Studying individual variation in the wild is a cornerstone of evolutionary ecology research. This is because individual variation, determined by genetic and non-genetic sources, is the raw material for natural selection to operate on, and its interaction with the environment can induce evolutionary change over time by triggering shifts in genotype and mean phenotype at the population level [1
Animal phenology is an example of phenotypic change across time and space: In a meta-analysis spanning 203 species, Parmesan [2
] reported significant phenological advancement of spring events of 2.8 days/decade across the northern hemisphere. More specifically, Both et al.
] report earlier budburst, caterpillar emergence and hatching dates in five bird species over 17 years of data. Animal phenology is also found to substantially vary across space. For example, the breeding phenology of a passerine bird, the blue tit Cyanistes caeruleus
can differ by up to one month when populations are 24 km apart, and up to 10 days when 6 km apart [4
]. These differences in breeding time are attributed to the type of habitat blue tits use for reproduction: they breed earlier in deciduous oak forests (Quercus pubescens
), and later in evergreen oak (Quercus ilex
) forests [4
]. A better understanding of the nature of such phenological changes [7
], as well as their ecological, demographic and evolutionary consequences [8
] at a fine spatial and temporal scale, is thus of key interest to evolutionary biologists and conservation scientists alike.
Historically, most studies linked changes in animal phenology to population-centered environmental variables, such as changes in temperature for the study site [11
], daylight length [12
], or predominant vegetation type [13
]. This is because individual-based environmental predictors were believed to vary only minimally across individuals in a population, were difficult to extract, or both. Although the environment is an inherently multi-dimensional space, logistical constraints reduce environmental sampling on the ground to points in space and point in time estimates, thereby imposing limits to inference relevant at the level of a single organism or reproductive event. Moreover, coarse environmental predictors such as temperature rarely impact higher organisms such as birds directly, and variation in phenology across trophic levels is therefore not necessarily hard-wired in a linear fashion [3
Insectivorous passerine birds in temperate forests usually synchronize their timing of reproduction and offspring energy requirements with peak food availability [15
] -such as the availability of oak-dependent caterpillars in the case of blue tits [16
]. While caterpillar peaks are triggered by spring temperatures [20
], they also show substantial variation across deciduous and evergreen habitats [16
]. Blue tit reproductive peaks are dependent on photoperiod and temperature [12
], but also vegetation type [4
] and/or vegetation phenology [22
]. In Mediterranean habitats, the contrasted phenology of deciduous and evergreen oaks can in fact override the importance of temperature variation: thus, blue tit timing of breeding is strongly correlated with the phenology of the dominant vegetation, which was found to be a more robust predictor of the timing of breeding than temperature at the inter-population level [13
]. At the population level, co-variation of avian breeding time and vegetation phenology can also occur at surprisingly low spatial scales: using a 13 year dataset of blue tit and great tit breeding events in Oxfordshire, UK, Cole et al.
] demonstrated that the onset of egg laying was positively correlated with local vegetation green-up when using 250 m MODIS imagery across a 385-ha mixed deciduous woodland. The same study system was also investigated on the ground, where it was found that measures of phenology at very local scales –as low as 20 m around the nestbox –were the most important predictors of the timing of breeding.
While open-access archives of MODIS and Landsat data offer means to study phenological variation from past records, until now, individual-based studies have been hampered by low-resolution (250 m for MODIS) or low frequency (every 16 days in the case of Landsat) image acquisitions (but see [22
]). As a consequence, satellite-derived information on environmental heterogeneity in animal ecology studies is usually applied to population-level analyses ([24
], but see Cole et al.
Because of sensor availability and cost limitations, high resolution remotely sensed data is an underexplored resource for animal ecologists in the monitoring of environmental heterogeneity and phenology. The increasing availability of higher frequency and high-resolution imagery is therefore likely to unleash the potential of remotely sensed data for individual based studies. Moreover, birds, and especially bird species breeding in nestboxes, are particularly amenable to the application of remote sensing approaches when inferring environmental heterogeneity: indeed, nestboxes provide insight into the reproductive success of individually marked birds, yet are static in time and easily geolocated. This offers the potential to decouple genetic properties of individuals from their environment, and can be characterized for each nestbox—and in consequence, each reproductive event—in a study site.
Here, we took advantage of detailed vegetation ground data available as part of a long-term study of blue tits in the forest of La Rouvière, a 300 hectares large typical Mediterranean woodland [4
] to validate the potential of high resolution Sentinel-2 satellite sensors for animal ecology research. We combined ground data with imagery stemming from an experiment on SPOT 4 satellite (Take 5) experiment acquired from February till June 2013, which generated images at a resolution of 20 m and revisit time every five days for five months. The SPOT 4 (Take 5) experiment was to be used as a simulator of time series generated by the European Space Agency’s Sentinel-2 mission [26
], which will capture scenes every five days with 10–20 m resolution and generate imagery freely accessible to all from 2015 onwards.
In preparation for the fine spatial and temporal resolution imagery that will be available from Sentinel-2 sensors, we first asked whether the Normalized Difference Vegetation Index (NDVI, [14
]) derived from an analogous sensor, SPOT 4 (Take 5), accurately renders habitat heterogeneity observed on the ground in terms of oak composition and spatial autocorrelation. Second, we evaluated NDVI time series of phenological change in two key tree species for the reproductive biology of the Mediterranean blue tit: the deciduous downy oak Quercus pubescens
and the evergreen holm oak Quercus ilex
. Finally, by using one year of blue tit breeding data matching the SPOT 4 (Take 5) experiment, we tested the potential for associations between ground-collected oak composition, NDVI-derived vegetation signal and blue tit egg-laying date.