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Summary of PhD-thesis by Chris Klok

Summary of the PhD-thesis by Chris Klok, Wednesday 27th September 2000, Email:

A Quest for the Role of Habitat Quality
in Nature Conservation

In this thesis the importance of habitat quality for the persistence and conservation of endangered species is discussed. The studies presented incorporate several aspects of habitat quality that have a major impact on the population dynamics of the species under consideration.

In chapter 2 the importance of habitat quality and size on the persistence of populations is analysed, given stochasticity in demographic processes. In this chapter habitat quality is expressed in terms of R0 (the lifetime reproductive output of an individual), and habitat size is described by K (the number of territories in the population). The results show that for the common shrew Sorex araneus the population density is mainly influenced by the habitat size. The population extinction time, on the other hand, depends more on the habitat quality than on habitat size. In the common shrew the expected extinction time increases exponentially with the number of territories and more than exponentially with the lifetime reproductive output. Moreover, the expected extinction time is always more than twice as sensitive to changes in habitat quality than to changes in habitat size. These results are derived for (1) a uniform and contiguous habitat area, (2) no restrictions on settlement (occupation of territories by juveniles is random) and (3) habitat quality (R0) not correlated with its size (K). In the context of (1) a discontinuous area (heterogeneous or fragmented habitat), (2) non-random settlement (e.g. in a metapopulation context), and (3) correlation between R0 and K, the sensitivity of the expected extinction time to habitat quality even increases. In a more general setting, chapter 2 shows that irrespective of the species under consideration, the likelihood of extinction as a consequence of demographic stochasticity can be more effectively countered by enhancing the reproductive opportunities of its individuals, if this is feasible, than by increasing population size.

Chapter 3 shows that a permanent reduction in habitat quality, such as induced by copper, can have a strong impact on population performance. In this chapter the effects of sublethal levels of copper on the population growth rate of earthworms is assessed. Copper influences the energy budget of the individuals which results in a decline in their growth and reproduction, leading to a decrease in the population growth rate. Especially reduction of individual growth has a large impact on the population growth rate because it delays the maturation. The population dynamical model used to assess the impact of Cu predicts population extinction to occur in the range of 200 to 300 mg copper per kg soil. This result, applied to earthworms living in sandy loam soil, is in reasonable agreement with field data. To assess the actual state of a population under field conditions the model is also used to study the effects of copper on the composition of the population in terms of juveniles, subadults and adults. These effects on the population structure reveal that toxicants, that reduce the individual growth, induce change in the population structure only close to the point where the population goes extinct. Therefore, to inspect the viability of earthworm populations in field situations the population structure is not a good statistic.

The sensitivity analysis in chapter 4 shows that in earthworms the time spent in the adult stage has the highest impact on the population growth rate. This implies that stress factors delaying maturation of juveniles, and thus reducing the duration of the adult stage, given a maximum life span, can have a large impact on the population. The result contrasts with the commonly held view in risk assessment that reproduction is considered the most important life history aspect.

The results of chapter 5 demonstrate that persistence and population density in the barn owl, a resident predator that responds numerically to temporal variations in its prey abundance (voles), are more sensitive to years with low vole densities than to peak prey years. Although peak vole years increase the number of owls in the population, the lows bring their number down to such levels that the population cannot benefit from the ensuing peak year. This result is irrespective of the period of the fluctuations and whether the fluctuations are periodic or random. This conclusion also holds for open populations since voles fluctuate in synchrony over extensive areas. Therefore, conservation in the barn owl should be directed at improving the food conditions in the years with low vole densities. Also for other raptor species, that show a numerical response to their prey, it is expected that especially the bad years, with respect to food, determine the population performance. In chapter 6 the contributions of soil pollution, acidification and fragmentation on survival of the badger are explored since the species seems to be threatened by the combination of these factors in The Netherlands. Soil pollution with cadmium can result in a direct effect (kidney lesion), whereas copper induces an indirect effect (food shortage). Badgers are confronted with these risks by feeding on earthworms. Earthworms accumulate cadmium to high levels, which badgers deposit in internal organs such as the kidney. Copper induces decline in development and reproduction in earthworms leading to reduction in their population growth rate and population density. This decrease in earthworm density reduces the amount of food available to the badger. Soil acidification enhances the risks of kidney lesion and food shortage on the badger, because it increases the availability of these pollutants.

