Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances

Abstract

Although abiotic factors, together with dispersal and biotic interactions, are often suggested to explain the distribution of species and their abundances, species distribution models usually focus on abiotic factors only. We propose an integrative framework linking ecological theory, empirical data and statistical models to understand the distribution of species and their abundances together with the underlying community assembly dynamics. We illustrate our approach with 21 plant species in the French Alps. We show that a spatially nested modelling framework significantly improves the model's performance and that the spatial variations of species presence-absence and abundances are predominantly explained by different factors. We also show that incorporating abiotic, dispersal and biotic factors into the same model bring new insights to our understanding of community assembly. This approach, at the crossroads between community ecology and biogeography, is a promising avenue for a better understanding of species co-existence and biodiversity distribution.

Figure 1

Theoretical framework and model structure. The aim of the nested model structure is to represent the theoretical framework. The first model focuses on presence–absence only and is expected to primarily involve abiotic drivers due to physiological filtering and dispersal mechanisms due to dispersal limitation. The second model focuses on abundance when presence has been assessed and is expected to involve local-scale mechanisms, including abiotic and biotic community-scale effects and source-sink dynamics.

Figure 2

Contribution of neighbouring cells to the dispersal-based index. A kernel function is applied to weight species presences around each focal cell (sampled point, black dot). Pixels under distance d₉₉ contribute by short distance dispersal and pixels between d₉₉ and ldd contribute by long distance dispersal (see equations). The neighbourhood map displays the species presence (black) and absence (white) around the focal community. The pixel contribution map shows the weights of each pixel. The resulting map is then added up to obtain the potential seed rain, known as the dispersal-based index.

Figure 3

Comparison between model A and models AC, AD and ACD. Each bar represents the average difference across all repetitions between the predictive accuracy of model A and the models AC, AD and ACD. Accuracy was measured using the Hanssen-Kuipers discriminant (HK), which varies from 0 to 1 for perfect fit. The numeric values on the x-axis are the mean predictive accuracy of model A. The following abbreviations are used to name the species: AA = Abies alba, AG = Alnus glutinosa, AM = Arnica montana, BE = Bromus erectus, BS = Buxus sempervirens, CA = Cacalia alliariae, CF = Carex ferruginae, DG = Dactylis glomerata, DO = Dryas octopetala, EC = Euphorbia cyparissias, FP = Festuca paniculata, GS = Geranium sempervirens, KM = Kobresia myosuroides, LD = Larix decidua, PhA = Phragmites australis, PlA = Plantago alpina, PV = Polygonum viviparum, RG = Ranunculus glacialis, RF = Rhododendron ferrugineum, UD = Urtica dioica, VM = Vaccinium myrtillus.

Figure 4

Importance of variables. Each barplot represents the relative importance of each variable or group of variables. The relative importance of each abiotic variable was added together. Species abbreviations are the same as for Fig. 3. Non-significant variables are marked with n.s. (a) Importance of variables to explain presence-absence distribution (modelling step 1). (b) Importance of variables to explain abundance distribution (modelling step 2).

Figure 5

Effects of the different drivers on the abiotic niche for Bromus erectus. The abiotic niche space is represented by the first two axes (53% of inertia) of a PCA of the abiotic variables. (a) Realised niche. Predictions of model ACD. Left: density of predicted presences normalised by the number of sample plots within each grid cell. Right: third quartile of predicted abundance class within each grid cell. Low: < 5% cover; Medium: 5–25% cover; High:> 25% cover. (b) Left/right: Proportion of sources/sinks among predicted presences. Middle: abundances in source and sink plots. (c) Effect of biotic interactions. Left: density of predicted presences with co-occurrence indices equalling zero. Right: negative and positive effects of the biotic interactions.