A’ is the transformed aspect and A is the
9
untransformed aspect measured in degrees clockwise from north. I used the models with
lowest AIC c values in 2010 to choose a reduced set of models to test in 2011. All AIC c
values were calculated using the AICcmodavg package in R (Mazerolle 2011).
To estimate the effect of seed predation on fertility, I counted the number of fruits matured by each marked uncaged plant and multiplied that by the mean number of seeds per fruit in caged plants. Then, I used the linear model described above to estimate the number of seeds each uncaged plant would be expected to produce in the absence of seed predation. Finally, I calculated the number of seeds matured by each plant as a proportion of that plant’s estimated total production and found the mean across all marked plants. Unfortunately, in both study years, too few marked uncaged plants escaped seed predation to perform any meaningful analysis relating seed predation to plant morphometric or microsite characteristics.
In addition, I used data from seed burial experiments performed by agency biologists to calculate seed survival rates. In 2008, five mesh bags containing ten seeds and 5 mL of soil each were buried at seven plots in the Saddle site. In 2009, 2010, and
2011, one bag was pulled up from each site, the number of germinants was counted, and intact seeds were tested for viability using a 1% dilution of tetrazolium chloride
(Carothers, unpublished ). I performed a linear regression with number of germinants and viable seeds in year t as the response and number of germinants and viable seeds in year t-1 as the predictor, and used the slope coefficient and variance estimates as the mean and variance of seed bank survivorship ( s1).
10
Finally, to calculate seed germination rates, I used data collected from a series of seed grid experiments. In 2005, agency biologists sowed 435 seeds in 27 grids at known locations across the Saddle and Red sites and recorded the number of germinants at these locations in the following six years. Seven similar plots were sown in 2008 and 2009 with 24 seeds each. I calculated a mean and variance of proportion annual germination between these plots and used their reciprocals as the mean and variance of the probability that seeds in the seed bank do not germinate ( g11 ).
Population Projections
I used a stage-structured, vital rate-based matrix model to project the population size 50 years in the future under three seed predation scenarios: all plants caged, all plants uncaged, and the actual caging rate. In these scenarios, uncaged plants matured a constant percentage of their seeds. Each of the three sites was projected individually using a 5 x 5 matrix (Table 1), while the population size of the whole species was projected using three copies of the single-site matrix linked by between-site correlations
(Figure 3). This multi-site matrix incorporated no movement between sites, an assumption suggested by site topography and genetic analyses (Wilson and Hipkins
2004).
Each growth and survival rate for the seedling, vegetative, and reproductive classes was drawn from an appropriate beta distribution, with a mean and variance (Table
2) calculated from annual transitions using the Kendall function of the popbio R package
11
(Stubben and Milligan 2007), which uses the Kendall correction for sampling variation
(Kendall 1998, Morris and Doak 2002). The growth rates of the dormant class were drawn according to the same procedure, but survival of the dormant class was set equal to that of the vegetative class, because it could not be estimated directly. The fertility rate was drawn from a stretched beta distribution with a mean, variance, minimum, and maximum from the individual seed production estimates calculated with the seed production model discussed above. Fertility rates were corrected for sampling bias according to the methods of Engen et al. (1998).
All projections incorporated within-year vital rate correlations and auto-and cross- correlations for one time step across all 50 projection years. Demographic stochasticity was simulated using Monte Carlo methods for any stage class with less than 50 members at a given time step. I performed 500 sets of 1000 runs apiece for each site in each scenario. I set the quasi-extinction threshold at 10 adult (vegetative and reproductive) plants or 30 seeds across for the whole species and three adults or 10 seeds at a single site. The cumulative probability of quasi-extinction at each time step was calculated by tallying the number of runs in which the population fell below the quasi-extinction threshold at that time step and adding it to the tally from all previous time steps. A mean stochastic population growth rate ( λs) for each scenario was calculated from all runs, using the formula λs = (N 50 /N 0) ^ (1/50) , where N50 is the population size at time step 50
and N0 is the starting population size. Finally, to ascertain how sensitive the model was to individual parameter changes, I used the eigen.analysis function of the popbio R
12 package (Stubben and Milligan 2007) to calculate the sensitivity and elasticity of the model to each of the matrix elements at each site.
