Adaptation and demography

Results from the IBM indicate that in situations where local adaptation exists and early survival depends on the phenotype; there is a greater probability of mortality in the early stages after planting takes place. In this IBM, it is possible for the planted populations to recover rather quickly from these intense mortality events (Figure 4.12).

99 Figure 4.12. Mean population size throughout the simulation period when juvenile mortality of planted trees is

selective and when other state variables are held at default values. Population recovery following decline is rapid. In reality, recovery would be constrained by exposure of very small populations to stochastic processes (Shaffer, 1981; Lacy, 2000; Willi and Hoffman, 2009). Stochastic processes may be natural catastrophes or high temporal environmental variability (Dale et al., 2001; Nabel et al., 2013; Botero et al., 2015), outbreaks of indigenous or exotic pathogens or herbivores (Woods et al., 2005; Ennos, 2015; Desprez-Loustau et al., 2016) or intense competition from other plant species (Kellomäki et al., 2001; Gómez‐Aparicio et al., 2011) ecological

interactions not included in the IBM.

Furthermore, in the IBM, unless population size declines to zero during the first five years (Table 4.4), the chance that at least one seedling establishes is 1 because annual mating events are implicit in the model. Even if only a single tree survives, pollen contribution from another population will ensure that a pool of seedlings will be established. From the pool of seedlings produced, seedlings with a smaller phenotypic mismatch from the current optimum have the greatest chance of survival. The effect of this is that there are no selective limits in the density-dependent natural recruitment phase. Furthermore, both male and female reproductive output is not related to fitness, does not vary from year to year and commences from an early age (5 years).

Table 4.4. Number of extinction events under all model replicates, arranged by planting strategy (max = 37500). Seed sourcing

strategy

Number of model replicates in which population size after 5 years is 0

Number of model replicates in which population size after 5 years is <5

Admixture 32 199

Composite 16 84

Local 12 43

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The IBM assumes a situation of random mating and even sexual allocation in individuals. Departures from either of these situations in a very small population in reality could lead to demographic stochasticity, reducing reproductive output and success, for example due to reproductive asynchrony caused by phenological differences (Ennos, 2003). The fact that reproduction and reproductive success is guaranteed provided there is as least one gap on the forest floor and at least one individual capable of dispersing seed into a gap likely combine to over-estimate the rate and ease by which natural regeneration can take place in reality. Recovery of the population from a very small initial size is possible within the IBM largely due to these two reasons, as well as the absence of stochastic events.

The IBM considers only additive genetic variation, with no genetic architecture and our inheritance model assumes free recombination. Similar allelic models which consider linkage show that strong linkage will constrain the efficiency of natural selection upon genetic variation and thus reduce likely rates of adaptation (Schiffers et al., 2013; Bourne et al., 2013).

Another consequence of the lack of genetic architecture is that there are no opportunities for genetic Allee effects (e.g. inbreeding depression) to emerge. Nonetheless, in a real-word setting with trees, genetic stochasticity is less likely to present a major problem than

environmental or demographic stochasticity for two main reasons. Firstly, small populations of highly fecund and predominantly outcrossing species which have the capacity for long distance dispersal of seed and/or pollen are unlikely to suffer from sustained inbreeding depression. Even very small, fragmented populations of fewer than ten trees are capable of producing highly genetically variable seed crops. Negative fitness consequences of

inbreeding are erased by selective purging of inbred individuals and genetic variation can be restored efficiently by distance migration of pollen and seed (Bacles et al., 2005; 2006; Hampe et al., 2013).

Secondly, forest tree planting schemes tend to be involve tens or hundreds of thousands of individuals, rather than the maximum of 1024 planted in the IBM. Genetic and demographic stochasticity in tree populations is likely to be much more sensitive to population size than environmental stochasticity or natural catastrophes, as the latter can similarly impact larger populations (Lande, 1993).

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4.4.3. Choosing a seed source in a changing climate

The purpose of this highly abstracted modelling exercise was to identify factors which may influence the suitability of different seed sourcing strategies, rather than to provide evidence for decision making in forest management. Results in realistic scenarios will be highly dependent on species biology, management objectives, landscape configuration and the magnitude and type of climate change, all of which will be highly context dependent, varying by region (Breed et al., 2013; Whittet et al., 2016b).

Nonetheless, several results are qualitatively useful. Firstly, adaptation is rapid when there are many spaces available for recruitment (Figure. 4.6), although slows down considerably when population size approaches carrying capacity. In a simulation of this type, mortality will necessarily hasten adaptation (Kramer et al., 2008; Kuparinen et al., 2010) because it generates more opportunities for contemporary natural selection to act upon recruits. This supports the concept of utilising disturbance based management in forest ecosystems

(Harvey et al., 2002; Brang et al., 2014; Lefèvre et al., 2014; Cavers and Cottrell, 2015; Fady et al., 2016), although sensible and context dependent limits to the magnitude of artificial disturbances imposed on forests are required. These limits should take into account population size, the ease by which natural regeneration occurs, resilience to environmental stochasticity at the population level and the delivery of other management objectives than adaptation to climate change. Additionally, increasing climatic variability caused by climate change may increase the frequency of such disturbance events without management

intervention (Dale et al., 2001).

The amount of adaptive change achieved, and the rate of juvenile mortality were both negatively influenced when the habitat phenotype was considered selectively important. If there is evidence that past adaptation to temperature regimes is much more important than to any other (temporally stable) aspect of the environment, demographic risks of using

proportions of non-local planting stock are lower than they would be otherwise (Aitken and Whitlock, 2013). For instance, when the habitat phenotype was not selectively important (hS

= 0), the difference in survival rate between composite and local provenancing was much

smaller than when habitat was considered selectively important (hS > 0) (Figure 4.9). If knowledge of adaptive variation is limited, habitat is heterogeneous and it is unclear whether non-climatic factors are of adaptive significance, predictive provenancing from a single population should be avoided. It will be safer, in this case, to assume that local adaptation does exist than to assume that it does not (Aitken and Bemmels, 2016).

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The dramatic population size reductions following hard early selection is couched in the assumption that populations show strong adaptive differentiation and a somewhat narrow climatic niche. This narrow local adaptation operates such that novel environments impose severe selective pressures on planted trees. The extent to which this reflects reality will very much depend on the geographical context and aspects of species biology. Local adaptation is common in plants, and perhaps especially in tree species with large ranges (Savolainen et al., 2007), but it is not ubiquitous (Leimu and Fischer, 2004). It will remain very difficult to empirically validate the extent to which composite or predictive provenancing would actually help forests adapt to climate change. A more plausible approach would be to reparametrize models with survival results from long term field provenance tests, as such data may be informative after 15-30 years (White et al., 2007), which is shorter than the generation time of most trees. If demographic risks can be quantified with empirical data, new models could then be applied to better understand whether predictive and composite provenancing strategies provide resilience to climate change whilst minimising risk of population collapse.

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