Usefulness of species traits in predicting range shifts
Alba Estrada, Ignacio Morales-Castilla, Paul Caplat and ReganEarly
Appendix S1
A.Details of Table 1
To select the studies detailed in Table 1 we searched the following combinations of words in ISI Web of Science: i) “range filling” (30 references); ii) “range shift” and “traits” (38 references); iii) “range size” and “traits” (359 references); and iv) “extinction risk” and “traits” and “climate change” (111 references). We checked all papers of i) and ii); a random selection of 30 papers of iii), and the 20 most cited papers of iv). From these papers we selected examples that used traits as explanatory variables in analyses. We restricted exemplar studies to those that address terrestrial species (thus excluding fishes and marine taxa). We also added some references that did not result from the searches above, but we believed a priori to be important.
B. Predictive traits included in the seven categories
Below we detail the specific traits included in the seven categories and how they are interpreted in Table 1. Trait names are those used in the references of Table 1. For each trait we reference a paper in which the trait was examined. Each trait is included in a single category except migration, breeding behaviourand brain size which are in two categories each: migration is in dispersal and site (in)fidelity, breeding behaviour is in site (in)fidelity and avoidance of small population effects, and brain size is in ecological generalization and competitive ability.
- Site (in)fidelity:
Site (in)fidelityindicates the likelihood that an individual will emigrate away from the natal patch (high site (in)fidelity = high likelihood of embarkation). In the referenceswe examined, we only encountered traits related to site (in)fidelity being used in animal studies.
Traits included in this category are:
Migratory status[1, 2], migration distance[3], migratory flexibility[4], migratory behaviour (i.e. migrants vs non-migrants; or residents, short-distance migrants and long-distance migrants)[5], breeding behaviour (social vs territorial)[5].
For site (in)fidelity ‘+’ in Table 1 means that traits that correspond to an increased likelihood of leaving the natal location were found to have a positive relationship with the response variable. Thus, migrant and social species are assigned a negative sign for this trait category in Table 1.
- Movement ability:
Traits in this category correspond to the length of time or distance across which individuals can undertake movements outside the natal range.
Traits included in this category are:
Dispersal mode[1, 6, 7], dispersal distance [8], seed size/mass[7-10], fruit length[11], seed terminal velocity[10, 12], seed production[7], seed shed height[1], seed shed duration[1], seed spread rate [9, 13], gut survival probability [12], natal dispersal [14], migratory status[15, 16], migration distance[3], migratory flexibility[4],migratory behaviour (i.e. migrants vs non-migrants; or residents, short-distance migrants and long-distance migrants)[4],morphological traits related to movement ability (e.g. hooks on seeds, attachment potential to animal fur, size of wings in birds, Kipp’s distance in birds)[4, 6, 12, 17], flight rank [1], flight behaviour [1], length of the flight period [18, 19], mobility index [18], home range[20], type of locomotion [21].
A positive sign for this category in Table 1 indicates that high movement ability (i.e. movement over long distances) had a positive relationship with the response variable. Regarding animal groups, migrants tend to be better dispersers [22], so in this category migrants contribute positively to movement ability.
- Avoidance of small population effects:
Small population effects include Allee effects [23], genetic drift, and susceptibility to demographic or environmental stochasticity. These factors make it difficult for small populations to grow, and thus hinder population establishment.
Traits included in this category are:
Capacity to self-fertilize[8, 24], vegetative regeneration[8], population group size[25], social group size[25], breeding behaviour (social vs territorial) [5].
A positive sign for this category in Table 1 indicates that traits which help avoid small population effects, e.g. having vegetative regeneration or not living in groups, had a positive relationship with the response variable.
- Persistence in unfavourable climatic conditions:
This category includes traits that relate to a population’s capacity to survive during periods of unfavourable conditions, even if the population growth is zero or slightly negative.
Traits included in this category are:
Seed bank persistence[8], seed bank longevity index [7], resproutingafter fire[8, 26], sleep behaviour[27], hide behaviour[27], annual rhythm (hibernation)[1], longevityor lifespan [1, 13, 15].
Both sleep and hide behaviours (those including hibernation, torpor, aestivation, dormancy, and the use of burrows, chambers, tunnels, tree holes, and caves) are thought to allow species to ‘wait out’ short periods of unfavourable climate and increase persistence [27]. Species with longer lifespans have more opportunities to reproduce, which may allow breeding to occur when conditions are more amenable, so longevity is thought to increase persistence. A positive sign for this category in Table 1 indicates that ability to persist in unfavourable conditions had a positive relationship with the response variable.
- Ecological generalization:
Generalizationfavours the establishment success of introduced species [16, 28], and it is related to increased population trends [3].