Life Table Response Experiments
To quantify the effect of the caging treatment on the population growth rate, I performed a fixed one-way life table response experiment (LTRE) using eight years of data from the Saddle site. This method decomposes a treatment’s effects on the deterministic population growth rate ( λd, the dominant eigenvalue of the mean projection matrix) into the effects mediated by each vital rate (Caswell 2001). The Saddle was the only site with sufficient data from both caged and uncaged plants to perform this analysis. First, I calculated the difference between the mean vital rates of caged and uncaged Saddle plants. Then, I calculated the sensitivity to each vital rate using an intermediate matrix built using the mean of the caged and uncaged vital rates. The contribution of a given vital rate to the change in population growth is the difference between caged and uncaged rates multiplied by its intermediate sensitivity.
To further examine the effects of seed predation on the deterministic population growth rate (λd), I performed a regression LTRE (Caswell 2001) using the proportion of
reproductive plants caged as the treatment variable. In this LTRE I assumed that caging
affected only fertility, by allowing all uncaged plants to mature only 5% of their fruits. I
calculated 21 projection matrices for each site, varying the percent of plants caged by 5%
increments from 0% to 100%. I calculated the deterministic population growth rate (λd),
13 stable stage distribution (w), and reproductive values (v) for each level of caging.
Finally, I determined the proportion of reproductive plants caged at which the population growth rate would be equal to one at each site. All analyses were conducted in R (R
Development Core Team 2011), loosely following the projection methods of Morris and
Doak (2002) and the LTRE methods of Caswell (2001).
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RESULTS
Seed Studies
I found a strong relationship between the number of inflorescences and the
2 number of seeds produced by individual plants using a linear model ( R adj = 0.8567, df =
1, p = <0.001). Therefore, I used the equation
seeds = 14.57 * inflorescences (1)
to estimate the number of seeds produced by reproductive plants in years past (Appendix
1). All 2011 data point fits fell within the prediction intervals calculated with this model.
No size bias in caging or bagging was revealed by t-tests using number of inflorescences
or rosette diameter as the response (Appendix 2).
AIC c analysis of power models revealed that the model using number of
inflorescences (I), rosette diameter (L), distance to the forest (F), and transformed aspect
(A), represented by the formula
seeds = 4.94 * I 0.612 * e0.069L *F 0.048 * e -0.195A (2)
had 97% of the model weight, whereas the model using number of inflorescences, rosette diameter, distance to the forest, and distance to the chaparral had 3% of the model weight, and no other models had any weight (Appendix 3).
In both study years, too few marked uncaged plants escaped seed predation to perform any meaningful analyses relating predation to plant morphometric or microsite characteristics. I estimated that approximately 2% of seed production escaped predation
15 in 2010 and 5% of estimated production escaped predation in 2011. Because seed predation in 2011 was qualitatively judged to be unusually light by the agency biologists who have worked with the Lassics lupine for the past decade, I chose to use that year’s seed predation rate as the estimate of the rate that uncaged individuals would sustain.
Therefore, the estimates of fertility of uncaged plants used in the population projections below can be considered conservatively high.
2 The seed burial linear regression ( R adj = 0.9483, df = 1, p = 0.100) yielded a slope coefficient with mean 0.70 and variance 0.05, which I used for the mean and variance of s1 in my population projections (Table 2). The mean (0.94) and variance (0.01) of the reciprocal of the proportion of seeds germinated in the seed grid experiments were used for g11 values in the population projections (Table 2).
Population Projections
Population projections showed that if current caging practices continue, the
Lassics lupine population will remain relatively stable ( λs = 1.001 ± 0.003), with a 0.7 to
31.5% probability (95% confidence) of quasi-extinction in the next 50 years (Figure 4).
Of the three sites, the Saddle has the best chance of surviving the next 50 years (0.2 to
31.7% probability of quasi-extinction, λs = 0.991 ± 0.003), if the current practice of
caging ~30% of its reproductive individuals is continued. The Red site has a slightly
greater risk of quasi-extinction (6.4 to 51.9%), despite the fact that 100% of its
reproductive individuals are caged every year and its population growth rate is slightly
16 higher ( λs = 1.002 ± 0.002). Even with 50% of its reproductive individuals caged, the
Forest site has a 56.8 to 100% probability of going extinct in the 50 years (λs = 0.947 ±
0.001).
