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In relation to the type of end-point used in these matrix type models, most are generally based on abundance However, Power and Power (1995) argue that this is inappropriate for assessing population level risk posed by sublethal pollution Comparison of an individualbased modelling framework for brook trout (Salvelinus fontinalis) with a Leslie matrix approach resulted in critical differences between the two Power and Power (1995) argued that these occurred because of a lack of population size structure information in the matrix bases approach A signi cant problem with matrix-based models is that they treat all individuals within the population as the same They take no account of the genetic differences within the population despite the importance of population heterozygosity and genetic stochastic event to population survival However, the potential importance of genetic differences and their effects on sh population dynamics are now being explored using modelling.

(S-65) to simulate the historical limited out ow capacity and associated prolonged retention and release of surface water from the upper to the lower basin (Toth et al., 1997). Operating criteria for the water control structure will be based on the historic stage discharge relationship for the upper basin, which will reestablish continuous in ows and discharge pulses of comparable magnitude and seasonality to those of historic pulses (Figures 6-3 and 6-9). However, because of the need for some continued regulation of the upper lakes for ood control, model simulations indicate that restored discharge pulses may have a slightly shorter duration (mean duration of simulated restored discharge pulses = 220 days) than historical pulses. The linkage between reestablished discharge pulses and restoration of the structural and functional integrity of oodplain wetlands requires the reestablishment of the associated historic slope of the water surface pro le, which is presently regulated and stepped down in 2 m increments at each water control structure along the longitudinal course of the channelized river. The historic water surface pro le will be reestablished primarily by the back lling of 35 continuous kilometers of the ood control canal (Figure 6-10) and removal of two water control structures within this dechannelized reach (Koebel 1995). Disjunct remnant channels will be reconnected with new river sections (Figure 6-11), which will be carved

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For example, a simulation model (FITPOP) has been developed that indicates that subtle differences in genetic and population parameters can have signi cant implications on population size, tness and sensitivity (McKenna, 2000) Genetic issues are also relevant in PVA if, such as inbreeding depression, they affect population persistence This is particularly important for small and isolated populations or those that have low growth rates (Allendorf & Ryman, 2002) It is possible to account for the loss of vital rates as a consequence of inbreeding depression and loss of heterozygosity This has been quanti ed in several studies in relation to juvenile and adult survival and reproduction (Ralls et al, 1988; Jimenez et al, 1994; Balou, 1997; Kendall & Fox, 2002).

number of descriptors is not always easy. Since many descriptors may be redundant in the information that they contain, principal component analysis has been the standard tool for descriptor reduction, often reducing the set of calculated invariants. This is accomplished by a vector space description analysis that looks for descriptors that are orthogonal to one another where descriptors that contain essentially the same information are linearly dependent. For example, a QSAR algorithm was developed by Viswanadahn et al. in which a set of 90 graph theoretic and information descriptors representing various structural/topological characteristics of these molecules were calculated. Principal component analysis was used to compress these 90 into the 8 best orthogonal composite descriptors [34]. Often molecular descriptors do not contain molecular information that is relevant to the particular study, which is another drawback one faces in selecting descriptors for a QSAR model. Due to the enormous number of descriptors available, coupled with the lack of interpretation one has for the molecular characteristics they exhibit, very little selection of descriptors is made a priori. Randic and Zupan reexamined the structural interpretation of several well-known indices and recommended partitioning indices into bond additive terms [35]. Advances in neural network capabilities may allow for the intermediate steps of molecular descriptor reduction and nonlinear modeling to be combined. Consequently, neural network algorithms are discussed in greater detail in Section 3.4. Applications of QSAR can be found in the design of chemical libraries, in molecular similarity screening in chemical databases, and in virtual screening in combinatorial libraries. Combinatorial chemistry is the science of synthesizing and testing compound en masse and QSAR predictions have proven to be a valuable tool. The QSAR and Modeling Society Web site is a good source for more information on QSAR and its applications.

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