Landsat 5 TM image (Germany, Saxony-Anhalt, path 194, row 24, acquisition date: May 8^th, 2011).

<p>The TERENO study locations are indicated by black frames (FBG: Friedeburg, GFH: Greifenhagen, HAR: Harsleben, SIP: Siptenfelde, SST: Schafstaedt, Wan: Wanzleben), and trapping points are given as yellow filled circles.</p

Modelling patterns of pollinator species richness and diversity using satellite image texture

<div><p>Assessing species richness and diversity on the basis of standardised field sampling effort represents a cost- and time-consuming method. Satellite remote sensing (RS) can help overcome these limitations because it facilitates the collection of larger amounts of spatial data using cost-effective techniques. RS information is hence increasingly analysed to model biodiversity across space and time. Here, we focus on image texture measures as a proxy for spatial habitat heterogeneity, which has been recognized as an important determinant of species distributions and diversity. Using bee monitoring data of four years (2010–2013) from six 4 × 4 km field sites across Central Germany and a multimodel inference approach we test the ability of texture features derived from Landsat-TM imagery to model local pollinator biodiversity. Textures were shown to reflect patterns of bee diversity and species richness to some extent, with the first-order entropy texture and terrain roughness being the most relevant indicators. However, the texture measurements accounted for only 3–5% of up to 60% of the variability that was explained by our final models, although the results are largely consistent across different species groups (bumble bees, solitary bees). While our findings provide indications in support of the applicability of satellite imagery textures for modeling patterns of bee biodiversity, they are inconsistent with the high predictive power of texture metrics reported in previous studies for avian biodiversity. We assume that our texture data captured mainly heterogeneity resulting from landscape configuration, which might be functionally less important for wild bees than compositional diversity of plant communities. Our study also highlights the substantial variability among taxa in the applicability of texture metrics for modelling biodiversity.</p></div

Model-average estimates (EST) of scaled test predictors of bee biodiversity represented by bee count (BC), Shannon diversity (SD), and species richness (SpR) using bumble bees (bb), solitary bees (sb) and all wild bees (nohb).

<p>The R² corresponds to the global model including all predictors that remained after model selection, while the R² of the Null model (R²_NULL) refers to the model including only the fixed effects control predictors. Δ = R²- R²_NULL. IMP = relative importance; SD = Standard deviation; n_model = number of models averaged; P = P-values. con2 = 2^nd order contrast; ent1 = 1^st order entropy; hom2 = 2^nd order homogeneity; NDVI_cv = coefficient of variance of the NDVI; rough = surface roughness; 100/1000 = 100 m or 1000 m scale.</p

Coordinates (site centroids) and characteristics of the six study sites as specified in a previous work [28].

<p>SN = semi-natural areas; CF = crop fields; For = forest; GL = grassland.</p

Landsat 5 TM image (Germany, Saxony-Anhalt, path 194, row 24, acquisition date: May 8^th, 2011).

<p>The TERENO study locations are indicated by black frames (FBG: Friedeburg, GFH: Greifenhagen, HAR: Harsleben, SIP: Siptenfelde, SST: Schafstaedt, Wan: Wanzleben), and trapping points are given as yellow filled circles.</p

Overview of predictors used in the averaged LMs.

<p>1^st = first order texture; 2^nd = second order texture; bb = bumble bees; cv = coefficient of variance; con = contrast; ent = entropy; hom = homogeneity; nohb = all wild bees; rough = roughness; sb = solitary bees; BC = bee count; SD = Shannon’s diversity; SpR = Species richness.</p

The metrics generated in the study as measures of spatial heterogeneity.

<p>The metrics generated in the study as measures of spatial heterogeneity.</p