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Habitat heterogeneity and survival in a bush cricket metapopulation.


Weather fluctuations are known to have great impact on the population dynamics of many species, including insects (e.g., Ehrlich et al. 1980, Solbreck and Sillen-Tullberg 1986, Kingsolver 1989), frogs (Sjogren 1988), snakes (Forsman 1993), small rodents (Peltonen and Hanski 1991), kangaroos (Cairns and Grigg 1993), and birds (Takekawa and Beissinger 1989). Because weather conditions are similar over large areas, weather is likely to lead to synchronized population dynamics and correlated local extinctions. This phenomenon is called regional stochasticity (Hanski 1991), i.e., spatially correlated environmental stochasticity. Synchronous population dynamics are also assumed to arise when interpatch dispersal rates are high (Maynard Smith 1974). The expected persistence time of a metapopulation decreases as the spatial synchrony among local population increases (Harrison and Quinn 1989, Gilpin 1990, Hanski and Woiwod 1993).

When trying to preserve endangered species, it is desirable to reduce the risk of local extinctions occurring at too many habitat patches simultaneously. Increasing isolation between habitat patches is expected to decrease the impact of regional stochasticity as well as interpatch dispersal, and hence should facilitate metapopulation persistence by reducing spatial synchrony among local populations (Hanski 1991). However, increasing the distance between patches reduces the probability of recolonizations once local populations have become extinct. Besides, isolation also reduces the rescue effect (Brown and Kodric-Brown 1977, Sjogren 1991). Both these processes are supposed to diminish the expected lifetime of a metapopulation as isolation increases. Consequently, it is hard to tell whether or not habitat patches should be widely dispersed in order to minimize the risk of extinction of a whole metapopulation. So far, very few attempts have been made to answer this important question.

Because dispersal is assumed to be fairly restricted within the scale of a metapopulation, it is likely that spatial synchrony among local populations is due more to regional stochasticity than to interpatch dispersal (cf. Hanski 1991, Hanski and Woiwod 1993). Therefore, it might be a good conservation strategy to maintain high connectivity among habitat patches and try to counteract the impact of regional stochasticity within a limited spatial scale. The aim of this study is to suggest how this could be achieved.

The habitat requirements of the bush cricket Metrioptera bicolor Philippi (Orthoptera: Tettigoniidae) are known to change in relation to weather (Kindvall 1995). Dense, tall grassland vegetation was beneficial for the survival of juveniles during a severe drought in 1992. Most specimens were observed to move into sites with taller vegetation where at least some moisture could be utilized. In extremely rainy years it might be better for insects like M. bicolor to live in sites with sparse vegetation on sandy soil that dries up faster. Increased rainfall has a depressing effect on both survival rate and fecundity of orthopteran species in the temperate zone (Richards and Waloff 1954).

When habitat quality changes in relation to weather parameters it may be expected that populations living on heterogeneous habitat patches should fluctuate less than populations living on homogeneous patches. Furthermore, populations living on homogeneous habitat patches should be more prone to extinction than populations living on patches with a high diversity of habitats with different local climate (cf. Dobkin et al. 1987, Ehrlich and Murphy 1987, Weiss et al. 1988). In this paper, I will test these two interrelated hypotheses.


The species

Metrioptera bicolor is a medium-sized (12-19 mm), ground-living bush cricket or katydid without flight ability (Holst 1986). Juveniles emerge in May, having six nymphal instars, and become adults during July. The mating period lasts until late October. No specimens survive the winter. M. bicolor may have both 1- and 2-yr egg cycles (Ingrisch 1986). A total reproduction failure on a patch in one year does not therefore necessarily mean that the local population becomes extinct, since there might be eggs left to ensure future generations.

In Scandinavia, M. bicolor only occurs within a tiny area ([less than]100 [km.sup.2]) in southernmost Sweden ([ILLUSTRATION FOR FIGURE 1 OMITTED], 55 [degrees] 40[minutes] N, 13 [degrees] 35[minutes] E). This regional distribution may be described as a metapopulation (cf. Hanski and Gilpin 1991), where both extinctions and colonizations have been documented (Kindvall and Ahlen 1992, Kindvall 1993a). Interpatch exchange of individuals is known to be very limited, and individuals occupying different grassland patches are without question separate demographic units, i.e., local populations (Kindvall and Ahlen 1992, Kindvall 1993b).