In contrast, if all reproductive plants were left uncaged and exposed to a constant
95% seed predation rate, the probability of quasi-extinction in the next 50 years is 68.4 to
100% ( λs = 0.917 ± 0.001) across all sites. The probability of quasi-extinction under this scenario is 65.8 to 100% ( λs = 0.908 ± 0.001) at the Saddle, 69.8 to 100% ( λs = 0.928 ±
0.002) at the Forest, and 75.5 to 100% ( λs = 0.924 ± 0.001) at the Red site.
If all reproductive plants were caged, and were therefore totally protected from
seed predation, the probability of quasi-extinction in the next 50 years across all sites
drops to 0 to 1.8% ( λs = 1.168 ± 0.005). The probability of quasi-extinction under this
scenario is 0 to 1.3% ( λs = 1.173 ± 0.004) at the Saddle and 0 to 19.7% ( λs = 1.054 ±
0.003) at the Forest site. Because, all reproductive plants at the Red site are caged every year, its population growth rate and probability of quasi-extinction are the same as those for the current practice (above).
At all sites, the reproductive class had most of the model’s elasticity (Forest
35.4%, Red 34.5%, and Saddle 40.6%), followed by the vegetative class (Forest 28.6%,
Red 25.3%, Saddle 16.5%) or seed bank (Forest 22.0%, Red 25.5%, Saddle 23.8%), then seedlings (Forest 12.6%, Red 14.0%, Saddle 18.3%), with the dormant class influencing the model least at all sites (Forest 1.3%, Red 0.8%, Saddle 0.8%; Table 3). With the exception of s 2, s 4, and g >55 , most between-site, within-year vital rate correlations were positive or close to zero (Figure 3).
17
Life Table Response Experiments
The one-way LTRE showed that the difference in f4 (fertility) values between caged and uncaged plants contributed almost nine times more to the effect of caging on
Saddle population growth than changes in any other vital rate (Figure 5). Survival of reproductive plants ( s4), the probability that reproductive plants remain reproductive or go dormant ( g>44 ), and the probability that reproductive plants go dormant ( g>54 ) all made smaller contributions to the effect of caging on population growth.
The regression LTRE showed that as a greater proportion of reproductive plants were caged and the population sustained lower seed predation rates, the population growth rate increased at all sites (Figure 6). At the Forest site, the deterministic population growth rate (λd) increased from 0.854 when no plants are caged to 1.079 when all plants are caged, reaching stability ( λd = 1) when approximately 49% of plants are
caged. At the Saddle, the population growth rate ranges from 0.852 to 1.239, reaching
stability when 21% plants are caged. At the Red site, the population growth rate ranges
from 0.785 to 1.046, and reaches stability when 70% of plants are caged.
The reproductive values (v) and stable stage distributions (w) also changed in response to changing fertility resulting from different caging levels. At the Forest site, a reproductive individual would be expected to contribute 60.5 times as much as a seed to population growth over the course of its lifetime when all plants are caged, while when no plants are caged, it would contribute only 13 times as much. The contributions of
18 seedling, vegetative, and dormant individuals would also be lower if all plants were left uncaged. Seeds, seedlings, vegetative, reproductive, and dormant individuals would ultimately make up 87.1, 5.0, 5.3, 2.3, and 0.3% of the population, respectively, with all plants caged and 59.7, 3.6, 20.0, 14.8, and 1.8% with no plants caged. Although numerically different, changes to reproductive values and stable stage distributions at the
Red and Saddle sites would be qualitatively similar across a caging gradient.
19
DISCUSSION
These results show that the Lassics lupine faces a significant threat of predation- driven population decline over the coming decades. In the absence of the caging effort, the Lassics lupine population would be declining towards extinction as a direct result of high levels of seed predation. With a caging treatment, however, the Lassics lupine is able to maintain population growth rates that result in lower extinction risk. It is remarkable that based on keen observation and knowledge of the system, managers were able to identify an underlying cause of population decline and counter it with perhaps the best possible response. Early identification of the role of seed predation in Lassics lupine population dynamics was likely instrumental in the prevention of a substantial reduction in population size. Nonetheless, at the current rate of caging the Lassics lupine still has a significant chance of extinction; current caging results in λs ≈ 1, where the chance of reaching quasi-extinction in the next 50 years is as high as 31.5%. Of course, the greater the proportion of plants caged, the lower that probability drops, but it is not feasible financially or logistically to try to cage all plants in all years. Even if managers did undertake such a cumbersome task, it certainly could not continue indefinitely. Thus, while the current intervention is effective and necessary, there is a strong need to find a more lasting approach to maintaining the Lassics lupine.
