Where do they go? The effects of topography and habitat diversity on reducing climatic debt in birds

Introduction

Faced with climate change, species can adopt and combine two strategies: maintaining its original geographic distribution by tolerance, acclimation, or evolutionary adaptation or to keep up with its original niche by tracking the variations in space (Parmesan, 2006; Lavergne et al., 2010) and/or in time (Root et al., 2003; Dunn & Winkler, 2010; Dunn & Møller, 2013). The fate of most species facing climate changes will therefore be determined by their ability to cope with local climate changes, which in turn depends on their capacity to adjust their physiology, phenology, and/or spatial distribution. Although resistance processes and rapid evolutionary adaptation have been documented for some taxa (Norberg et al., 2012), spatial tracking of climatic shifts seems to be a common strategy, and the distribution of many species appears to have already been subject to strong poleward and upward shifts (Hickling et al., 2006; La Sorte & Thompson, 2007; Tingley et al., 2012). However, while some movements are consistent with climatic shift (Chen et al., 2011; Pinsky et al., 2013; Poloczanska et al., 2013), others are not (La Sorte & Jetz, 2012; Zhu et al., 2012; Comte & Grenouillet, 2015).

The concept of climatic debt encapsulates this discrepancy (Kuussaari et al., 2009) and can be defined as the spatial lag accumulated by a species or a community when its spatial response is not consistent with the experienced climatic shift. At the species scale, several formal estimates of the lag between a spatial shift in climatic conditions and species’ range shifts have been calculated (Gillings et al., 2015; Lenoir & Svenning, 2015). One common approach compares the relative composition of cold- vs. warm-dwelling species in a local community [known as the community temperature index (CTI)] with the velocity of climatic shift (Devictor et al., 2008). The ratio between the spatial CTI gradient (°C^cti km⁻¹) and its temporal trend (°C^cti yr⁻¹) is used to generate an estimate of the velocity of change in the composition of a community (km yr⁻¹). Similarly, the velocity of isotherm climatic shift (km yr⁻¹) is a climate change metric calculated from the ratio between spatial (°C km⁻¹) and temporal (°C yr⁻¹) thermal gradients (Loarie et al., 2009). The direct comparison of climatic change and biotic responses can highlight whether the composition of a community is lagging behind an isotherm shift at the national (Devictor et al., 2008) or continental (Devictor et al., 2012a,b) scale. The CTI has been shown to be a reliable indicator of the re-composition of a community in response to local climatic variations (Lindström et al., 2013; Princé & Zuckerberg, 2014; Gaüzère et al., 2016). It has been applied to various groups such as birds, plants, butterflies, and fishes (Cheung et al., 2013; Roth et al., 2014; Zografou et al., 2014; Nieto-Sánchez et al., 2015; Savage & Vellend, 2015) and adopted as an indicator of climate change impacts on biodiversity by both governmental and nongovernmental conservation organizations in Europe (Van Swaay et al., 2010).

However, source of variability in climatic and community shifts may have been overlooked, due to the oversimplification of estimations (Ohlemüller, 2011; Dobrowski et al., 2013). Local variations in climate are both highly heterogeneous and dynamic at the regional (Walther et al., 2002; Burrows et al., 2011) and local (Gaüzère et al., 2015) spatial scales. The complex lattice created by latitude, longitude, elevation, landscape, and land use leads to high variability in the rate and direction of isotherm shifts in space and time (Loarie et al., 2009), and recent studies have shown that fine-scale climate variability may drive distribution dynamics (Early & Sax, 2011; Bennie et al., 2013; Lenoir & Svenning, 2015). In practice, any measure of climatic debt is highly dependent on the scale used to measure the spatial thermal gradients, and the addition of the spatial distribution of climate change velocity (rather than monotonic changes for a given area) is a promising approach that can help to downscale climate change pressure and responses, and estimate the speed and direction of climate characteristics (Ohlemüller, 2011). Adopting a spatial analysis that integrates the systematic quantification of climatic conditions and communities is expected to refine the climatic debt concept and improve its local relevance.