The patches that can support local populations of M. bicolor consist of four distinct types of vegetation habitat (Kindvall and Ahlen 1992), designated as follows: habitat type 1, poor sandy grassland, dominated by the grass species Corynephorus canescens (L.); habitat type 2, sparsely overgrown and unstable sand; habitat type 3, dense, low grassland, dominated by the grass species Agrostis capillaris (L.); habitat type 4, dense, tall grassland, consisting mainly of the grass species Arrhenatherum elatius (L.). The amount of each habitat type varies between patches. Other habitats occurring in the study area, e.g., pine forest, pastures, and farmland, are considered matrix habitats, where M. bicolor cannot reproduce and probably cannot even survive for longer periods (Kindvall and Ahlen 1992).

The field work

In the years 1989-1994, all available patches of suitable habitat (n = 115) were visited annually during the mating period to determine the presence or absence of M. bicolor. The surveys were carried out during periods when males in reference areas were stridulating vigorously. In cases where no specimens were observed and no stridulation heard, the localities were revisited. Because of the plurennial egg cycles of M. bicolor, a local population is only considered to have become extinct when a previously occupied patch remains empty during at least two successional years. Knowledge about occupancy in the years 19851986 (Kindvall and Ahlen 1992) was used to score extinctions in 1989.

Estimates of local population size of M. bicolor were made annually from 1989 to 1994 in 45 patches by counting the number of stridulating males. During sunny conditions, almost every male stridulates, which makes it possible to get reliable estimates of adult population size just by walking through a patch in straight lines spaced at 4 m and counting each male. Within a distance of 4 m it is possible to decide how many males are calling at the same time, even when they are close to each other.

Statistical treatment

The arithmetic mean as well as the coefficient of variance (CV) of local population size was calculated for each trajectory (n = 45). The CV has been recommended as the least ambiguous measure describing population variability (e.g., McArdle et al. 1990). Zeros were not included in the calculations, and I only considered estimates of local population size that were observed before extinctions. At least three data points have been used for variable estimation from those trajectories that include extinction events (n = 5) or occasional zeros (n = 3). Most CV values and means are based on six observations. To estimate the degree of spatial synchrony in the dynamics of local populations, I calculated Spearman rank correlation coefficients between all pairs of local trajectories that do not include extinction events (n = 40).

Information on habitat heterogeneity was extracted from digitized infra-red (IR) flight photographs (from 30 June 1986). Grassland habitats have different coloration on IR film, depending on humidity (Ihse et al. 1993). Habitat types 1, 2, and 3, which are extremely dry, are bluish, while habitat type 4, which contains relatively more humidity, is usually red or pink. Habitat heterogeneity was measured for each patch as the variation of the spectral range (hue), extending from red to a bluish color. In order to do this, I split the digitized color pictures into the three spectral ranges, red, green, and blue. Then I calculated the standard deviation of gray levels for each range separately. By summing these SD values for each patch, I constructed an index of habitat heterogeneity (SD-hue).
TABLE 1. The relationship between temporal variability of local
population size (CV) and the independent variables: habitat
heterogeneity (SD-hue), interpatch distance (metres), and (a) mean
population size or (b) patch area (hectares), alternatively.
are from multiple linear regression analysis (n = 45).(*)

                   coeffi-                t                    P
Variables           cient      1 SE     value                value

a) Intercept        91.61     10.17      9.00     [less than]0.0001
SD-hue              -0.13      0.03     -4.64     [less than]0.0001
Log distance        -3.63      4.03     -0.90                0.37
Log mean            -1.34      3.83     -0.35                0.73

b) Intercept        89.00      7.89     11.28     [less than]0.0001
SD-hue              -0.13      0.03     -4.61     [less than]0.0001
Log distance        -3.40      3.96     -0.86                0.40
Log mean            -1.00      3.89     -0.26                0.80

* (a) [R.sup.2] = 0.31; (b) [R.sup.2] = 0.31.

The area of habitat type 4, i.e., dense, tall grassland, has been quantified previously for 40 of the patches in the study area (Kindvall 1995). The relationship between the index of habitat heterogeneity and the relative area of habitat type 4 is shown in Fig. 2. As expected, SD-hue is greatest when about half of the patch consists of habitat type 4, and lowest when habitat type is either dominant or absent. However, it is worth noting that the variation of SD-hue is strikingly high even on patches with only dense, tall grassland. This indicates that the humidity can vary considerably within the same type of vegetation. Thus, SD-hue is better interpreted as a variable describing heterogeneity of humid conditions, rather than heterogeneity of vegetation types.

Patch area and interpatch distances were measured on an economic map (1:10 000). In the variability and extinction analyses, I used an estimate of interpatch distance that was measured as the edge-to-edge distance to the nearest patch where M. bicolor was continuously present during the survey period. In the analysis of spatial synchrony, I used the distances between pairs of habitat midpoints.