The results in chapter 6 show that the risk on kidney lesion and food shortage are substantial; kidney lesion occurs within two to four years, and food levels are reduced up to 10%. This implies that next to fragmentation also soil pollution and acidification can interact with fragmentation to increase traffic mortality since both intoxication (that increases energy requirements for detoxification) and food shortage can force the badger to roam larger areas in search of food.


This thesis emphasises the importance of habitat quality for the conservation of endangered species. As shown in chapter 2, the likelihood of extinction as a consequence of demographic stochasticity can best be countered by improvement of habitat quality. Also an enlargement of the habitat size increases the population persistence. However, increases in habitat size always have a lesser effect than a similar improvement in habitat quality. Populations of endangered species are often restricted to highly fragmented habitats of marginal quality. Management options aiming at the preservation of these populations should therefore include strategies to improve both habitat quality and habitat size. Compared to enlargement of the habitat area improvement in quality is more difficult to achieve. Habitat quality comprises many factors such as physical conditions, the abundance of resources, breeding sites etc. and its assessment requires thorough knowledge of the ecology of the species under consideration. Common practice in conservation is to classify habitat quality into categories like marginal, central, good, medium and bad (Gilpin & Soulé 1986). It is not always possible to improve both the quality and the size of the habitat. Hence it is important to understand in how far these habitat aspects can be interchanged with respect to their effect on population persistence. The fact that habitat quality always has a higher impact on the population persistence than size, suggests that these aspects are not equivalent and therefore not directly interchangeable. This implies that for example a management strategy that enlarges the area of an endangered population with habitat categorised as bad, may not lower the risk of extinction, but, on the contrary, enhance this risk. Conservation strategies should therefore focus especially on improvement of the habitat quality; if this is not feasible, alternatives must be chosen with caution. In The Netherlands a large number of rural areas are of marginal quality resulting from pollution with toxicants. There is a tendency to use these areas of questionable habitat quality for nature conservation (e.g. Ilperveld and Volgermeer in the province of Noord-Holland, De Venen in the western part of The Netherlands). Given the importance of habitat quality for the persistence of species, addition of these areas to existing reserves may be an erroneous strategy. Common practice in conservation is to assess the viability of field populations by monitoring population density. As shown in chapter 2 the population persistence can decrease drastically with decline in habitat quality whereas the population density remains more or less stable. This implies that the viability of populations is not reflected in the population density by definition. The viability of populations depends on both the number of individuals in the population and their probabilities to reproduce and survive. Chapter 2 emphasises that the likelihood of extinction is even more dependent on these probabilities to reproduce and survive, which can be summarised in the lifetime reproductive output (R0), than on the population number per se. As a measure of the viability of field populations these processes therefore are more informative than the population density. Moreover, the population density is a resultant of the reproduction and survival of the individuals, and strongly influenced by the dynamic interaction of the individuals with their environment (Hengeveld & Walter 1999). For example in a territorial species such as the common shrew, social interaction within the species for a limited number of territories restricts the number of individuals in the population. The common shrew is an annual species with a high reproductive output. As long as the individuals can replace themselves (tex2html_wrap_inline150) all territories remain occupied. Only if the outcome of reproduction and survival leads to values of R0 < 1 the population density is expected to decline. Given the fact that the viability of populations is not unambiguously reflected in the population abundance it follows that monitoring based on species density alone can be misleading. Therefore, the processes that determine the life time reproductive output R0, that is survival and reproduction of individuals, should be measured in addition to density (van Horne 1983).