Although the concept of climatic debt has proved to be a useful yardstick to measure the delay of biodiversity responses on a large spatial scale (Visser, 2012), its magnitude and directionality have never been quantified on a local scale. The study of these factors in the context of a spatial shift of a community therefore represents a crucial step toward a better understanding of the key factors that determine the biodiversity responses to climatic variations (VanDerWal et al., 2012).

A few studies have compared the shifts in species’ distributions with climate change velocities (Pinsky et al., 2013; Comte & Grenouillet, 2015; Gillings et al., 2015), but none, to our knowledge, have also related velocities to landscape characteristics.

In the case of birds, the optimal biotic spatial shift (i.e., the shift required to pay off climatic debt) is probably limited by individual traits such as dispersal ability or ecological specialization (Anderson et al., 2009; Hof et al., 2012) and landscape particularities such as topography, composition, and structure. For instance, in France, alpine biogeographical domain is expected to shelter slower velocities, whereas faster shifts are expected to be seen in flatter regions such as the French Atlantic coast (Loarie et al., 2009).

On an even smaller scale, many mechanisms potentially mediate the spatial response. First, steep and complex topography promotes the efficient tracking of climatic shifts by reducing the distance between isotherms. Secondly, spatiotemporal variability in microclimates can create the gaps in climate paths (Early & Sax, 2011) and create the local climatic refugia (Lenoir et al., 2013). Conversely, an inhospitable matrix of heavily managed landscapes dominated by intensive agriculture or forestry is thought to make the colonization of new areas more difficult (Thomas et al., 2012). Other human imprints, such as roads or artificial areas, have been shown to increase the habitat fragmentation and affect the dispersal of birds (Andrews, 1992; Spellerberg, 1998). Therefore, the presence of natural ‘corridors’ or ‘stepping stones’ via which species can easily disperse and settle are thought to play a key role in the process of climatic niche tracking (Huntley et al., 2008). As more diversified landscape increases provide greater probability that a given species will find a suitable habitat, habitat diversity is expected to promote dispersion and settlement during range expansion (Vos et al., 2008). Overall, whether and how the local climatic debts are accelerated or mitigated by specific habitats, landscape structures, and/or composition are still largely unknown (Bennie et al., 2013; Feeley et al., 2014; Jarzyna et al., 2015).

This study combines a climate space analysis with the recent advances in community ecology in order to estimate the spatial lag between local climatic and community isotherm shifts. We use data from the French Breeding Bird Survey (FBBS) over an 12-year period (2001–2012) to estimate, visualize, and test (i) for congruence between the direction and velocity of spatial shifts in climate and community isotherms and (ii) whether spatial congruence between climatic and bioclimatic shifts is mediated by the landscape composition and configuration. The prediction is that greater congruence between climate and community changes will be found in areas with steep elevation gradients and in diverse and natural habitats.

Materials and methods

Community and climatic isotherm shifts

The analytical framework was inspired by the climatic velocity approach developed by Loarie et al. (2009). This framework aims to derive the local isotherm shifts from the ratio between temporal and spatial gradients (Loarie et al., 2009; Burrows et al., 2011) in CTI, and mean breeding season temperature. The framework is shown in Figure 1.

A moving window analysis estimated the spatial and temporal CTI trends and the mean breeding season temperature on a spatial continuum covering the whole study area. The analysis consisted in consecutively defining each plot as the center of an 80-km-radius circle encompassing at least 20 sites (average 88.6 ± 42, maximum = 207). This generated 2094 similarly sized spatial windows, each containing enough temporal and spatial repetition to estimate the reliable linear trends in variables. It is a straightforward way to summarize the spatial or temporal local trends emerging from regional dynamics (Gaucherel et al., 2007, 2008) and has previously been used successfully with this dataset (Devictor et al., 2010; Godet et al., 2015; Gaüzère et al., 2016).