PVA has a long history of use for the examination of plant population dynamics
(Menges 1990, Schemske et al. 1994, Morris and Doak 2002), the potential efficacy of management strategies (Emery and Gross 2005, Griffith and Forseth 2005, Farrington et
20 al. 2009), and the identification of the life history stages that most influence population growth (Caswell 2000). Its use in the study of how community interactions mediate the connection between individual fitness and population demographics, however, is much less common (Maron and Kauffman 2006). PVA has been used to examine the effects of a reduction in individual fitness on population-level processes due to pollination limitation (Knight 2004), harvest by humans (Farrington et al. 2009), and herbivory by deer (Knight et al. 2009), rodents (Dangremond et al. 2010), insects (Rose et al. 2005), and livestock (Gomez 2005). Seed predation by insects and rodents, both pre- and post- dispersal, has also been observed to affect plant population growth (Louda et al. 1990,
Maron and Simms 2001, Dangremond et al. 2010).
Rarely, however, have the effects of seed predation been found to be so great as to transition a plant population from growth to decline, as they do in the case of the Lassics lupine. Long-lived species are generally insensitive to changes in fertility and far more sensitive to changes in growth and survivorship of adult individuals (Silvertown et al.
1993, Knight et al. 2009). With the exception of the dormant class, the elasticity of my model was spread fairly evenly across the stage classes, which is unusual and provides a partial explanation of why seed predation has such a dramatic effect in this system. As fewer plants are caged and the population sustains more seed predation, reproductive individuals make up proportionally more of the population and seeds less. At the same time, reproductive plants undergo a decrease in their reproductive value and their contribution to population growth becomes closer to that of a seed. High seed predation
21 leads to a Lassics lupine population with a reproductive class that is disproportionately large, but of diminished value to population growth.
The mechanics of Lassics lupine demographics, however, do not provide any insight into why seed predation is having this effect now, but did not in the past. Was the species reaching the end of a long decline when it was discovered to science in 1979? Or have its range and population size always been small and seed predation is a more recent phenomenon? Given the brief window for which we have data, it is doubtful that this particular question can ever be answered with certainty. Nevertheless, it seems unlikely that the Lassics lupine was more widespread in the recent past than it is now.
Comparison of soil from areas inhabited by the Lassics lupine with nearby similar areas where it does not grow found very few sites that met the lupine’s soil requirements and were not already occupied (D. Imper, unpublished data ). This indicates that the species current and historic ranges are comparably-sized. So, if the Lassics lupine has existed as a small but stable population, what caused the recent intensification of seed predation?
The answer may lie in the vegetation changes that have taken place on Mount
Lassic over the past several decades. Analysis of historical photos dating as far back as the 1930s indicates that the edge of the Jeffrey pine/incense-cedar forest has moved upslope markedly on the north side of Mount Lassic, while the chaparral edge has made a similar move upslope on the south side (Carothers 2008). The small mammals that consume Lassics lupine seeds are unlikely to live exclusively in the bald areas where the plant grows, because they need cover for dens and other shelter, and to avoid being predated themselves. While scattered cover exists in some lupine areas in the form of
22 shrubs, trees, and large rocks, both cover and small mammal abundance are generally higher in nearby chaparral and forest areas (G. Falxa, personal communication ). The closer the rodents’ preferred habitat gets to that of the lupine, the more likely that they will risk venturing into an unprotected bald area to take advantage of the large and plentiful fruit. This hypothesis is supported by a preliminary study that I conducted in
2010, in which I used transects originating in bald areas occupied by lupines and running into the surrounding vegetation (forest and chaparral) to measure removal rates of seeds placed in trays at regular intervals. I found that the seeds were removed more frequently within and just outside of the forest and chaparral boundaries (Kurkjian 2011).
Unfortunately, due to small sample size and unforeseen data collection complications, I was unable to draw a conclusive link between seed predation intensity and the proximity of surrounding vegetation, but it did indicate further study into this relationship is warranted.