Populations of endangered species often consist of small numbers. As a result of their low number, they are trapped in what is called the extinction vortex. This indicates that the combination of inbreeding depression, demographic stochasticity and genetic drift lead to positive feedback loops that make small populations even smaller (Caughley 1994). Management practice directed at conservation of these populations is confronted with the question how large the population size must be to escape the risk of extinction inherent in small numbers. The concept of the minimum viable population (MVP) size aims at resolving this question. The MVP size corresponds to the threshold number of individuals at which the population will exist in a viable state for a given interval of time under the risk of extinction as a result of demographic, environmental and genetic stochasticity, or catastrophes (Shaffer 1981). The MVP size is species-specific since the life history of a species can have a large impact on the outcome of the abovementioned stochastic events (Franklin 1980; Soulé 1980). Moreover, the MVP size will also depend on the temporal and spatial distribution of the habitat (Gilpin & Soulé 1986), and its quality. Since habitat quality has a drastic non-linear effect on the risk of extinction (see chapter 2) this factor has a large impact on the MVP size. As a consequence, the size of MVPs in areas of inferior habitat should be non-proportionally greater than those in good habitat (van Horne 1983). In case of temporal fluctuations in habitat quality, when populations are restricted to island-like habitats (Gilpin & Soulé 1986) or if the habitat quality fluctuates in synchrony over extensive areas (chapter 5), the MVP size should be based on the lowest habitat quality The above considerations on MVP size indicate that a single species-specific MVP size is of little practical use. To overcome this problem species-specific MVP size distributions seem to be a solution. However, the concept of MVP based on size fails since the likelihood of population extinction is more sensitive to the processes of reproduction and survival than to the population number. A better alternative would be to develop a statistic based on both the lifetime reproductive output and the population number.

Risk assessment

The objective in risk assessment is to protect ecosystems from detrimental effects of pollution. In The Netherlands, current methods in ecotoxicological risk assessment are mainly based on laboratory tests at the individual level using a limited number of species. From these single species tests critical toxicant concentrations are derived, beneath which the ecosystem is considered to be protected. The applied derivation method (Aldenberg & Slob 1993) assumes that the species-specific sensitivities to a certain pollutant of the species composing an ecosystem can be expressed by a log-logistic distribution. This log-logistic distribution is constructed if at least four No Observed Effect Concentrations (NOECs) are available, derived from single species tests of different taxonomic groups. From the distribution a Maximum Permissible Concentration (MPC) is derived at the toxicant level where less than a certain percentage (usually 5%) of the species are affected by the pollutant. This method has some serious restrictions since it implicitly assumes that (1) the sensitivity of the populations of each species correlates linearly with the sensitivity of its individuals, that (2) the sensitivity of organisms to toxicants measured under laboratory conditions, in which all circumstances are optimal with the exception of the toxicant, represents the sensitivity under field conditions, and (3) interactions among species are totally ignored.

