No abstract

Uncontrolled, large fires are a major threat to the biodiversity of protected heath landscapes. The severity of the fire is an important factor influencing vegetation recovery. We used airborne imaging spectroscopy data from the Airborne Prism Experiment (APEX) sensor to: (1) investigate which spectral regions and spectral indices perform best in discriminating burned from unburned areas; and (2) assess the burn severity of a recent fire in the Kalmthoutse Heide, a heathland area in Belgium. A separability index was used to estimate the effectiveness of individual bands and spectral indices to discriminate between burned and unburned land. For the burn severity analysis, a modified version of the Geometrically structured Composite Burn Index (GeoCBI) was developed for the field data collection. The field data were collected in four different vegetation types: Calluna vulgaris-dominated heath (dry heath), Erica tetralix-dominated heath (wet heath), Molinia caerulea (grass-encroached heath), and coniferous woodland. Discrimination 1804 between burned and unburned areas differed among vegetation types. For the pooled dataset, bands in the near infrared (NIR) spectral region demonstrated the highest discriminatory power, followed by short wave infrared (SWIR) bands. Visible wavelengths performed considerably poorer. The Normalized Burn Ratio (NBR) outperformed the other spectral indices and the individual spectral bands in discriminating between burned and unburned areas. For the burn severity assessment, all spectral bands and indices showed low correlations with the field data GeoCBI, when data of all pre-fire vegetation types were pooled (R 2 maximum 0.41). Analysis per vegetation type, however, revealed considerably higher correlations (R 2 up to 0.78). The Mid Infrared Burn Index (MIRBI)had the highest correlations for Molinia and Erica (R 2 = 0.78 and 0.42, respectively).In Calluna stands, the Char Soil Index (CSI) achieved the highest correlations, with R 2 = 0.65. In Pinus stands, the Normalized Difference Vegetation Index (NDVI) and the red wavelength both had correlations of R 2 = 0.64. The results of this study highlight the superior performance of the NBR to discriminate between burned and unburned areas, and the disparate performance of spectral indices to assess burn severity among vegetation types. Consequently, in heathlands, one must consider a stratification per vegetation type to produce more reliable burn severity maps.

Climate change causes changes in the timing of life cycle events across all trophic groups. Spring phenology has mostly advanced, but large, unexplained, variations are present between and within species. Each spring, migratory birds travel tens to tens of thousands of kilometers from their wintering to their breeding grounds. For most populations, large uncertainties remain on their exact locations outside the breeding area, and the time spent there or during migration. Assessing climate (change) effects on avian migration phenology has consequently been difficult due to spatial and temporal uncertainties in the weather potentially affecting migration timing. Here, we show for six trans-Saharan long-distance migrants that weather at the wintering and stopover grounds almost entirely (∼80%) explains interannual variation in spring migration phenology. Importantly, our spatiotemporal approach also allows for the systematic exclusion of influences at other locations and times. While increased spring temperatures did contribute strongly to the observed spring migration advancements over the 55-y study period, improvements in wind conditions, especially in the Maghreb and Mediterranean, have allowed even stronger advancements. Flexibility in spring migration timing of long-distance migrants to exogenous factors has been consistently underestimated due to mismatches in space, scale, time, and weather variable type.

Over the past decades, spring arrival and passage of most short‐ and medium‐distance migrating birds in the Northern Hemisphere have advanced. Changes in spring temperature at the passage or arrival area have been most frequently shown to be related to these changes in spring migration phenology. In most studies, preliminary assumptions are made on both the spatial location and the specific time frame of the weather influencing spring migration phenology. We performed a spatially explicit time‐window analysis of the effect of weather on mean spring passage dates of nine short‐ and medium‐distance passerines. We analysed data from standardized daily captures at the Helgoland (Germany) constant‐effort site, in combination with gridded daily temperature, precipitation and wind data from the NCEP data set over a 55‐year period (1960–2014), across the whole of West Europe and North Africa. Although we allowed for a time window of any length at any location, nevertheless incorporating various measures to avoid spurious correlations, time windows at the likely wintering or spring stopover grounds were almost exclusively selected as the best predicting variables (96%–100% of identified variables). The weather variables at the wintering and stopover grounds explain up to 77% of the interannual variability in spring passage. Yet, the response of spring migration phenology to weather at the winter or stopover areas does not fully explain the observed trends. Spring migration phenology is, hence, strongly driven by weather at the wintering and stopover grounds, but additional mechanisms are needed to fully explain the advancement of spring migration. Our results also clearly show that previously illustrated correlations, or the lack thereof, between spring migration phenology and weather at the passage or arrival location are due to spatio‐temporal correlations in the weather data. This spatial mismatch might have led to false conclusions, especially the further away the wintering or stopover sites are.