UNIVERSITY OF CALIFORNIA, SANTA CRUZ

Niche Partitioning by Peromyscus californicus and Peromyscus boylii in Mixed Evergreen Forest

A Senior Thesis submitted in partial satisfaction

of the requirements for the degree of

BACHELOR OF ARTS

In

ENVIRONMENTAL STUDIES

by

Yasaman Natasha Shakeri

February 2010

ADVISOR(S): Christopher Wilmers, Environmental Studies

Abstract: We used 126 Sherman live traps on a 9 by 14 grid at the University of California Santa Cruz Forest Ecology Research Plot (FERP) to look at small mammal diversity in a mixed evergreen forest. The ability for Peromyscus boylii and Peromyscus californicus to coexist in the same area is believed to be a form of niche partitioning at the canopy level. We used multivariate logistic regression to look for a correlation between the presence of each mouse species and the stem count of 7 of the most common tree species within 10 meters of each trapping station on the plot. P. boylii was correlated with Lithocarpus densiflorus during all three trapping sessions. P. californicus was correlated with Pseudotsuga menziesii and Quercus agrifolia during the spring and summer, but its correlation changed to Quercus agrifolia, Quercus parvula, Arbutus menziesii, and Corylus cornuta during fall. These results show that P. boylii is more of a specialist and P. californicus is more of a generalist in the same habitat. These correlations suggest that microhabitat partitioning is occurring by these two species, allowing for them to coexist.

Key words: niche partitioning, Peromyscus boylii, Peromyscus californicus, forest ecology research plot, resource partitioning, interspecific competition, coexistence

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Introduction In order to share the same habitat, two similar species must exhibit a difference in

resource use (Hardin 1960). The competitive exclusion principle states that two non-

interbreeding populations that share the same ecological niche cannot coexist. The main

purpose for studying resource partitioning is to understand the limits that interspecific

competition places on sympatric species (Schoener 1974).

A more diverse plant community facilitates plant-animal microhabitat associations,

implying that plants directly impact the ability of competitors to coexist, thus influencing

the species richness of animals. There is the potential that without microhabitat

partitioning, one species of mouse would outcompete the others, thus reducing the

species richness of mice. The quantitative conditions for the coexistence of two species

depend on both the respective carrying capacities of each species and the strength of the

competitive interaction (Figure 1 a, b).

The Lotka-Volterra competition model is a

mathematical model used to describe

interspecific competition between two or more

species. The competition coefficient α12 is the

impact the population growth of species 2 has on species 1, while α21 is the impact the

population growth of species 1 has on species 2. K1 and K2 represents the carrying

capacity (Gotelli 2008). If then coexistence can occur. If

microhabitat partitioning is occurring between these two Peromysucus species the

competition coefficient (α) would be reduced allowing

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both species to coexist (Figure 1, b). If there is an increase in α21 due to a decrease in

microhabitat partitioning then competitive exclusion would occur (Figure 1, a) and

species 2 would outcompete species 1 to extinction.

Peromyscus boylii and P. californicus inhabit mixed evergreen forest in the Santa

Cruz Mountains. P. boylii, also known as the Brush mouse, is a medium sized member of

its genus. It survives in various habitats throughout the western United States

including chaparral, pine and mixed woodland forest (Baker 1968, Findley et al. 1975,

Hoffmeister 1986). They prefer habitat that provides cover such as high understory

growth, logs and rocks (Wilson 1968, Holbrook 1978). P. boylii is also known for its

arboreal behavior. The feeding habits of P. boylii are determined by their location and

the seasonal food availability. Acorns, Manzanita and conifer seeds make up a large

portion of their winter diet while their summer diet consists of insects and fruits

(Jameson, 1952; Smartt, 1978). These small mammals are able to produce multiple litters

Figure 1: Panel (a) shows competitive exclusion of species 1 by species 2. Panel (b) shows competitive coexistence by both species. K represents the carrying capacity of each species. α12 is the impact population growth of species 2 on species 1. α21 is the impact of the population growth of species 1 on species 2. Partitioning of resources would decrease the impact of α12 and α21 allowing for coexistence (b). If there is an increase in α in panel (b) then it would look more like panel (a) resulting in one species to go extinct.

