Population Viability Analysis of the Mountain lion (Puma concolor) in the State of Wisconsin

Brad Bulin
Steve Fullington
Ryan Meinerz

Introduction

Currently the Wisconsin Department of Natural Resources does not recognize the existence of any wild populations of mountain lions (Puma concolor) inside the state of Wisconsin.  The persistence of unverified sightings by Wisconsin residents has led many to believe that there either is a small population of mountain lions already living in the state or that mountain lions will eventually immigrate into the state and establish themselves.
          
Our objectives were to (1) determine the approximate total area of available suitable habitat for the mountain lion in Wisconsin; (2) determine the environmental carrying capacity for the mountain lion in the state of Wisconsin; and (3) examine the likelihood of mountain lion population survival in Wisconsin for at least 100 years using various beginning population sizes.
 

Methods

In order to assess the probability of survival (P_s) for a mountain lion population in the state of Wisconsin we had to first determine the total area of available mountain lion habitat, and then use that data to determine the states mountain lion carrying capacity.  In order to determine the amount of available mountain lion habitat, we first reviewed mountain lion habitat requirements.  Mountain lions tend to favor riparian vegetation types (Dickson and Beier, 2002) as well as closed conifer vegetation types (Williams et al, 1995).  Mountain lions tend to avoid human-dominated areas and grasslands (Dickson and Beier, 2002).  Aside from vegetation types, proximity to roads has been shown to influence mountain lion home ranges. In general, mountain lions use of areas increase with distance from paved roads (Dickson and Beier, 2002; Van Dyke et al, 1986; Sweanor et al, 2000).  Mountain lion response to road densities and human disturbance is similar to that of the gray wolf (Canis lupus).  Favorable wolf habitats contain low human densities, limited public accessibility, and minimal livestock production (Thiel 1985, Mech 1988, Fuller 1995).  The amount of primary and secondary wolf habitat in Wisconsin has previously been studied (Mladenoff et al. ,1995).  In that study it was found that Wisconsin has 9,354 km² (5,812 mi²)of primary wolf habitat and 8,071 km² (5,015 mi) of secondary habitat (total 17,424 km or 10,827 mi²). 

Since habitat suitability for mountain lions and wolves may be similar, we used the primary and secondary wolf habitat figures for our analysis of Wisconsin mountain lion carrying capacity.  Home range sizes differ for male and female mountain lions.  We used male and female mountain lion home range data from 14 different mountain lion studies (Ross and Jalkotzy, 1992; Cunningham et al., 1995; Beier and Barrett, 1993; Hopkins, 1989; Anderson et al., 1992; Maehr et al. 1991; Seidensticker et al., 1973; Ashman et al., 1983; Logan et al., 1996; Murphy, 1983; Pittman et al., 2000; Pence et al. 1987; Hemker et al., 1984; Logan, 1983) to estimate an average home range size for both male and female mountain lions.  The studies yielded average male home ranges of 436 km  and average female home ranges of 203 km Using the data on potential habitat, we estimated that roughly 40 males and 85 females (17424km/436 km and 17424km/203 km respectively) could be supported in Wisconsin.  This would give us a 2:1 female to male ratio of mountain lions in the state and a total state carrying capacity of 125 mountain lions. The 2:1 female/male ratio has been documented previously (Hanson, 1992). 

We used a stochastic PVA (Vortex 9.33) to assess the possibility of a population of mountain lions to survive in Wisconsin for a time frame of at least 100 years. We ran simulations using beginning mountain lion populations of 5, 10, 15, 25, 35, and 50 individuals. We ran each simulation through 1000 iterations and  We used a carrying capacity of 125 individuals with a standard deviation of 25 as detailed previously in this report.