Temporal variability of local population size is significantly affected by habitat heterogeneity [ILLUSTRATION FOR FIGURE 3A OMITTED]. This positive relationship is robust even when other plausible covariates are controlled for (Table 1). There is no effect of interpatch distance, mean population size, and patch area on the population variability of Metrioptera bicolor (Table 1, [ILLUSTRATION FOR FIGURE 3B-D OMITTED]). Mean population size is positively correlated with patch area (Spearman rank correlation: [r.sub.s] = 0.82, n = 45, P [less than] 0.0001). The coefficient of variance of local population size was not significantly higher on patches where extinctions occurred (median = 72%, n = 5) than on patches with extant populations (median = 60%, n = 40; Mann-Whitney U test: Z = 1.07, P = 0.29, NS; cf. [ILLUSTRATION FOR FIGURE 3 OMITTED]).

Local population fluctuations are, on average, slightly synchronized within the metapopulation of M. bicolor. The median Spearman rank correlation coefficient between pairs of local populations is 0.19 (n = 780). However, the degree of spatial synchrony in the dynamics varies considerably, and many populations fluctuate out of phase [ILLUSTRATION FOR FIGURE 4 OMITTED]. There is no relationship between correlation coefficients and distance between pairs of local populations (Spearman rank correlation: [r.sub.S] = 0.042, n = 780, P = 0.24, NS).

Local extinctions occurred once on 17 habitat patches during the study period. Between one and six extinctions were recorded annually. Most extinctions occurred in 1992. No extinctions were observed on 80 patches that were occupied by M. bicolor. The number of patches that remained empty was 18.

Both patch area and habitat heterogeneity independently affected extinction probability (Table 2). Local extinctions occurred on patches that were either particularly small or relatively homogeneous. There was an apparent compensatory effect between habitat heterogeneity and patch area on population persistence [ILLUSTRATION FOR FIGURE 5 OMITTED]. There were examples of both extinctions occurring on quite large patches that were homogeneous, as well as local populations that managed to survive on very small, but heterogeneous patches. Patches where extinctions occurred were not more isolated than patches with extant populations (Table 2).


Population variability and extinction risk

It has been suggested that the risk of extinction is greater for populations whose densities are subject to large variation through time than for populations with low temporal variability (MacArthur 1972, Leigh 1975, 1981, Wright and Hubbel 1983, Diamond 1984, Pimm 1991). Several attempts have been made in order to test this hypothesis. Some authors find support for the variability hypothesis (e.g., Karr 1982, Wright and Hubbel 1983, Pimm et al. 1988, Forney and Gilpin 1989, Bengtsson and Milbrink 1995) while others do not (e.g., Bengtsson 1989, Schoener and Spiller 1992, Tracy and George 1992). As illustrated by Schoener and Spiller (1992), it is possible to get both positive and negative relationships between extinction probability and temporal variability of population size, depending on whether or not zeros are included in the calculations of the measure describing population fluctuations. In my opinion, the only way to avoid this statistical ambiguity is to consider only the part of trajectories prior to the extinction event that does not include zeros. Among the studies mentioned, only three have used this approach (Bengtsson 1989, Forney and Gilpin 1989, Bengtsson and Milbrink 1995).
TABLE 2. The influence of habitat heterogeneity (SD-hue), patch
(hectares) and interpatch distance (metres) on the binary response
variable describing local extinction probability (E). Habitat
patches where local extinctions of Metrioptera bicolor occurred (E
1) during the years 1989-1994 are compared with occupied patches
where no extinctions were observed during the same period (E = 0)
using multiple logistic regression analysis (n = 97).(*)

Variables   coefficient    1 SE    [[Chi].sup.2]            P value

Intercept      3.05       1.06          8.36       [less than]0.01
SD-hue        -0.027      0.0073       13.44       [less than]0.001
Area          -1.53       0.66          5.32       [less than]0.05
Distance       0.0015     0.0033        0.21                  0.65

* Log likelihood chi-square = 44.79, df = 3, P = 0.0001. Concordant
= 86.7%, discordant = 5.8%, tied = 7.5%.

The present data from the M. bicolor system are too meager to enable a rigorous direct test of the variability hypothesis but, nevertheless, my results are clearly in accordance with that hypothesis. It is obvious from this study that habitat heterogeneity can influence both the temporal variability of local population size and the probability of local extinctions. Generally, temporal variation of local population size may be interpreted as an indicator of mechanisms that affect the risk of extinction, rather than as a direct predictor of extinctions, as had been suggested earlier (Leigh 1981, Wright and Hubbel 1983).