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a year, which can lead to dense populations in parts of its range (Bradley and Schmidly

1999).

P. californicus, also known as the California mouse, ranges throughout the California

coast and inland to the eastern Sierras. Their range is restricted to dense chaparral and

mixed woodland (Meritt, 1974). Fruit, seeds and flowers make up a large portion of their

diet. In the woodland areas of California their diet consists mainly of the seeds of

California Bay Laurel (Meritt 1974). Life span ranges from 9 to 18 months in the wild.

This species is also known for its monogamous behavior; both the female and male

provide care for their offspring (Meritt, 1974; Merritt, 1999).

The existence of these two species on the plot raises questions related to resource

partitioning. If P. boylii and P. californicus are able to coexist then some form of spatial

or food source partitioning is taking place. In figure 1 we can see that either competitive

exclusion or competitive coexistence determines whether two ecologically similar species

can exist in the same area. The various plant species on the FERP allow for microhabitat

partitioning between the two Peromyscus species to occur.

A previous study conducted on these two mouse species at Hastings reserve in

Monterey California found P. boylii specializing more on Coast live oak (Quercus

agrifolia) as a food source and canopy cover whereas P. californicus was more of

a habitat generalist and had a broader diet. It was discovered that in areas where high

densities of P. californicus were present, both species of Peromyscus were negatively

numerically associated. Wherever P. californicus was abundant P. boylii was less likely

to be found. The strong association between P. boylii and Quercus agrifolia shows a form

of habitat partitioning that allows for these two species to coexist. P. boylii has shown

increased specialization, perhaps allowing it to coexist with P. californicus (Kalcounis-

Ruppell and Millar 2002). To test whether these two species exhibit similar partitioning

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behavior in the FERP, we began a long term study of small mammal populations in

a temperate forest ecosystem. The goal of our project is to determine whether small

mammals associate with different resources and to answer questions regarding their

behavior and survival.

Methods

Location and collecting methods We conducted trapping in Santa Cruz County at the

University of California Santa Cruz Forest Ecology Research plot at 37°0.745' N,

122°4.490' W. The annual rainfall in this area is 776

mm, with 96% of the rain falling between the months

of October and April. The 6 hectare plot, constructed

in 2007 by Professor Gregory Gilbert, consists of 200

by 300 meters of mixed evergreen forest. A grid

divides the forest into 20 by 20-m quadrats. A corner

of each quadrat was marked with a pvc-pipe and a

flag. In order to determine the location of each plant

the distance from the plant to the corner of each

quadrat was measured. This radial measurement was

then converted to a global x,y coordinate (Gilbert et

al 2009). The plot has 31 different plant species and

8,175 individuals with a diameter at breast height

greater than 1cm. Each of these individuals has been

marked and their location recorded within this large

grid system. Data such as soil moisture, nutrients,

woodrat nests and seed and fruit litter has also been recorded (Gilbert et al. 2009). Figure

2 shows a visual representation of the Forest Ecology Research Plot. The red dots each

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represent a small mammal trapping station on the 9 by 14 grid, each separated by 20

meters.

Field work was conducted during the spring, summer and fall of 2009. Our spring

trapping session occurred from April 29th through May 1st, the summer trapping occurred

from July 28th through the 30th, and the final trapping session occurred from October

27th through the 29th. We set 126 Sherman live traps on the 9 by 14 grid for a total of 378

trap nights for each three day trapping session. We placed one trap within one meter of a

flag that identified each individual trapping station. The outside perimeter of the forest

plot was excluded from the experiment since plant data was not recorded beyond the plot.

The exclusion of the outer perimeter also reduced possible edge effects, especially in

areas near roads.