Mortality figures that were used in the analysis are detailed in Tables 1, 2 and 3.  We ran analyses for each starting population using three different mortality rates.  For 1/3 of the simulations we ran mortality rates for mountain lions that were supported by relevant literature, these were listed as normal mortality rates.  We halved these rates in 1/3 of our simulations; these were labeled low mortality rates.  We added 50% to the mortality rates in the remaining 1/3 of the simulations, these we labeled as high mortality rates.  In each simulation, a total of 1000 iterations were run.
   
For the purposes of each simulation, we defined extinction as any case where the population number was less than or equal to 1(N≤ 1).  We determined the probability of extinction (P_E) by dividing the number of iterations that went extinct by 1000.  This produced a percentage of iterations that reached extinction during the simulation. Vortex mammalian default settings of 3.14 for lethal equivalents and 50% due to recessive lethals were used.  Age of first offspring was entered as 2 years for both males and females (Anderson, 1983).  The maximum number of progeny per year was listed as 6 and the mean number of young per year was listed as 3.4 with a standard deviation of 1 (Hansen, 1992). Since mountain lions can maintain the ability to reproduce throughout their lives the maximum age of reproduction was entered as 10 (Hansen, 1992).  An equal sex ratio value was assigned for males and females at birth (Anderson, 1983).  The percent of adult females breeding was listed as 80 (Sweanor, et al, 2000). The percentage of males in the breeding pool was listed as 35.  No harvest, population supplementation, or catastrophe data were used in the analysis.

Analysis of population viability for a founder population of 5 individuals showed that under low mortality rates 21.8% of the iterations became extinct by the end of the 100 year time period.  The normal or medium mortality rate simulation for the founder 5 population showed significantly higher probabilities of extinction.  Under this scenario 61.3% of the iterations went extinct by the end of the 100 year time period.  High mortality rate analysis of the founder 5 population size showed extinction (Fig. 1).

Analysis of population viability for the founder population of 10 individuals showed that under low mortality rates approximately 13% (13.3) of the iterations became extinct by the end of the 100 year time period.  The normal or medium mortality rate simulation for the founder 10 population showed significantly higher probabilities of extinction.  Under this scenario approximately 35% (35.2) of the iterations became extinct by the end of the 100 year time period.  High mortality rate analysis of the founder 10 population size showed complete extinction when the simulation was run.  100% of the iterations run under the high mortality scenario were extinct by the end of the 100 year time period (Fig. 2).

Analysis of population viability for the founder population of 15 individuals showed that under low mortality rates approximately 12% (11.7) of the iterations became extinct by the end of the 100 year time period.  The normal or medium mortality rate simulation for the founder 15 population showed significantly higher probabilities of extinction.  Under this scenario approximately 31% (30.7) of the iterations became extinct by the end of the 100 year time period.  High mortality rate analysis of the founder 15 population size showed complete extinction when the simulation was run.  100% of the iterations run under the high mortality scenario were extinct by the end of the 100 year time period (Fig. 3).
 
Analysis of population viability for the founder population of 25 individuals showed that under low mortality rates approximately 9% (9.3) of the iterations became extinct by the end of the 100 year time period.  The normal or medium mortality rate simulation for the founder 25 population showed significantly higher probabilities of extinction.  Under this scenario approximately 23% (22.9) of the iterations became extinct by the end of the 100 year time period.  High mortality rate analysis of the founder 25 population size showed complete extinction when the simulation was run.  100% of the iterations run under the high mortality scenario were extinct by the end of the 100 year time period (Fig. 4).
  
Analysis of population viability for the founder population of 35 individuals showed that under low mortality rates approximately 11% (11.2) of the iterations became extinct by the end of the 100 year time period.  The normal or medium mortality rate simulation for the founder 35 population showed significantly higher probabilities of extinction.  Under this scenario approximately 22% (21.7) of the iterations became extinct by the end of the 100 year time period.  An analysis using high mortality rates with a founder 35 population size showed almost complete extinction when the simulation was run.  99.8% of the iterations run under the high mortality scenario were extinct by the end of the 100 year time period (Fig. 5).

