general strengths and weaknesses
As we have shown, approaches to community-level modelling are numerous and highly varied. Differences between approaches occur at three levels: (i) the broad analytical strategy employed (Fig. 2); (ii) the type of spatial output produced (Fig. 1 and Table 1); and (iii) the exact analytical technique (algorithm) used to produce a given spatial output employing a given broad strategy (e.g. using generalized linear modelling vs. neural networks to model pre-classified community types). The existing literature on community-level modelling devotes very little attention to discussing the relative strengths and weaknesses of available options across these three levels. Published examples have focused on detailed differences between analytical techniques, rather than on differences between broad analytical strategies or types of spatial output. Thus readers of this literature may gain the impression that there is only one logical strategy for modelling biodiversity at the community level, and that the only choice that needs to be made concerns the exact analytical algorithm to be employed. This narrowness of focus on low-level differences between analytical techniques within a given strategy, rather than on high-level differences between alternative strategies, is consistent with a similar focus within the species-level modelling literature. We feel there is a real need for more broadly focused consideration of the strengths and weakness of major alternative strategies for modelling biodiversity, at both the species level and the community level. Decisions relating to the selection of a broad modelling strategy for any given study will probably have a much greater impact on the effectiveness of such modelling than decisions relating to the exact analytical algorithm employed.
No single approach to community-level modelling is likely to be optimal for all purposes and across all data sets. Different approaches may be better suited to different situations. The real challenge should therefore be seen not as one of searching for a single best approach, but rather of selecting the most appropriate approach in any given situation. Such decisions need to be informed by a good understanding of the respective strengths and weaknesses of available options. In Table 3 we present a first attempt at summarizing the relative strengths of the various community-level modelling approaches described earlier in this review. This evaluation considers only those approaches that retain and convey information on community composition.
Table 3. Relative strengths of different approaches to spatial modelling of community composition (approaches that predict species richness, or other macro-ecological properties, are not included). *Limited capacity, **moderately developed capacity, ***highly developed capacity
|Broad strategy||Specific approach||Strengths|
|1. Rapidly analyses very large numbers of species||2. Adds value to data for rare species by ‘pooling’||3. Addresses interactions between species||4. Allows individualistic species responses||5. Combines taxa surveyed at different sets of locations||6. Produces individual species’ distributions||7. Enforces congruence with known ‘communities’||8. Extrapolates beyond known ‘communities’|
|1. Assemble first, predict later||1a. Modelling of pre-derived community types||**||***||**||*||*||*||***||*|
|1b. Modelling of pre-derived species groups||**||***||**||*||*||*||**||**|
|1c. Modelling of pre-derived ordination axes||**||***||**||*||*||*||*||**|
|2. Predict first, assemble later||2a. Derivation of community types from modelled species’ distributions||*||*||*||***||***||***||*||**|
|2b. Derivation of species groups from modelled species’ distributions||*||*||*||***||***||***||*||**|
|2c. Derivation of ordination axes from modelled species’ distributions||*||*||*||***||***||***||*||**|
|3. Assemble and predict together||3a. Multiresponse modelling of multiple species||***||**||***||**||**||***||*||**|
|3b. Constrained ordination||***||***||**||*||*||**||*||**|
|3c. Constrained classification||***||***||**||**||*||**||**||*|
|3d. Modelling of compositional dissimilarity||***||***||**||**||**||**||*||***|
Each approach is first rated in terms of its capacity to analyse rapidly very large numbers of species (strength 1 in Table 3). Processing time may be an important constraint when dealing with data sets containing many hundreds or thousands of species. Approaches within strategy 2 (predict first, assemble later) require that a separate model be fitted and extrapolated for every individual species, and are therefore likely to be more time-consuming to implement than approaches within the other two strategies. Strategy 3 (assemble and predict together) offers the best potential to minimize processing time, by performing all analyses simultaneously within a single integrated process. A related weakness of strategy 2 is that species occurring infrequently in a data set may not be modelled reliably, or may not be modelled at all, because of insufficient records (strength 2). These species therefore contribute little to the subsequent derivation of community-level entities or attributes from the stack of modelled species’ distributions. This could be a significant problem for data sets in which a sizeable proportion of species is represented by very few records, particularly for conservation-related applications requiring an emphasis on the needs of rare species. The other two strategies (1, assemble first, predict later, and 3, assemble and predict together) use data from all species, no matter how infrequently recorded, in deriving community-level entities or attributes. By pooling data from all species these strategies may provide more power to detect shared patterns of environmental response across infrequently recorded species than can be detected by analysing the data for each of these species independently. Combining data in this way also provides more scope to address interactions between the distributions of different species, such as those resulting from competition or predation (strength 3).
