Fig 1.
Point-counts were carried out fortnightly at 80 different localities (black dots) within an area of ca. 6,000 km2 in southwestern Spain that encompasses permanent and temporary water masses within the provinces of Huelva, Cadiz and Seville. This wetland network includes the Tinto & Odiel marshes (1), the Doñana wetland complex (2) and Bay of Cadiz (3).
Fig 2.
Meteorological conditions in the study area.
Yearly anomalies (deviations from the long-term -1994 to 2016- mean) in the annual accumulated precipitation (blue bars, in mm) and the yearly-averaged daily mean temperature (black line, in °C). Red bar indicates our sampling year, i.e. the year in which our point-counts were carried out.
Table 1.
Predictors and control factors.
Complete list of predictors and control factors considered for modelling habitat associations in the waterbird community in the southwestern Spain wetland network.
Table 2.
List of species considered within guilds.
(T) denotes that the species is threatened according to BirdLife International categorization SPEC 1 (European species of global conservation concern), SPEC 2 (species with global population concentrated in Europe and with an unfavourable conservation status in Europe) and SPEC 3 (species not concentrated in Europe, but with an unfavourable conservation status in Europe).
Fig 3.
Averaged and smoothed regional projections of climatic variables in the study area (including all available regional models for the provinces of Seville, Cadiz and Huelva; sourced online from AEMET–Agencia Estatal de Meteorología–: http://www.aemet.es/es/serviciosclimaticos/cambio_climat; accessed on March 2017). Trends (2010–2100) for temperature and precipitation are shown for two different Representative Concentration Pathways–RCP–: RCP 8.5 (8.5 W·m-2) and RCP 4.5 (4.5 W·m-2). Changes in precipitation regimes are split by season. Horizontal dotted lines represent the % change (10%, 30% and 50%) we used for generating the different scenarios in our horizon scanning assessments.
Fig 4.
Waterbirds’ associations with environmental features.
Waterbird species (n = 69) are grouped into 7 different guilds. Lines connect waterbird guilds with those habitat variables driving their distribution. Those environmental features making up > 15% relative importance for BRT and included in the final GAMs for >80% of species within guilds are highlighted with bold lines. In the case of GAMs, red lines indicate negative effects on respective guilds, whereas blue lines indicate positive effects.
Table 3.
Relative importance of each variable as predictors of waterbird occurrence.
For GAMs, we show the percentage of waterbird species (n = 69) for which the predictor was included in the final models. For BRTs, we show the mean relative importance.
Fig 5.
Change in waterbird habitat suitability per guild.
We show the effect predicted for three different scenarios with changes of 10%, 30%, and 50% in the main environmental predictors (see Methods). Colours denote the guild and ellipses summarize the distribution of species per guild by considering the variance/covariance matrix. We show the Standard Ellipses corrected for small sample sizes (SEAc) using the R-package SIAR (Parnell et al. 2008). Numeration as in Table 2.
Fig 6.
Change in waterbird habitat suitability per life-history strategy and conservation status.
We show the effect predicted for three different scenarios with changes of 10%, 30%, and 50% in the main environmental predictors (see Methods). Colours denote waterbird life-history strategy (resident, breeding and wintering) and conservation status (solid lines and solid dots indicate non-endangered species). Ellipses summarize the distribution of species per life-history strategy and conservation status by considering the variance/covariance matrix. We show the Standard Ellipses corrected for small sample sizes (SEAc) using the R-package SIAR (Parnell et al. 2008). Numeration as in Table 2.
Fig 7.
A horizon scan exercise to anticipate conservation issues.
Percentage of species per guild whose conservation status may change; i.e. non-endangered species that will be negatively impacted by predicted environmental changes and endangered species that may benefit from the new Climate Change scenarios (CC).