Within each window, and for both CTI and the mean breeding season temperature, linear mixed-effects models consecutively estimated the temporal temperature trends as the linear trend in average temperature across time (the years 2001–2012 were considered as fixed continuous linear effects, while the site as the random factor effect) and the spatial thermal gradient as the trend in average temperature across the whole 80 km window (latitude and longitude based on the RGF93/Lambert-93 projection in meters were considered as fixed continuous effects and the years 2001–2012 as random factor effects). As both CTI and temperature can be expressed in °C, we extracted, for each model, their respective fixed-effect coefficients in order to estimate the temperature or CTI gradient over time (°C yr⁻¹), latitude (°C kilometer_latitude⁻¹), and longitude (°C kilometer_longitude⁻¹). Secondly, we derived biotic and climatic spatial isotherm shifts from the ratio of temporal trends to spatial (latitudinal and longitudinal) temperature or CTI gradients (°C yr⁻¹/°C km⁻¹ = km yr⁻¹). Isotherm shift vectors (temperature or CTI) were then defined for each window as the combination of its latitudinal and longitudinal components.

Results

The framework made it possible to map the local shifts in the climatic and biotic temperature of each window (Fig. 2). All isotherm shifts estimated for the period 2001–2012 exhibited a strong heterogeneity in space. Climatic isotherm shifts velocity showed a geographical pattern, with values ranging from 0 in mountainous areas to 125 km yr⁻¹ in the western and northern part of the territory; the national average velocity reached 11 km yr⁻¹. The spatial distribution of climatic shifts’ directions is highly heterogeneous, although the average direction of the shifts was heading north–northwest (Fig. 2a). CTI isotherm shifts were generally shorter than climatic ones and ranged from 0 to 112 km yr⁻¹ with a national average of 5 km yr⁻¹. The average direction of CTI isotherm shifts headed southwest. Both velocities and directions exhibited a strong spatial variability (Fig. 2b). The residual isotherm shifts resulting from the vectorial difference between climatic and CTI shifts were also spatially heterogeneous in both velocities and directions. However, the velocities were generally lower in the southeast and higher in the central part of the territory. The average residual shift was heading north and ranged from 0 to 187 km yr⁻¹ with an average velocity of 14 km yr⁻¹.

A univariate analysis of the influence of landscape descriptors on RDS (GLS models) revealed a linear relationship (Fig. 3). RDS decreased as habitat diversity increased (−0.197 ± 0.030, P < 0.0001), elevation gradient increased (−0.139 ± 0.023, P < 0.0001), and the extent of seminatural and forest habitats increased (−0.136 ± 0.033, P < 0.0001). Conversely, RDS increased as the extent of agricultural areas increased (0.135 ± 0.033, P < 0.001). Finally, no significant relationship was found between RDS and the extent of artificial areas and road density (0.05 ± 0.08, P = 0.5 and 0.059 ± 0.031, P = 0.056, respectively). It should be noted, however, that univariate analyses did not take into account colinearity of variables describing the composition and structure of the landscape (Fig. S2).

The results from the multivariate model that took into account all landscape descriptors (Table 1) showed that RDS decreased as habitat diversity (−0.181 ± 0.047, t = −3.85, P < 0.001) and steeper elevation gradients (−0.100 ± 0.030, P < 0.001) increased. No significant effects were found for other predictors.

The model selection procedure (based on testing for all possible fixed-effect structures nested within the global model including all landscape variables, and ranking them on the basis of AICc) showed that the averaged model (Table 2) that best predicted RDS included elevation gradients (−0.103 ± 0.023, P < 0.001) and habitat diversity (−0.140 ± 0.048, P = 0.004) with a relative variable importance (i.e., the sum of AIC weights across models including the variable, Burnham & Anderson, 2002) of 1. Natural areas (relative importance = 0.49), agricultural areas (relative importance = 0.30), and artificial areas (relative importance = 0.14) were also retained in the averaged model, but their effects on RDS were nonsignificant (see Table 2). Hierarchical variance partitioning performed on a linear model that incorporated all landscape descriptors and longitude–latitude coordinates as covariates (see Fig. S3) confirmed that the independent effect of elevation gradient explained the most RDS variability (10% of independent explained variance), followed by habitat diversity (6%, see Fig. S3). Supplementary analyses of a dataset restricted to the sites situated below 800 m (see Tables S4a–c and Fig. S4) showed that habitat diversity became the only predictor that reduced the local climatic debt (−0.12 ± 0.05, t = −2.09, P = 0.03).