An obvious, intermediate step to remedy these shortcomings is to evaluate toxic effects at the population level. Population dynamic models as illustrated in this thesis can integrate laboratory results on individuals and assess their population level consequences. Moreover, these models can approximate effects under field conditions by evaluating the effects on the population under different suboptimal environmental conditions. Also the impact of toxicants on interacting species can be assessed to arrive at the consequences at the community level. Models that interpret the effects of toxicants at the population level should be firmly based on the ecology of the species under consideration. Such models should exist of a submodel at the individual level and a submodel at the population level. The first model assesses the effects of toxicants on the individual life history parameters, i.e. growth, development, reproduction and survival. The second submodel at the population level performs a bookkeeping operation to track changes in life history of all individuals composing the population. At the individual level, the energy allocation in organisms changes when they are confronted with toxicants. These changes result from e.g. detoxification or decreased food intake (Kooijman & Metz 1984) and can become apparent in the individual life history as retarded growth and development, or reduced reproduction and survival. To analyse these reductions in a consistent way energy budget models such as the Dynamic Energy Budget model (DEB) (Kooijman & Metz 1984; Kooijman 1993; Kooijman & Bedaux 1996) are essential instruments. The DEB model makes it possible to correlate in a mechanistic way negative effects such as reduced individual growth and reproduction since they are likely to result from the same mode of action of the toxicant on the energy allocation in the individual (see chapters 3, 4 and 6). Given such changes in the individual life history, the impact of toxicants on the population performance can be assessed with discrete- or continuous-time structured models. These population dynamical models can be extended to incorporate the impact of toxicants in a food chain (e.g. chapters 5 and 6) and the impact of toxicants in combination with other stress factors (e.g. chapter 6). In The Netherlands many rural areas are polluted resulting from dumps of industrial and household waste and application of harbour sediments. Given the pollutant levels of these sites high ecotoxicological risks are expected which makes their use for agriculture, recreation or nature doubtful. Insight is needed in the actual risk posed by the pollutants on the local ecosystem. To assess site-specific risks of soil pollution, the model PODYRAS (Population Dynamical Risk Assessment) has been developed which is based on the earthworm models described in chapters 3, 4 and 6. Earthworms are central in the model since they play an important role in the soil ecosystems; they readily accumulate various chemical substances from the soil, constitute one of the central nodes in the food web, and have a large influence on soil processes (Edwards & Lofty 1977; Lawton 1994). The model is parameterised with data from laboratory bioassays on growth and reproduction of earthworms living in the polluted soils of the site. The model can be extended to assess effects in food chains (earthworm-badger, earthworm-godwit) and in the functioning of the soil by assessing the contribution of earthworms in soil processes like decomposition.


Aldenberg, T. & W. Slob 1993. Confidence limits for hazardous concentrations based on logistically distributed NOEC toxicity data. Ecotoxicology and Environmental Safety 25: 48-63.

Caughley, G. 1994. Directions in conservation biology. Journal of Animal Ecology 63: 215-244.

Edwards, C.A. & J.R. Lofty 1977. Biology of earthworms. Chapman and Hall, London.

Franklin, I.R. 1980. Evolutionary change in small populations. In: M.E. Soulé & B.A. Wilcox (eds.), Conservation Biology, an Evolutionary-Ecological Perspective. Sinauer, Sunderland, MA; 135-149.

Gilpin, M.E. & M.E. Soulé 1986. Minimum viable populations: processes of species extinction. In: M.E. Soulé (ed.), Conservation biology. The science of scarcity and diversity. Sinauer, Sunderland, MA; 19-35.

Hengeveld, R. & G.H. Walter 1999. The two coexisting ecological paradigms. Acta Biotheoretica 47: 141-170.

Kooijman, S.A.L.M. 1993. Dynamic Energy Budgets in Biological Systems. Theory and applications in ecotoxicology. Cambridge University Press.

Kooijman, S.A.L.M. & J.J.M. Bedaux 1996. The analysis of aquatic toxicity data. VU University Press, Amsterdam.

Kooijman, S.A.L.M. & J.A.J. Metz 1984. On the dynamics of chemically stressed populations: the deduction of population consequences from effects on individuals. Ecotoxicology and Environmental Safety 8: 254-274.

Lawton, J.H. 1994. What do species do in ecosystems? Oikos 71: 367-374.

Shaffer, M.L. 1981. Minimum population sizes for species conservation. BioScience 31: 131-134.

Soulé, M.E. 1980. Thresholds for survival: maintaining fitness and evolutionary potential. In: M.E Soulé & B.A. Wilcox (eds.), Conservation Biology, an Evolutionary-Ecological Perspective. Sinauer, Sunderland, MA; 151-171.

Van Horne, B. 1983. Density as a misleading indicator of habitat quality. Journal of Wildlife Management 47: 893-901.

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Bob Kooi
Mon Oct 23 14:54:58 MET DST 2000