A number of studies have shown that seed predation intensity and its effects on
plant population demographics differ between habitats. Maron and Kauffmann (2006)
demonstrated that Lupinus arboreus underwent significantly higher post-dispersal seed
predation by mice in dune habitats than in grasslands, and that this disparity led to
markedly different population-level effects. In a forest ecosystem, Kolb et al. (2007)
found that while pre-dispersal seed predation by insects on Primula veris increased over a
gradient of canopy cover, the effects of predation on the population growth rate actually
decreased over the same gradient. And Dangremond et al. (2010) determined that
proximity to an invasive grass in a dune habitat increased pre-dispersal seed predation by
23 rodents on Lupinus tidestromii , thereby causing population decline. Determining the underlying causes of the timing and intensity of seed predation on the Lassics lupine will be essential to preventing its demise.
While PVAs have clear and demonstrated value in species management, the term
‘viability’ is somewhat abstract and the question of what makes a species viable is the subject of philosophical debate (Ginzburg et al. 1982, Morris and Doak 2002). My study demonstrates that even the more concrete question of whether a population’s growth is stable can be less straightforward than at first it seems. The Lassics lupine population appears to be close to stability at the current rate of caging, but if it were left totally uncaged it is predicted to decline precipitously towards extinction, while it would grow quite robustly if all plants were caged (Figure 4). Is the Lassics lupine population growing, stable, or declining? Depending on the level of human intervention, my model predicts that it could be any of the three. Further complicating the question of population status is the fact that this model did not incorporate extreme variability in the vital rates.
While climatic conditions during the ten study years did include some severe weather, there is no reason to believe this timespan captured the most extreme conditions we might expect in the coming decades. The summer survival of Lassics lupine seedlings may be hit especially hard by climate change, as that vital rate seems to be the most closely correlated with ambient air temperature (D. Imper, unpublished data ). Since the seedling class holds between 12.6 and 18.3% of model elasticity, increased seedling mortality could have a dramatic effect on population persistence.
24
Finally, when considering the vulnerability of the Lassics lupine population to extinction, the small absolute size of the population cannot be ignored. There are fewer than 400 reproductive plants total, occupying an extremely small area. A single catastrophic event could eradicate the species en masse. This, more than any model behavior, indicates that the species is at risk. The Lassics lupine is not the only rare plant in need of active management to ensure its persistence. Hypericum cumulicola and
Dicerandera frutescens ssp. frutescens , for example, are endemic to the scrub habitats of
Florida and rely on frequent prescribed fire to maintain their population sizes (Quintana-
Ascencio et al. 2003, Evans et al. 2010). Population growth of the rare wetland plant
Aeschynomene virginica of the eastern coastal United States is encouraged using treatments that remove interspecific vegetation (Griffith and Forseth 2005). Lepidium papilliferum , a rare desert annual, depends on the inter-annual variability in precipitation of its native habitat to survive, but may be unable to persist without the prevention of trampling by livestock (Meyer et al. 2006). And a study of the North Carolina shrub
Hudsonia montana showed that it must be managed with both prescribed fire and reduced trampling in order to thrive (Gross et al. 1998).
This study shows that with an appropriate management approach, the Lassics lupine is not doomed. The caging treatment already in place provides valuable support to population stability, but it is not enough. An effective strategy should include caging in the short-term while long-term measures are sought to reduce seed predation. The relationship between seed predation and the proximity of the encroaching forest and chaparral edges should be examined in further depth, in order to determine if the elevated
25 seed predation rate is driven by vegetation change. Monitoring efforts should continue alongside any new conservation measures; with ten years of baseline data, this dataset will be invaluable in evaluating any future management strategy. The Lassics lupine will likely require active management for the foreseeable future, but it can persist.
26
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Table 1. Projection matrix for a single Lassics lupine site, where sj is the probability of the survival of class j for one time step, sss is survival of seedlings over the summer, gij is the probability of transition from class j to class i in one time step, g>ij is the probability of transition from class j to any class i or greater, and fj is the mean number of seeds produced by class j.
Seed Seedling Vegetative Reproductive Dormant
Seed s1*g 11 0 0 f4*s 1*g 11 0
(9/12) Seedling s1*(1-g11 ) 0 0 f4*(1-g11 )*s 1 *s ss 0
Vegetative 0 s2*(1-g>42 ) s3*(1-g>43 ) s4*(1-g>44 ) s3*(1-g>45 )
Reproductive 0 s2*g >42 s3*(g >43 -g>53 ) s4*(g >44 -g>54 ) s3*(g >45 -g>55 )
Dormant 0 0 s3*g >53 s4*g >54 s3*g >55
33
Table 2. Mean vital rate values (±variance) at all three Lassics lupine monitoring sites. Caged and uncaged values shown where appropriate. All reproductive plants at the Red site were caged.