Discussion

This study examined the impact of local temperature shifts in France in the period 2001–2012 on the estimated composition of the bird community at the same sites and during the same period. We estimated the local climatic debt of bird communities, and further analysis investigated whether it was mediated by landscape characteristics. The average velocity of CTI isotherm shifts were surprisingly close to the national-scale shift in CTI reported in France by Devictor et al. (2008), which found a northward shift of 91 ± 11 km for 1989–2006 (i.e., 5.35 km yr⁻¹).

Unlike the earlier work, however, here the average shift was not expected to be oriented northward, as the national mean spring temperature did not increase significantly between 2001 and 2012 (Gaüzère et al., 2015). Consequently, there was no overall, large-scale directional change in CTI. Instead, the local temporal variations were particularly marked in the southeast (see Fig. S5). On the national scale, the standard deviation of the mean velocity was high, reflecting the high spatial heterogeneity shown in Figure 2. Specific climatic variations at the local scale also explained the high heterogeneity in the direction and magnitude of isotherm shifts. Climate change is highly variable in both space (Ackerly et al., 2010; Burrows et al., 2011) and time (Karl et al., 1995), particularly in the period and locations considered in our study (Gaüzère et al., 2015). This climatic variability is exacerbated by the topographic determinant of climatic velocity, resulting in highly divergent vectors between contiguous local areas (Dobrowski et al., 2013). This complex mosaic is likely to be crucial for understanding the variability in spatial responses observed between species and areas (Ackerly et al., 2010; Tingley et al., 2012; Pinsky et al., 2013).

These results confirm the previous findings that document the fine-scale sensitivity of species’ range shifts to climate change for various taxa (Early & Sax, 2011; Bennie et al., 2013; Lenoir et al., 2013). The complex spatial patterns found here offer support for the argument that a more nuanced evaluation of spatial variability in climatic and biotic shifts is necessary to properly describe the responses of biodiversity to climate change – in contrast to the oversimplified descriptions of uniform poleward shifts (Lenoir & Svenning, 2015). A promising avenue for such analyses lies in quantitative estimates of the local delay and direction of biotic responses to climatic shift. To the best of our knowledge, only a few studies have compared the climatic shifts with species-scale biotic responses (VanDerWal et al., 2012; Pinsky et al., 2013; Comte & Grenouillet, 2015; Ash et al., 2016). For instance, Gillings et al. (2015) modeled the species’ distribution to estimate the direction of British breeding birds’ range centroid shift, but did not find a consistent correlation with the spatial shift of various climatic variables. One of the main hypotheses that could explain this discrepancy lies in the availability and distribution of suitable habitats that foster species’ dispersal and colonization, and which probably partially drive the climate-related species’ range shifts at the local scale in the short term (e.g., Hill et al., 1999). Beyond its ability to highlight the fine-scale heterogeneity of climatic and biotic isotherm shifts, our framework also made it possible to test the specific predictions regarding landscape characteristics that may underlie the ability of communities to track the local climatic changes.

In a national-scale analysis back-crossing the temporal and spatial CTI gradient, Devictor et al. (2008) defined climatic debt as the accumulated delay in species’ spatial responses to temperature change, which could be attributed to an inability to track the climatic change. Here, we estimated a local climatic debt over the entire country, allowing us to test for the effect of landscape characteristics expected to affect the ability of birds to track the climate change.

