#Example 1: Examining European Bird Species’ Range Losses and Climate Change
In the first example, we explored how climate change metrics relate with the loss of bird species’ ranges across Europe. For this purpose, we used distribution data for three European bird species (Milvus milvus , Cettia cetti , and Merops apiaster ) that were digitized from published resources. The data included species presence-absence at a 50x50 km spatial resolution for two time periods obtained from European Breeding Bird Atlas available for two time periods (EBBA1 in the 1980s (Hagemeijer et al., 1997) and EBBA2 in (Keller et al., 2020). Pixel level population losses were calculated by comparing species records between the two time periods.
Monthly time series of climate variables, such as maximum and minimum temperature and mean annual precipitation, were obtained from the updated version of the Climate Research Unit’s database. These climate variables covered the period between 1957 and 2017, including twenty years before the first atlas (EBBA1).
Monthly time series of climate variables including maximum and minimum temperature and mean annual precipitation were obtained from the updated version of the Climate Research Unit’s database (http://www.cru.uea.ac.uk). These climate variables covered the period between 1957 and 2017, including twenty years before the first atlas (EBBA1) on the assumption that species’ ranges respond to the long-term climate conditions.
The climetrics R package was used to characterize various dimensions of climate change between 1980 and 2017. We then tested whether and to what extent the climate change metrics explained species range losses.
Four machine learning algorithms were employed to characterize the relationship between the species loss (as the response variable) and various metrics of climate change (as the predictor variables), and then measure the relative importance of climate change metrics to ‘explain’ species range loss. The algorithms include Generalized Linear Models (GLM), Generalized Additive Models (GAM), Random Forests (RF), and boosted regression tree (BRT). We used the sdm R package (Naimi & Araújo, 2016) for modelling species distributions. In order to avoid biases to the parameter estimation and measure performance of the models, we used a bootstrapping resampling approach (e.g., Hastie et al., 2009) implemented in the sdm R package with 50 replications for each species and modelling method.
The fitted models were used to predict climate suitability loss over the study area (e.g., Naimi et al., 2022). For each species, considering the four modelling methods and 50 replications, a total of 200 projections of climate suitability for were obtained for each species considered. We then used an ensemble approach (Araújo & New, 2007) to combine these predictions into a single layer using a weighted averaging function implemented in the sdm R package. We also calculated the relative importance of each climate change metric in explaining climate suitability loss for each bird. The metrics included extreme climate events, standardized local anomalies, climate change velocity, trend in precipitation, trends in temperature, average temperature, and precipitation in spring on the assumption that species respond to spring weather conditions (e.g., Ahola et al., 2004; Kluen et al., 2017).
We used AUC (area under the curve of a receiver operating characteristic of extinction events) for evaluating model performance in predicting extinction events of the three birds considered (Fielding and Bell, 1987) (Fig.2).