Despite the drawbacks just discussed, strategy 2 has some unique strengths. By modelling species one at a time this strategy provides maximum opportunity, or flexibility, for each species to respond to the environment in an individualistic manner (strength 4). In contrast, approaches within the other two strategies place various constraints on the flexibility of species–environment relationships, for example by assuming that all species are responding to the same set of environmental gradients or that the functional form (shape) of these responses is the same across all species. Another unique strength of strategy 2 is the potential ability to combine species’ models derived from different biological survey or collection data sets (strength 5). Imagine, for a hypothetical region, that animal species have been surveyed at one set of sites while plant species have been surveyed at a different set of sites. Once models have been derived for individual species of plants and animals, the combined stack of extrapolated distributions can then be readily subjected to community-level classification, ordination or aggregation regardless of the fact that these distributions were originally modelled using different survey data sets (Scotts & Drielsma 2003). This capability also has benefits for the analysis of presence-only data from museum collections, in which data for different taxa are often derived from different sources, collectors or expeditions. In contrast, strategy 1 and, to a lesser extent strategy 3, assume that all species have been surveyed (i.e. recorded as present or absent) at the same set of sites. Another strength of strategy 2 is that modelled distributions of individual species are produced as a standard by-product, thereby complementing any community-level outputs derived from these models (strength 6). However, it should be noted that several approaches within strategy 3 also offer this capability.
The final two strengths evaluated in Table 3 are less closely aligned with any particular broad strategy and relate more to individual approaches within these strategies. The first of these strengths concerns the extent to which an approach constrains the community-level composition predicted for each unsurveyed grid cell to match the composition observed at one or more surveyed locations (strength 7). Such congruence is enforced most strictly by approach 1a (modelling of pre-derived community types) within strategy 1. This approach treats community types, generated by the initial classification of surveyed locations, as fixed entities. The subsequent modelling stage therefore forces each unsurveyed grid cell to be assigned to one of these known communities. Whether this enforced congruence is regarded as a strength, rather than a weakness, is likely to depend on the purpose of modelling and the nature of the data involved. For example, this might be viewed as a strength for an application requiring that mapped entities concord directly with pre-defined community types, and for which sufficient survey work has been conducted to detect all community types within the region of interest. However, where field sampling of communities is incomplete, or sparse, then enforcement of a one-to-one congruence between surveyed community types and modelled entities may instead be seen as a weakness. This situation may be better served by approaches with an ability to extrapolate beyond sampled communities, thereby predicting the occurrence of other community types, or species assemblages, in as yet unsampled environments (strength 8). Most approaches other than 1a (modelling of pre-derived community types) offer at least some capacity for such extrapolation.
The main conclusion to be drawn from Table 3 is that the appropriateness of a given approach for a given application will depend on the relative importance of various strengths and weaknesses in relation to both the purpose of the application and the type, quality and quantity of data involved. We illustrate this point using the application of community-level modelling to predict distributional shifts in biodiversity in response to climate change.
a special challenge: predicting responses to climate change
Palaeoecology provides clear evidence that community types of the past were different from those observed today (Huntley 1991; Ackerly 2003). Hence some community-level modelling approaches may face serious problems in predicting probable responses to climate change if they assume that the composition of communities will remain fixed over time, i.e. that the same community types will continue to exist and only the distributions of these types will change. Palaeoecology also provides evidence of an even more fundamental problem, i.e. the realized niches of some species appear to change over time (Ackerly 2003), probably as a result of changing interactions (e.g. competition and predation) between species (but see also Peterson et al. 1999). Any approach to predicting climate change responses that treats the currently realized niche of a species as if it were the fundamental niche is therefore also likely to encounter problems (Austin 1992).
All three modelling strategies described in this review have been used to predict community-level responses to climate change (Table 2). However, if future community types are likely to differ in composition from those observed today then strategy 1 will generate unreliable predictions. This strategy may provide useful evidence that current community types will not be able to persist at their present locations, but cannot reliably predict where similar communities (if maintained as such), or new community types, will be distributed in the future. Strategy 2 may be a better option in this regard (Guisan & Theurillat 2000) because it can account for individual responses of species, and may even allow different migration rates to be incorporated into the modelling of distributional shifts for different species. However, a potential problem with most existing implementations of this strategy is that current species’ interactions, and therefore the realized (as opposed to fundamental) niches of species, are assumed to remain constant into the future. This is unlikely and thus inclusion of species’ (and other biotic) interactions into the species-modelling stage of this strategy is expected to improve the rigour with which shifts in community composition are predicted. Leathwick et al. (1996) is the only example found so far where interactions were explicitly included in species’ models used to derive spatially explicit climate change projections at the community level. Strategy 3 has, to date, been employed much less frequently in predicting climate change responses. However, the potential for this strategy to provide an effective balance between the respective strengths of the other two strategies warrants further attention.