We used bait consisting of a mixture of peanut butter and oats, which also provides

calories to captured animals. We also provided polyester bedding to provide

insulation during cold nights in the spring and fall.

We placed traps at sunset of each night and checked the following morning. All small

mammals that were captured were identified, sexed, and weighed. The grid location of

each individual animal was also recorded. Other recordings included measurements of

ears, tail, and body, if an individual was difficult to identify. Female mammals were

examined to determine if they were pregnant or lactating. In order to recognize

recaptured animals in future trapping sessions, we placed a small metal ear tag displaying

a unique number on the right ear of each mammal. We recorded the above measurements

and the locations of each captured individual on a constructed datasheet (Appendix I).

Data Analysis

For each trapping session a distinct logistic regression was run by assigning presence

and absence for each of the two species at every trapping station. All statistical

analysis was performed using the program R (version 2.10.1). We used multivariate

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logistic regression to determine microhabitat associations of each species of mouse.

Microhabitats were described by using the stem counts of each of the seven most

common tree species within 10 meters of each trapping station. We determined the most

parsimonious model using Akaike Information Criterion (AIC) by using the step function

in R.

Results Capture Results We sampled for a total of 1134 trap nights, which resulted in 414 captures. P. boylii was

captured 260 times and P. californicus was captured 118 times through all three trapping

sessions (Figure 3; these numbers include recaptures).

Figure 3 The number of P. boylii and P. californicus caught each day of trapping through out the three trapping sessions. Presence and absence correlations The estimate and p-value for all the covariates that were statistically significant are

shown with the presence and absence of both species during each trapping session

(Appendix II). The presence of P. californicus and the stem count of Douglas fir

(Pseudotsuga menziesii) showed statistical significance within 10 meters of trap stations

where this species was caught during both spring and summer trapping sessions (April p-

value=0.000315 standard error=0.0232; July p-value=0.0473, standard error=0.01990).

There was also statistical significance between the presence of P. californicus and the

stem count of Quercus agrifolia during spring trapping (April p-value=0.049413,

2009 trapping sessions Peromyscus boylii Peromyscus californicus April 29 29 21 April 30 36 16 May 1 40 5 July 28 28 15 July 29 28 15 July 30 30 16 October 27 17 13 October 28 23 9 October 29 29 8 Total Captures 260 118

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standard error=0.06348). The AIC value for this multivariate logistic regression for April

was 128.48 and July trapping had an AIC value of 142.47. As the number of stems of

Pseudotsuga menziesii increases the probability of P. californicus being present also

increases (Figure 5).

The presence of P. boylii during both the spring and summer trapping sessions showed

statistical significance with the stem count of Tan oak (Lithocarpus densiflorus) (April P-

value: 0.00171, standard error: 0.02877), (July P-value= 0.00113, standard

error= 0.02734) and Shreve oak (Quercus parvula) (April P-value= 0.04147, standard

error= 0.02514), (July P-value= 0.02403, standard error= 0.02462). April trapping had an

AIC value of 161.64, and July

Figure 5 As the stem count of Pseudosuga menziesii increases the probability of presence of P. californicus increases.

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trapping had an AIC value of 154.77. Figure 6 shows the probability of presence of P.

boylii as the stem count of Lithocarpus densiflorus increases during spring and summer

trapping sessions.

October trapping showed very different results than previous trapping sessions for P.

californicus. The presence of P. californicus was correlated with Pacific madrone

(Arbutus menziesii) (P-value= 0.0450, standard error= 0.10339), Western hazelnut

(Corylus cornuta) (P-value= 0.0400, standard error= 0.02080), Quercus agrifolia (P-

value= 0.0828, standard error= 0.08050), and Quercus parvula (P-value= 0.0325,

standard error= 0.03599). The AIC for this statistical analysis was 92.405.

Figure 6 As the stem count of Lithocarpus densiflorus the probability of presence of P. boylii increases.