We found that increased elevation gradient and habitat diversity were associated with the reduced local climatic debt for French bird communities. Elevation gradient is known to reduce the lag in biodiversity response to spatial climatic variations (Lenoir et al., 2008). Large-scale studies investigating the changes in community composition related to the climate change have most always assumed uniform spatial gradient in temperature. However, one expects the effect of a local change on biodiversity in temperature to be highly dependent on the topography. In this respect, Bertrand et al. (2011) have shown that plant communities responded differently in lowland and highland areas, with much faster response in highlands. Here, we integrated the local topography in the estimate of the local climatic debt (that we called RDS) to account for the spatial distribution of the local gradient in temperature. First, steep topography and rough terrain create significant temperature variations over a small area, which decrease the velocity of climatic change (Loarie et al., 2009) and so the climatic debt. Secondly, complex topography shelters more diverse microclimates for species within a close vicinity, which in turn are likely to lower risks of population decline due to gradual or violent climatic variations (Luoto & Heikkinen, 2008). Finally, the diversity of climatic conditions in mountainous areas increases the local co-occurrence of both cold- and warm-dwelling species, leading to biotic assemblages composed of a wider range of species’ thermal preferences (Lenoir et al., 2013). For example, alpine plant communities have been found to track the climate warming more quickly than the lowland assemblages (Bertrand et al., 2011). As mountainous ecosystems are particularly vulnerable to climate change (Nogués-Bravo et al., 2007), the mitigation of climatic debt due to elevation gradient is good news. However, steep elevation gradients are spatially limited, and mountaintop extinction will inevitably follow accelerating temperature increases (Walther et al., 2002; Colwell et al., 2008).

Our analysis also provides the reliable evidence for the positive effect of landscape diversity in mitigating the spatial lag between biotic and climatic isotherm shifts. Although the descriptor is very integrative, the diversity of habitats within a landscape is known to provide a mosaic of microhabitats and microclimates for species and communities to track the global changes. For example, studies based on range boundary adjustment in response to climate change have shown that the successful dispersal and establishment of the butterfly Pararge aegeria is positively related to habitat availability (Warren et al., 2001). While the ability of corridors and stepping stones to increase the ‘connectivity’ of landscapes is controversial, ‘landscape permeability’, defined as the ability of a given habitat mosaic to foster species’ dispersal into new, climatically suitable areas, is likely to partially explain the climatic debt patterns (Vos et al., 2008; Hole et al., 2009; Mawdsley et al., 2009; Hole & Huntley, 2011).

The thermal reshuffling of a community, represented by a given change in CTI, can result from two concomitant processes. First, the local extirpation and colonization of individuals in response to climate changes is affecting the occurrence of specific species. Secondly, the changes in local abundance can result from the changes in demographic processes such as reproductive success and survival without immigration or emigration events. Studies and supplementary analysis based on occurrences have previously shown that among these two processes at stake, extirpation and colonization events are likely to drive a large part of CTI dynamics (Devictor et al., 2008; Barnagaud et al., 2012; Gaüzère et al., 2015; Tayleur et al., 2016).

Beyond the specific dispersal abilities that limit the species’ range shifts (Brooker et al., 2007; Anderson et al., 2009; Sandel et al., 2011; Schippers et al., 2011), the probability that common bird species will become successfully established in a given area is likely to be influenced by the probability of finding a suitable habitat (Opdam & Wascher, 2004; Vos et al., 2008). Habitat diversity is therefore expected to enhance the community-level isotherm tracking. Moreover, greater spatial heterogeneity tends to support greater taxonomic, functional, and genetic diversity (Vellend & Geber, 2005; Devictor et al., 2010). These metacommunity processes enhance the adaptive variation pool, increasing the likelihood that an appropriate phenotype adapts to new conditions during the colonization events. In the same way, taxonomic diversity and functional diversity are expected to foster the environmental affinities, which provide new species’ assemblages that are better adapted to the particular environmental conditions.

Note that habitat diversity was, to some extent, related to the altitudinal gradient (see Fig. S2), which intrinsically affects the velocity of isotherms. Consequently, the observed relationships between habitat diversity and RDS could be considered as a spurious effect arising from our estimation of climatic and CTI isotherms. We therefore performed a supplementary analysis with a dataset limited to the sites below 800 m in elevation. Interestingly, habitat diversity was also found to have a significant effect (see Fig. S3). The elevation gradient was too limited for isotherm tracking of birds in lowland areas; however, the effect of landscape diversity was exacerbated. While the closely spaced isotherms that are found in mountainous landscapes intrinsically help birds to track the climatic change, this result suggests that a diverse mosaic of habitats could be even more crucial for climate tracking in plain ecosystems. Our results are clearly scale dependent. In particular, they refer to a circular window with a radius of 80 km, which is the result of a trade-off between the need for wide spatial coverage and the number of sites in each window. Although we are confident that the relationship between CTI and breeding season temperature is consistent over different spatial scales (radiuses ranging from 30 to 200 km, see Gaüzère et al., 2015), we argue that finer spatial resolutions provide a better understanding of the effect of habitat composition and configuration on climatic debt. However, spatial tracking of climatic shift is not the only response available for species facing climate change. Facing temperature changes, birds can indeed shift their phenology, migration strategy, physiology, or behavior to keep track with their climatic niche (Stenseth & Mysterud, 2002; Dunn & Winkler, 2010). Therefore, climatic debt could encompass both spatial delay in species’ dynamics and nonspatial responses of individual responses, particularly when local temperature changes are weak (Gaüzère et al., 2015).