Saddle Forest Red Caged Uncaged Caged Uncaged All Caged
sss 0.65 ± 0.01 0.65 ± 0.01 0.65 ± 0.08 0.65 ± 0.08 0.72 ± <0.01 s1 0.70 ± 0.05 0.70 ± 0.05 0.70 ± 0.05 0.70 ± 0.05 0.70 ± 0.05 s2 0.44 ± 0.04 0.44 ± 0.04 0.44 ± 0.08 0.44 ± 0.08 0.47 ± 0.02 s3 0.63 ± 0.02 0.63 ± 0.02 0.77 ± <0.01 0.77 ± <0.01 0.60 ± 0.02
s4 0.85 ± <0.01 0.78 ± <0.01 0.87 ± <0.01 0.87 ± <0.01 0.79 ± 0.03 f4 45.0 ± 124.9 2.25 ± 0.31 24.3 ± 1.01 1.21 ± <0.01 32.5 ± 64.2
g11 0.94 ± 0.01 0.94 ± 0.01 0.94 ± 0.01 0.94 ± 0.01 0.94 ± 0.01 g>42 0.18 ± 0.04 0.18 ± 0.04 0.07 ± <0.01 0.07 ± <0.01 0.02 ± <0.01
g>43 0.52 ± 0.02 0.52 ± 0.02 0.28 ± <0.01 0.28 ± <0.01 0.29 ± <0.01 g>44 0.92 ± <0.01 0.74 ± 0.01 0.70 ± 0.04 0.70 ± 0.04 0.72 ± 0.01
g>45 0.72 ± 0.04 0.72 ± 0.04 0.50 ± <0.01 0.50 ± <0.01 0.89 ± 0.02 g>53 0.06 ± <0.01 0.06 ± <0.01 0.05 ± <0.01 0.05 ± <0.01 0.02 ± <0.01
g>54 0.03 ± <0.01 0.05 ± <0.01 0.01 ± <0.01 0.01 ± <0.01 0.01 ± <0.01 g>55 0.50 ± 0.10 0.50 ± 0.10 0.42 ± 0.01 0.42 ± 0.01 0.67 ± 0.11
34
Table 3. Elasticity matrices for each Lassics lupine monitoring site, evaluated under a scenario where all plants were caged.
Forest Seed Seedling Vegetative Reproductive Dormant Seed 0.134 0 0 0.086 0 Seedling 0.086 0 0 0.039 0 Vegetative 0 0.104 0.146 0.031 0.006 Reproductive 0 0.022 0.132 0.197 0.003 Dormant 0 0 0.008 0.001 0.004
Red Seed Seedling Vegetative Reproductive Dormant Seed 0.160 0 0 0.095 0 Seedling 0.095 0 0 0.044 0 Vegetative 0 0.130 0.104 0.019 0.001 Reproductive 0 0.010 0.146 0.186 0.004 Dormant 0 0 0.003 0.001 0.003
Saddle Seed Seedling Vegetative Reproductive Dormant Seed 0.126 0 0 0.112 0 Seedling 0.112 0 0 0.071 0 Vegetative 0 0.109 0.040 0.015 0.002 Reproductive 0 0.074 0.122 0.206 0.004 Dormant 0 0 0.003 0.002 0.002
35
Figure 1. A reproductive Lassics lupine individual beginning to fruit inside an herbivory exclosure (top). View from Mount Lassic main peak looking eastward toward the saddle and secondary peak, with Red Lassic in right background (bottom).
36
Humboldt Trinity Forest
# Mt. Lassic Saddle
t
d l Red
o y b t i
m n i u r T H # Red Lassic
0100 200 400 Meters ¯
Figure 2. Full extent of Lassics lupine range, with monitoring sites outlined in blue, green, and red. Inset shows northern California, USA.
37
1.0
0.5
0.0
-0.5
Forest-Red Forest-Saddle -1.0 Red-Saddle s s s s s f g g g g g g g g
Spearman Rank Correlation Coefficient ss 1 2 3 4 4 11 >42 >43 >53 >44 >54 >45 >55 Vital Rates
Figure 3. Spearman rank correlation coefficients for vital rate values between each pair of Lassics lupine monitoring sites, within years.