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October results for P. boylii showed a repeated correlation between Lithocarpus

densiflorus (P-value= 0.0515, Standard error= 0.01931, AIC= 143.82) within 10 meters

of each trap station, but no statistical significance with Quercus parvula.

Discussion

The strong correlation seen by each Peromyscus with the stem counts of certain tree

species in the plot tells us that a partitioning of microhabitats can explain their ability to

coexist. P.boylii’s correlation with Lithocarpus densiflorus through all three trapping

sessions and Quercus parvula throughout two of the three trapping sessions could be a

sign of food source specialization. During the fall it was hypothesized that diet change

may occur with the dropping of acorns, yet the correlation between P. boylii and

Lithocarpus densiflourus continued to be the only statistically significant data witnessed

in October.

P. californicus correlated with Pseudotsuga menziesii during the spring and summer

trapping sessions may be a form of resource partitioning during times of lower food

availability. October results show P. californicus being correlated with Quercus

agrifolia, Quercus parvula, Corylus cornuta and Arbutus menziesii within the plot. This

correlation shift may be explained by the maturing of acorns, seeds and fruit during the

fall season. The increased availability of these food sources would allow P.

californicus to broaden its diet, making it more of a generalist in this habitat. Even with

this shift in correlation, P. californicus and P. boylii do not show any overlap with the

plant the data analyzed.

The ability for P. boylii and P. californicus to coexist in the same habitat has also been

demonstrated by other researchers at the UC Hastings reserve. P. boylii was more of a

specialist than P. californicus, which allowed for the partitioning of food sources

(Kalcounis-Ruppell and Millar 2002).

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The presence of both Peromyscus species with certain trees in mixed evergreen

forest brings up further questions regarding zootomic disease transmission. Lyme disease

(Borrelia burgdorferi) has many vertebrate host species including the genus

Peromyscus. In the Northeastern United States studies have shown that fragmented

forests with low biodiversity hold higher levels of Borrelia burgdoferi, generally where

high densities of White footed mouse (Peromyscus leucopus) are present. The high

density of this Peromysucs species may be explained by the low biodiversity associated

with forest fragmentation (LoGiludice et al. 2003).

If the biodiversity of this area was to decrease due to forest fragmentation this may

impact the ability of P. boylii and P. californicus to be able to coexist, allowing one of

the two species to grow in population and the other to go extinct. The loss of biodiversity

would also remove predators and other competitors of this Peromyscus species. If such

forest fragmentation was to occur in this area, Lyme disease may become more

prominent, which could have an impact on public health.

As this study continues on the Forest Ecology Research Plot Lyme disease testing on

all captured mice should take place as well as a calculation of the population density of

both P. boylii and P. californicus. It should be determined if the densities of either

species is statistically significant with the stem count of any of the trees these mice have

been correlated with. This additional research will determine whether this disease is a

prominent risk to public health at the current time or in the future.

As additional data is collected over the years, we will be able to determine if the

correlations observed in this study are a continuing cycle in regards to microhabitat

partitioning or whether other factors such as intraspecific competition may be occurring.

To further test this hypothesis trapping should be conducted in areas where a single

dominant plant species reside, such as Oak woodland. If either Peromyscus species is

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absent then we can conclude that microhabitat partitioning plays is a big role in the

coexistence of these two species in the same niche.

The higher number of P. boylii captured during each session may make this species

the dominant competitor in the region or it may just be more abundant in its geographic

range. Further examination of small mammal ecology will aid in answering questions

related to the ecosystem functions of this temperate region and in other parts of the world.

Acknowledgements: I would like to thank Yiwei Wang, Taal Levi, Christopher Wilmers

and Gregory Gilbert for giving me this opportunity to conduct research as well as

advising me on data analysis and the construction of my paper. I would also like to thank

Christopher Lay, Gage Dayton and Kiri Ando for their help and advice throughout my

research. A big thank you goes to everyone who came out to help during trapping

sessions.