Several factors are expected to increase both population variability and extinction risk simultaneously: (a) interpatch dispersal (Horn 1983, Gonzales-Andujar and Perry 1993, Hanski and Woiwod 1993), (b) interspecific competition (Bengtsson and Milbrink 1995), (c) environmental stochasticity (Leigh 1981), which apparently can be modified by habitat heterogeneity, and (d) population size, alternatively patch area, by affecting the impact of demographic stochasticity (Wright and Hubbel 1983). I have not considered interspecific competition in this study. However, among the investigated factors, I find at least environmental stochasticity to be an important determinator of both population variability and extinction risks in the Swedish metapopulation of M. bicolor.

Interpatch dispersal rates are known to be very low in the metapopulation of M. bicolor (Kindvall and Ahlen 1992, Kindvall 1993b). Therefore, it was not surprising that no relationship was found between interpatch distance and population variability. It is also clear that interpatch dispersal does not affect the probability of local extinctions, i.e., there is no apparent rescue effect (cf. Brown and Kodric-Brown 1977) in the metapopulation of M. bicolor.

Habitat heterogeneity and population dynamics

For species that are susceptible to weather fluctuations, habitat heterogeneity becomes an important factor that determines the relative strength of environmental stochasticity acting on different local populations, and thus the extinction risk. This notion is clearly supported by my data on M. bicolor as well as by the studies of Ehrlich and co-workers (e.g., Dobkin et al. 1987, Ehrlich and Murphy 1987, Weiss et al. 1988).

Topography is a crucial factor determining long-term survival of local populations of threatened butterfly species, Euphydryas editha bayensis, in the San Francisco Bay area of California (Weiss et al. 1988). Extinctions have been observed on large habitat patches that lack topographic heterogeneity (Ehrlich and Murphy 1987). Topographically diverse patches are favorable for larval survival and growth in any weather regime. North-facing slopes have the best local climate during most years, while south-facing slopes are best during wet years.

There is almost no topographical variation among patches within the metapopulation of M. bicolor. However, the variation of soil composition and vegetation types seems to give rise to effects on the population dynamics of M. bicolor similar to those imposed by topography on the butterfly system. Dense, tall vegetation (habitat type 4) is most favorable for survival during dry years (Kindvall 1995), while low grassland (habitat type 3) is as suitable for M. bicolor as tall grassland during most years (Kindvall and Ahlen 1992). During wet seasons, sandy soils with only sparse vegetation (habitat types 1-2) are expected to give the best opportunities for survival and growth of nymphs, because this habitat will probably dry up much faster than the other grassland habitats after a rainfall, leading to higher local temperatures. Whether this is true or not has yet to be confirmed.

There are not many studies dealing with the hypothesis that populations should be less variable in size on heterogeneous patches than homogeneous patches. However, at least a few studies have shown that habitat quality changes in relation to weather conditions. For example, the Snail Kite, Rostrhamus sociabilis, used other habitats than usually preferred during a severe drought in 1981, and hence was able to survive quite well (Takekawa and Beissinger 1989).

Spatial synchrony

Because spatial synchrony in the local population dynamics of M. bicolor was not affected by interpatch distance, it is reasonable to assume that regional stochasticity, rather than migration, is responsible for the observed levels of synchrony (cf. Hanski and Woiwod 1993). The degree of spatial synchrony in this study seems to be lower than reported for other insect taxa (e.g., Thomas 1991, Hanski and Woiwod 1993), possibly because most habitat patches are heterogeneous enough to counteract the influence of environmental stochasticity on local population dynamics. It is likely that local population dynamics of M. bicolor would be much more synchronous if all patches were equal. This idea is supported by the fact that most (75%) of the investigated local populations of M. bicolor decreased in size between the years 1991 and 1992, as a result of extremely dry conditions in 1992. However, populations living on patches dominated by short vegetation decreased more than those in patches dominated by taller vegetation (Kindvall 1995).

Species preservation

We have seen that the impact of weather on population dynamics can be reduced on habitat patches consisting of several types of habitats with different local climate. The risk of correlated extinctions in a metapopulation is expected to be diminished if habitat patches are sufficiently heterogeneous. Thus, one way to make a metapopulation more immune to regional stochasticity is to preserve a high diversity of habitats both within and between patches. It is important that the various habitats are available for the animals within a fairly limited spatial scale, so that the animals can easily move between them. I believe it is a better strategy to give priority to patches with a high diversity of habitats, rather than to preserve as large patches as possible of the habitat that has proven to be most productive in normal years.


The research was supported by the Swedish Council for Forestry and Agricultural Research and World Wildlife Fund-Sweden. I thank Annika Kindvall and Susanne Godow for helping me with the field work, Ingemar Ahlen and anonymous referees for comments on the manuscript, and Jan Bengtsson for discussions about population variability in general. I also appreciated the assistance of Bo Nordin at the Centre for Image Analysis in Uppsala.


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Author:Kindvall, Oskar
Date:Jan 1, 1996
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