The interplay of various sources of noise on reliability of species distribution models hinges on ecological specialisation

<div><p>Digitized species occurrence data provide an unprecedented source of information for ecologists and conservationists. Species distribution model (SDM) has become a popular method to utilise these data for understanding the spatial and temporal distribution of species, and for modelling biodiversity patterns. Our objective is to study the impact of noise in species occurrence data (namely sample size and positional accuracy) on the performance and reliability of SDM, considering the multiplicative impact of SDM algorithms, species specialisation, and grid resolution. We created a set of four ‘virtual’ species characterized by different specialisation levels. For each of these species, we built the suitable habitat models using five algorithms at two grid resolutions, with varying sample sizes and different levels of positional accuracy. We assessed the performance and reliability of the SDM according to classic model evaluation metrics (Area Under the Curve and True Skill Statistic) and model agreement metrics (Overall Concordance Correlation Coefficient and geographic niche overlap) respectively. Our study revealed that species specialisation had by far the most dominant impact on the SDM. In contrast to previous studies, we found that for widespread species, low sample size and low positional accuracy were acceptable, and useful distribution ranges could be predicted with as few as 10 species occurrences. Range predictions for narrow-ranged species, however, were sensitive to sample size and positional accuracy, such that useful distribution ranges required at least 20 species occurrences. Against expectations, the MAXENT algorithm poorly predicted the distribution of specialist species at low sample size.</p></div

Framework demonstrates the factors that need to be considered depending on the characteristics of species specialisation.

<p>The number represents the minimum sample size of occurrences that is needed to model the SDM according to the positional accuracy of species occurrences and algorithm type.</p

Result of the linear model analysis investigating determinants of area under the receiver operating characteristic curve (AUC) values.

<p>Exponentially transformed AUC values were modelled as a function of spatial resolution, SDM algorithm, positional accuracy, sample size, and species specialisation. Akaike Information Criterion (AIC) showed that the full model with interaction was the less parsimonious model with AIC = -66657.46.</p

True Skill Statistic (TSS) for the models fitted with precise and imprecise occurrences.

<p>The variation between the performance of SDMs fitted with precise and imprecise species occurrences with different sample size (x axis) using five different SDM algorithms (column-wise) for four species with difference specialisation levels (row-wise). Line colour represents the precision levels of the species occurrences. Solid lines represent low grid resolution, and dashed lines represent high resolution. Dotted line is the threshold value below which poor model performance is indicated.</p

Flow diagram explaining the study design used to answer the study questions.

<p>The first step uses the first two axes of PCA and a Gaussian distribution function to create the four virtual species by adjusting the standard deviation (S.D.) value according to the species specialisation level. The second step shows the modelling process for these four species using five modelling algorithms with different sample sizes and different levels of positional accuracy (one precise and three increasingly imprecise levels) and two raster resolutions (high and low). The third step shows the evaluation procedure for model prediction based on the spatial agreement (reliability) and statistical performance (Area Under the Curve AUC and True Skill Statistics TSS).</p

The agreement index.

<p>The spatial agreement between the predicted ranges with precise species occurrences and the predicted ranges with imprecise species occurrences for four different species at high and low grid resolutions–according to Overall Concordance Correlation Coefficient (OCCC) index. The y-axis is scaled from 0 to 1, where 0 means no agreement and 1 is 100% agreement. Solid lines represent low grid resolution and dashed lines represent high resolution. Line colour denotes the precision levels of the species occurrences, where the black line denotes precise species occurrence, the blue line denotes low imprecise, the green line denotes intermediate imprecise, and the red line denotes highly imprecise species occurrences.</p

The inter-quantile range of the standard True Skill Statistic (TSS) at high grid resolution.

<p>This plot shows the variation in model performance for four species (row-wise) with increasing the sample size (x axis) using five different SDM algorithms (column-wise). The dashed line represents the threshold line, where median values above this line indicate good performance.</p

Occurrences of plants in plots and seeds in dung piles of guava and passion fruit across elevation and suitability gradients in La Reserva (LR) and Cerro Fatal (CF) regions of Santa Cruz Island, Galapagos.

<p>Occurrences of plants in plots and seeds in dung piles of guava and passion fruit across elevation and suitability gradients in La Reserva (LR) and Cerro Fatal (CF) regions of Santa Cruz Island, Galapagos.</p

Plant species dispersed by Galapagos tortoises surf the wave of habitat suitability under anthropogenic climate change

<div><p>Native biodiversity on the Galapagos Archipelago is severely threatened by invasive alien species. On Santa Cruz Island, the abundance of introduced plant species is low in the arid lowlands of the Galapagos National Park, but increases with elevation into unprotected humid highlands. Two common alien plant species, guava (<i>Psidium guajava</i>) and passion fruit (<i>Passiflora edulis</i>) occur at higher elevations yet their seeds are dispersed into the lowlands by migrating Galapagos tortoises (<i>Chelonoidis</i> spp.). Tortoises transport large quantities of seeds over long distances into environments in which they have little or no chance of germination and survival under current climate conditions. However, climate change is projected to modify environmental conditions on Galapagos with unknown consequences for the distribution of native and introduced biodiversity. We quantified seed dispersal of guava and passion fruit in tortoise dung piles and the distribution of adult plants along two elevation gradients on Santa Cruz to assess current levels of ‘wasted’ seed dispersal. We computed species distribution models for both taxa under current and predicted future climate conditions. Assuming that tortoise migratory behaviour continues, current levels of “wasted” seed dispersal in lowlands were projected to decline dramatically in the future for guava but not for passion fruit. Tortoises will facilitate rapid range expansion for guava into lowland areas within the Galapagos National Park where this species is currently absent. Coupled with putative reduction in arid habitat for native species caused by climate change, tortoise driven guava invasion will pose a serious threat to local plant communities.</p></div

Potential distribution of guava and passion fruit on Santa Cruz Island.

<p>Derived from Maxent SDMs under current climate conditions, followed by future climate change scenarios for the years 2050 and 2070 (see text for details). Colours represent climatic suitability for the focal species. Dark red indicates higher climatic suitability and dark blue displays low suitability values.</p