38
All Caged None Caged Real Caged
1.0 Adults 0.8 Seedbank 0.6 λ= 1.168 λ= 0.917 λ= 1.001 0.4
AllSites 0.2 0.0
1.0 0.8 0.6 λ= 1.054 λ= 0.928 λ= 0.947 0.4 Forest 0.2 0.0
1.0 0.8 0.6 λ= 1.002 λ= 0.924 All Red Plants Caged 0.4 Red 0.2 0.0
1.0 0.8 0.6 λ= 1.173 λ= 0.908 λ= 0.991 0.4 Saddle 0.2
Cumulative Probability of Quasi-Extinction Probability Cumulative 0.0
0 10 20 30 40 50 0 10 20 30 40 50 0 10 20 30 40 50 Years in the Future
Figure 4. Results of population projections for all Lassics lupine monitoring sites combined (row 1), the Forest site (row 2), Red site (row 3), and the Saddle site (row 4), under three caging scenarios: all plants caged (left column), no plants caged (middle), and plants caged at the actual current rate (right; see text for details). The cumulative probability of quasi-extinction for adult plants is shown in black (dashed line, 95% confidence interval), while that of the seed bank is shown in gray. All Red site plants are currently caged. Each panel includes the mean stochastic population growth rate for that projection.
39
0.4
0.3
0.2 Contribution
0.1
0.0 sss s2 s3 s4 f4 g>42 g>43 g>53 g>44 g>54 g>45 g>55 s
Vital Rate
Figure 5. Results of one-way LTRE, showing contribution to changes in the Lassics lupine population growth rate by each vital rate due to caging.
40
Forest Red Saddle
1.2 49% Caged 70% Caged 21% Caged
1.1
λ 1.0
0.9
0.8
seed 80 seedling vegetative 60 reproductive dormant v 40
20
0
0.95
0.75 0.20
0.15
w 0.10
0.05
0.00
0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Proportion of Plants Caged
Figure 6. Results of a regression life table response experiment (LTRE), showing the response of Lassics lupine population growth rate (λ), reproductive value ( v), and stable stage distribution ( w) at each of the three monitoring sites, across a gradient of caging of reproductive plants.
41
Number of seeds Number seeds = 14.57*inflorescences
2010
0 50 100 150 200 250 300 350 2011
0 5 10 15 20 25 Number of inflorescences
Appendix 1. Linear regression forced through the origin, using number of seeds as the response and number of inflorescences as the predictor.
42
Appendix 2. Results of t-tests comparing number of inflorescences and rosette diameter between caged/uncaged and bagged/unbagged plants in 2010 and 2011.
Year Response Predictor t df p-value
2010 number of inflorescences caging -0.412 120 0.6812 2010 rosette diameter caging -1.6894 120 0.09397 2010 number of inflorescences bagging -0.4219 56 0.6766 2010 rosette diameter bagging 0.0599 56 0.9527 2011 number of inflorescences caging -0.3842 68 0.702 2011 rosette diameter caging -0.2844 68 0.777 2011 number of inflorescences bagging 0.1798 34 0.8585 2011 rosette diameter bagging -1.1347 34 0.2650
Appendix 3. AIC c table for 20 power models, using number of seeds produced per reproductive plant as the response and the parameters shown plus number of inflorescences as predictors.
Model number Predictors ∆ AIC c Model Weight 20 rosette diameter, distance to forest, aspect 0.00 0.97 17 rosette diameter, distance to forest, distance to chaparral 6.76 0.03 11 rosette diameter, distance to chaparral 17.62 0.00 18 rosette diameter, distance to forest, distance to cover 21.40 0.00 10 rosette diameter, distance to forest 32.46 0.00 19 rosette diameter, distance to forest, slope 34.88 0.00 9 rosette diameter, aspect 39.88 0.00 2 rosette diameter 55.84 0.00 12 rosette diameter, distance to cover 55.89 0.00 8 rosette diameter, slope 57.99 0.00 16 distance to forest, aspect 119.06 0.00 14 distance to forest, distance to cover 138.17 0.00 13 distance to forest, distance to chaparral 164.10 0.00 4 aspect 164.35 0.00 5 distance to forest 167.99 0.00 15 distance to forest, slope 168.59 0.00 6 distance to chaparral 181.35 0.00 7 distance to cover 183.72 0.00 1 [null] 191.23 0.00 3 slope 193.49 0.00 43