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References

Baker, R. 1968. Habitats and distribution. Pp. 98-126 in J. King, ed. Biology of *Peromyscus* (Rodentia). Special Publication Number 8: American Society of Mammalogists. Boyett, William D. 2001. Habitat relations of rodents in the Hualapai Mountains of northwestern Arizona. Oshkosh, WI: University of Wisconsin Oshkosh. 75 p. Thesis. Bradley, R., D. Schmidly. 1999. Brush mouse / *Peromyscus boylii*. Pp. 564-565 in D. Wilson, S. Ruff, eds. The Smithsonian book of North American mammals. Washington, D.C: Smithsonian Institution Press. Findley, J., A. Harris, D. Wilson, C. Jones. 1975. Mammals of New Mexico. Albuquerque, New Mexico: University of New Mexico Press. Gilbert, GS, E. Howard, B. Ayala-Orozco, M. Bonilla-Moreno, J. Cummings, S. Langridge, IM Parker, J. Pasari, D. Schweizer, and S. Swope. 2009. Beyond the tropics: forest structure in a temperate forest mapped plot. Journal of Vegetation Science in press. Gotelli, Nicholas J. A Primer of Ecology. 4th ed. Sunderland: Sinaurer Associates. 2008. Print. Hardin, G. 1960. The Competitive Exclusion Principle. American Association for the Advancement of Science. Hoffmeister, D. 1986. Mammals of Arizona. Tucson, Arizona: Arizona Game and Fish Department and University of Arizona Press. Jameson, E. 1952. Food of deer mice, *Peromyscus maniculatus* and *P. boylei*, in the northern Sierra Nevada, California. Journal of Mammalogy, 33: 50-60. Kalcounis-Ruppell, M.C, J. Millar. 2002. Partitioning of Food, Space and Time By Synoptic Peromyscus boylii and P. californicus. Journal of Mammalogy. LoGiludice, Kathleen; Duerr, Shannon T. K.; Newhouse, Michael J.; Schmidt, Kenneth A.; Killilea, Mary E.; Ostfeld, Richard S. 2008. Impact of host community composition of Lyme disease risk. Ecology. Vol. 89 Issue 10, p2841-2849. Meritt, J. 1974. Factors influencing the local distribution of Peromyscus californicus in Northern California. Journal of Mammalogy, 55: 102-113. Merritt, J. 1999. California mouse. Pp. 565-566 in D.E. Wilson, S. Ruff, eds. The Smithsonian Book of North American Mammals. Washington, D.C.: Smithsonian Institution Press. Meserve, P. 1977. Three-dimensional home ranges of cricetid rodents. Journal of Mammalogy, 58: 549-558.

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Schmidly, D., R. Bradley, P. Cato. 1988. Morphometric differentiation and taxonomy of three chromosomally characterized groups of *Peromyscus boylii* from east-central Mexico. Journal of Mammalogy, 69: 462-480. Smartt, R. 1978. A comparison of ecological and morphological overlap in a *Peromyscus* community.Ecology, 59: 216-220.

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Appendix I

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Appendix II

The estimate and p-value for all statistically significant correlations found between stem count and presence and absence of each Peromyscus species through out all three trapping sessions on the FERP

Estimates and P-values of Multivariate logistic regression on covariates and both Peromyscus species for each trapping session Trapping sessions Spring Summer Fall Covariate P. boylii P. californicus P. boylii P. californicus P. boylii P. californicus Sum of stem Pseudotsuga menzeisii 0.08364 **** 0.03947 ** Sum of stem Lithocarpus densiflorus 0.09022 **** 0.08902*** 0.0376 * Sum of stem Quercus parvula 0.05126 *** 0.05556 ** 0.07694 ** Sum of stem Arbutus menziesii -0.20728 ** Sum of Stem Corylus cornuta 0.04272 ** Sum of stem Quercus agrifolia -0.12474 ** -0.13965 * Significance code * : p ≤ 0.1 * * : p ≤ 0.05 * * * : p ≤ 0.01 * * * * : p ≤ 0.001