Traits included in this category are:
Habitat breadth [1, 8, 16], diet breadth[1, 16, 29], trophic level[3, 29], food habits (i.e., herbivores, omnivores, insectivores) [21], soil preference (ordinal between dry to wet soils, or infertile to fertile soils) [30], light preference (ordinal between deep shade to full sunlight) [30],specialism index[10], specificity of larval host plant[18], tolerance to environmental variables[3, 13], use of open water [1], egg habitat [1], number of floristic zones[1], number of oceanic zones[1], brain size[29], daily rhythm[1], geographical range size[1, 10, 13], habitat shift since the Last Glacial Maximum[31], occupied area[32], latitudinal range boundaries[29, 33].
A positive sign for this category in Table 1 indicates that ecological generalism (e.g. broad diet, occupancy of several biomes) had a positive relationship with the response variable.
- Reproductive strategy:
This category is related to demography, fecundity and speed of life history.
Traits included in this category are:
Age at maturity[25], age at first breeding[5, 29],age of first flowering [8], reproductive frequency[8], growth rate[13, 32, 34], growth/life form[6, 13], fast-slow continuum [5], generation length[32], clutch/litter size[1, 5, 29], clutches/litters per year[1, 15, 29], fecundity[4, 5, 16], length of incubation period[5], fledgling period (number of days the young birds stay in the nest from hatching to leaving the nest)[5], inter-birth interval[20], gestation length[25], age at eye opening[25], weaning age[15], vertebrate body mass[1, 4, 17], vertebrate body size (head-body length or wing span)[20, 29], vertebrate neonatal mass[25].
A positive sign for this category in Table 1 indicates that traits that denote an ‘r’ strategy (e.g. species that reproduce early, have small body mass, and high offspring per year) had a positive relationship with the response variable.
- Competitive ability:
Having a low competitive ability makes newly established populations in a recently-colonised location susceptible to eradication due to competition with incumbent species (hindering establishment), and slows the growth of already-established populations (hindering proliferation).
Traits included in this category are:
Plant height[7, 8, 34],specific leaf area[8, 35], dominant canopy plant[12], brain size[5], population size[32], local abundance[26], population density[29], intra-generic species richness[31].
A positive sign for this category in Table 1 indicates that competitive ability had a positive relationship with the response variable. Intra-generic species richness indicates the number of similar and therefore potential competitor species, so we would assign a positive sign if the relationship between intra-generic species richness and the response variable were negative.
C. Species distribution models used in Box 3
To obtain the maps of the plants and birds represented in figure I we performed species distribution models. We used presence data of native European plants, mammals and breeding birds on 50 km x 50 km UTM grid cells [36-39] within Europe from -10º 9’ 23’’ – 30º 43’ 10’’ E and from 34º 59’ 30’’ – 70º 58’ 33’’ N. We modelled the distribution of each species with four climatic variables of the period 1961-1990: mean temperature of the coldest month, mean annual summed precipitation, mean annual growing degree days (> 5ºC), and a moisture summer index calculated as potential evapotranspiration divided by precipitation (mean values of the 6 months between April and September).Future climatic variables were obtained considering the global climate model CNRM-CM5 and the representative concentration pathway (RCPs) 4.5 for the period 2071-2100, i.e., projections that are recorded in the Fifth Assessment IPCC report (
An ensemble of models was generated for each species. The ensemble included projections with seven methods: generalized linear models (GLM), generalized additive models (GAM), generalized boosting models (GBM), classification tree analysis (CTA), artificial neural networks (ANN), flexible discriminant analysis (FDA), and surface range envelope (SRE) [40]. Models were calibrated for the baseline (1961–1990) using 80% random sample of the initial data andevaluated against the remaining 20% data, using the area under thecurve (AUC) of the receiver operation characteristic (ROC) and the true skill statistic (TSS) [41]. Projections were performed 10 times, each time selecting a different 80% random sample while verifying model accuracy against the remaining 20%. The evaluation statistics were used to consider thepossibility of exclusion of models in the ensemble approach on the basis of poor matching between predictions and observations. Here, models with AUC < 0.8 or TSS values < 0.6 would be removed from the analysis. For the final assessment, models were calibrated using 100% of the species distribution data as it has been shown that random removal of presence records adds a non-trivial amount of uncertainty in future projections [42]. Given the projections obtained with the seven models, we calculated an ensemble model for each species. The ensemble was based on a weighted mean probability of occurrence per species and per grid cellwith a proportional decay, where weights are obtained from the AUC and TSS obtained on the evaluation data [43]. Ensemble projections were transformed into suitable/unsuitable using the TSS as the binary method [44]. All models were run in R [45]usingdefault options of the biomod2 package [44].
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