Moreover, synergies are likely between climate and land-use dynamics because habitats can affect the shifts in species’ distribution ranges in response to the climate change (Warren et al., 2001). For instance, while we could not detect the specific effect of road densities and artificialized areas, human settlement and habitat fragmentation are probably influencing species’ or communities’ responses at finer scales (Andrews, 1992). More generally, Clavero et al. (2011) have emphasized that the spatial correlations between the particular biogeographical gradient of land use and thermal composition of community can emerge beyond the direct effect of climate on the community composition. At the species level, Barnagaud et al. (2012) have shown that habitat and thermal niches could be intrinsically related, casting some doubts on the relevance of interpreting community changes only in terms of a response to land-use or climate changes. Although the recent analysis on the local-scale congruency between temperature and CTI temporal changes showed that such bias is limited (Gaüzère et al., 2015), where, and to what extent, each of these processes contributes to the specific effect of landscape diversity on community is difficult to disentangle. It is even more likely that climate and land-use change and occupancy act jointly on species’ and communities’ responses (Hockey et al., 2011; Kampichler et al., 2012).

Accordingly, the development of our work lies in the quantification of the synergistic effects of climate and specific land-use changes (e.g., following landscape disturbance, forest recovery, or conservation plans) providing that high-enough-resolution data on habitat dynamics are available (Gaüzère et al., 2016; Tayleur et al., 2016).

Our results show that the local changes in the composition of bird communities are not perfectly consistent with the changes in breeding season temperature. However, these results were based on the entire bird community, and the findings might be different when considering the individual species. Species-specific responses to the climatic variation are inherent in their sensitivity to different aspects of climate, which can shift in different directions at the local level (Dobrowski et al., 2013; Gillings et al., 2015) and dramatically influence species’ responses (Nadeau & Fuller, 2015). Consequently, community-aggregated responses (such as CTI) are likely to reflect a limited set of species, which are particularly affected by a particular climatic change (breeding season temperature in this particular case). Further investigation of spatial shifts at the community level should be carried out on functional groups. These investigations should be based upon species-specific sensitivities to other aspects of climate, such as precipitation during egg laying or nest provisioning. Habitat and, more broadly, ecological preferences also discriminate the ability of a species to track the climatic shifts, and mediate the effect of these shifts on their distribution, notably through range contractions (Jetz et al., 2007; Hockey et al., 2011).

Earlier work has documented the greater ability of generalist species to track the climatic variations at the expense of more specialized species. Although our results suggest that habitat diversity reduces the spatial lag between biotic and climatic shifts, the effect is likely to be mediated by species’ specialization. For instance, forest habitat specialist species can track the climate change more easily in homogeneous, forested landscapes than in more diverse habitats (Opdam & Wascher, 2004). Therefore, an interesting extension of our work would repeat the analysis on species as a function of their habitat preferences. This would highlight synergies between the diversity/fragmentation and composition of the landscape on particular groups (such as forest-, agricultural-, or urban-dwelling birds).

Overall, our work pleads for the finer investigation of high-resolution and multidimensional aspects of global changes and their effects on the spatial distribution of biodiversity. We here downscaled the concept of biotic community isotherm shifts in order to test the ability of landscape characteristics to shape the climatic debt of French breeding birds. Coupling community, species, and population responses and even finer spatial resolution should allow us to better understand the ecological processes at play in the biodiversity tracking of climate changes.