Analysis of population viability for the founder population of 50 individuals showed that under low mortality rates approximately 11% (10.9) of the iterations became extinct by the end of the 100 year time period.  The normal or medium mortality rate simulation for the founder 50 population showed significantly higher probabilities of extinction.  Under this scenario approximately 22% (22.2) of the iterations became extinct by the end of the 100 year time period.  High mortality rate analysis of the founder 50 population size showed almost complete extinction when the simulation was run.  99.9% of the iterations run under the high mortality scenario were extinct by the end of the 100 year time period (Fig. 6).
   
We saw marked increases in survival with each increase in founder the population until the founding population was 35.  The extinction rates for each of the levels of mortality (low, medium and high) were virtually unchanged between the 25 and 50 founder populations.  Three distinct trends in the P_E rates were observed when all the starting populations using medium mortality rates are plotted (Fig. 7).  The first trend is the line for the starting population of 5 individuals showing a large increase and a continued increase in the P_E.  The second trend is the lines for the populations starting with 10 and 15 individuals showing a more gradual rise in the P_E and also shows a continual increase in the P_E over time.  The third trend contains the lines for the populations starting with 25, 35 and 50 individuals that show the most gradual and constant rise in P_E than what was observed with trends 1 and 2.
 

Discussion

If there is to be a viable population of mountain lions in the state of Wisconsin there must be a relatively significant starting population.  Small founding populations (5,10,15) are highly variable in their ability to survive for 100 years.  Under normal mortality conditions, in order to have a reasonable chance of long-term survival a minimum of 25 individuals would be desirable.  If for any reason (e.g., catastrophe, poaching, disease, etc.) mortality rates are significantly higher for Wisconsin mountain lions than for populations in other states, there will be little chance for the population to survive.  Even with the relatively large starting population of 50 individuals (a large starting population when one assumes no current individuals in the state) there was near complete extinction with increased mortality rates.  All of the simulations indicated increasing P_E over time, and none of the simulations indicated that the P_E would remain stable. 

Possible sources of error in the analysis include the figure that was used for carrying capacity.  To our knowledge there are no studies on available mountain lion habitat in the state of Wisconsin.  Since wolves and mountain lions share many similar habitat attributes we based our figures on total wolf habitat.  We acknowledge there was no specific study defining mountain lion habitat in Wisconsin or estimate the populations size that could be supported on the quality of habitat.

It is important to note that our data were obtained without factoring in the effect density dependence may have on mountain lion populations in the state.  Given the extremely small populations of mountain lions we used in our analysis it is difficult to assess what effect, if any, density dependence would have on the mountain lions. We ignored potential density dependent effects even though in same areas density dependence does have an effect on the number of breeding mountain lions in a given year (Hanson, 1992; Logan & Sweanor, 2001).  It is possible our P_E figures are artificially low due to exclusion of density dependence effects.

Table 1: Data used for low level cougar mortality.

Table 2: Data used for medium level cougar mortality.

Table 3: Data used for high level cougar mortality.

*Anderson et al., 1992

Figure 1:  Percent probability of extinction with an initial cougar population of 5 individuals according to Vortex 9.33 PVA software

Figure 2:  Percent probability of extinction with an initial cougar population of 10 individuals according to Vortex 9.33 PVA software

Figure 3:  Percent probability of extinction with an initial cougar population of 15 individuals according to Vortex 9.33 PVA software

Figure 4:  Percent probability of extinction with an initial cougar population of 25 individuals according to Vortex 9.33 PVA software

Figure 5:  Percent probability of extinction with an initial cougar population of 35 individuals according to Vortex 9.33 PVA software

Figure 6:  Percent probability of extinction with an initial cougar population of 50 individuals according to Vortex 9.33 PVA software

Figure 7:  Comparison of percent probability of extinction using medium mortality rates for each tested founder population according to Vortex 9.33 PVA software

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