Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models

Abstract While biological distributions are not static and change/evolve through space and time, nonstationarity of climatic and land‐use conditions is frequently neglected in species distribution models. Even recent techniques accounting for spatiotemporal variation of species occurrence basically consider the environmental predictors as static; specifically, in most studies using species distribution models, predictor values are averaged over a 50‐ or 30‐year time period. This could lead to a strong bias due to monthly/annual variation between the climatic conditions in which species' locations were recorded and those used to develop species distribution models or even a complete mismatch if locations have been recorded more recently. Moreover, the impact of land‐use change has only recently begun to be fully explored in species distribution models, but again without considering year‐specific values. Excluding dynamic climate and land‐use predictors could provide misleading estimation of species distribution. In recent years, however, open‐access spatially explicit databases that provide high‐resolution monthly and annual variation in climate (for the period 1901–2016) and land‐use (for the period 1992–2015) conditions at a global scale have become available. Combining species locations collected in a given month of a given year with the relative climatic and land‐use predictors derived from these datasets would thus lead to the development of true dynamic species distribution models (D‐SDMs), improving predictive accuracy and avoiding mismatch between species locations and predictor variables. Thus, we strongly encourage modelers to develop D‐SDMs using month‐ and year‐specific climatic data as well as year‐specific land‐use data that match the period in which species data were collected.

In the last decades, species distribution models (SDMs), which relate species occurrence locations with environmental predictors to estimate the probability of species occurrence to unsurveyed sites or to unsurveyed times (e.g., to predict response to climate change), experienced vigorous development at the intersection of ecology, biogeography, applied statistics, and computer science (e.g., Guillera-Aroita, 2017;Kéry, 2011). SDMs work under the assumption that species are in equilibrium with their environment; however, it is increasingly accepted that this is unrealistic in many cases, leading to the emergence of "temporal ecology" to complement the more established study of "spatial ecology" (Ryo, Aguilar-Trigueros, Pinek, Muller, & Rillig, 2019). This recognizes that the distributions of wild species are not static, but change/ evolve through time and space (as do the environmental conditions in which they occur), in a hierarchical way, with longer-term interannual dynamics overlaying shorter-term intraannual dynamics. Recently developed approaches, such as spatiotemporal exploratory models (STEM; Fink et al., 2010) and dynamic occupancy models (also known as multi-season occupancy models; Kéry & Chandler, 2012), have accounted for this spatiotemporal variation of species occurrence. Specifically, STEM is based on an ensemble or a mixture of static SDMs applied at a spatiotemporally restricted extent and then averaged over the whole extent (to account for local spatial and temporal patterns, reducing misleading extrapolation to distant regions), while dynamic occupancy models describe the occurrence at each site and the colonization and extinction probabilities from the previous time step.
However, even in these recent techniques, as well as in the more traditional static SDMs, the environmental predictors considered to estimate species occurrence are basically static. For instance, most of the studies applying SDMs attempt to predict species' distribution under future climate change scenarios (Titeux et al., 2016), but through static climatic predictors; specifically, most of these studies use climatic data that are averaged over a 50-(1950-2000, Hijmans, Cameron, Parra, Jones, & Jarvis, 2005 or 30-year period (1970-2000Fick & Hijmans, 2017). Thus, there could be a strong bias due to monthly/annual variation between the climatic conditions in which mobile species' locations were recorded and those used to develop SDMs, or even a complete mismatch in case of species locations collected after the year 2000. This problem becomes more acute with the increasing prevalence of climatic extremes (e.g., the summer of 2003 in Europe ;Jentsch & Beierkuhnlein, 2008), either models using averaged baseline data are increasingly unrepresentative, or, if such events are included in the baseline, the mean values may be unduly skewed (or at least the variability about them, which is often unaccounted for, is increased).
Currently, there is wide consensus that both climate and landuse change are among the most important threats to biodiversity and ecosystem services worldwide (IPBES, 2018(IPBES, , 2019Maxwell, Fuller, Brooks, & Watson, 2016;Scheffers et al., 2016), and often interact (Oliver & Morecroft, 2014). However, the impact of land-use change has rarely been explored in SDMs to date (Titeux et al., 2016) and the few studies (Milanesi, Breiner, Puopolo, & Holderegger, 2017;Newbold et al., 2015;Radinger et al., 2016) that have considered it have used static predictors (e.g., neglecting the effect of the destruction and modification of natural habitats). Actually, while the use of climate change projections is a commonplace, land-use change projections have more rarely been used in species forecasting (Bateman et al., 2013). This may be because they are not as widely available as climatic predictions are; incorporating these will be of critical importance since the interactions among multiple drivers of global change have recently been identified as a major cause of uncertainty in climate change attribution projection (Oliver & Morecroft, 2014;Parmesan et al., 2013), in part because multiple environmental pressures may have a greater joint impact than when operating in isolation (Ostberg, Schaphoff, Lucht, & Gerten, 2015).
Thus, in the absence of integrative multi-driver approaches, limited understanding of how interactions among drivers affect species distribution will be likely to hamper reliable projections (Titeux et al., 2016).
Although in some cases the distribution and/or diversity of species in different seasons may be better explained by a single predictor rather than multiple seasonally specific ones (e.g., Lennon, Greenwood, & Turner, 2000), SDMs that do not account for nonstationarity of climatic and land-use predictors may suffer from at least three problems: (a) The probability of species' occurrence at particular locations could be inaccurate due to the (temporally) averaged values in the predictors; (b) estimated slopes of predictor relationships could be biased; (c) some predictors may be wrongly identified as determinants of species' occurrence and/or may mask the real determinants, resulting, for example, in misleading inference or the wrong identification of areas of conservation importance. For example, species distribution may be negatively related to summer temperatures, but positively related to those in winter (Kawamura, Yamaura, Senzaki, Ueta, & Nakamura, 2019), patterns that would be masked by simply using annual values.
Treating the environment as static has, in part, been enforced McCullagh & Nelder, 1989), and random forests (RF; Breiman, 2001) in R (R Development Core Team, 2013) although D-SDMs could be developed using any appropriate algorithm. Similarly, while one can use any of the widely used validation statistics (Lecocq, Harpke, Rasmont, & Schweiger, 2019), to compare the predictive accuracy of both static and D-SDMs, in our simulations we considered the area under the receiver operating characteristic curve (AUC) and the true skills statistic (TSS). AUC ranges between 0 and 1 (worse than a random model and best discriminating model, respectively) while TSS varies between −1 and 1 (higher values indicate a good predictive accuracy, while 0 indicates random prediction). By using a random subsample of 90% of the locations to calibrate the models and the remaining 10% to evaluate them (Thuiller, Lafourcade, Engler, & Araújo, 2009) approach (Breiner, Guisan, Bergamini, Nobis, & Anderson, 2015) provides an alternative way to reduce overfitting.
Thus, D-SDMs can show higher predictive accuracy compared with static SDMs and could also improve the outcomes of STEM and dynamic occupancy models, providing more robust trends of species occurrence. For these reasons, we strongly encourage modelers to develop D-SDMs in order to provide more accurate estimates of species distribution. Such approaches are likely to be particularly valuable in more climatically variable regions and habitats (e.g., Reside, Wal, Kutt, & Perkins, 2010) and may become more important as climatic variability is predicted to increase (Kharin, Zwiers, Zhang, & Hegerl, 2007). However, species data quality (i.e., availability of information about the year and month in which species locations were collected), as well as the lack of open-access databases for nonterrestrial environments (e.g., pH concentration for freshwater biotopes), can strongly limit the application of D-SDMs and thus further improvements of both species data quality and accessibility of additional spatiotemporal environmental data are needed (e.g., Wetzel et al., 2018).
Finally, we urge researchers to make use of newly available datasets coming online to include both climate and land-use dynamic predictors, as the strength of impacts on biodiversity will likely depend on the interaction between these (Oliver & Morecroft, 2014). This will ensure, perhaps in combination with recently developed methods for generating high-resolution climate projections (Maclean, 2019), that future decision-making, such as prioritizing areas for conservation (e.g., Jones, Watson, Possingham, & Klein, 2016), more robustly anticipates the response of biodiversity to future climate and landuse changes.

ACK N OWLED G M ENTS
The comments of the Editor-in Chief Gareth Jenkins and those of two anonymous reviewers greatly helped improving this paper.

None declared.
F I G U R E 2 Differences between static and averaged dynamic species distribution models. Predictor variables and static model as in Figure 1, for the dynamic model year-specific predictor values are pooled for 2010-2015 into a single database, shown on the second line. Resulting map of static and averaged dynamic species distribution models is shown on the third line. Red-gray scale indicates high-low probability of occurrence

AUTH O R CO NTR I B UTI O N S
PM conceived and designed the overall study, PM and RAR wrote the manuscript, FDR and PM conducted the statistical analyses and designed the figures. FDR prepared the figures. All three authors contributed substantially to revisions of the paper.

DATA AVA I L A B I L I T Y S TAT E M E N T
Climatic GIS layers used for analysis in this paper are freely available at http://chelsa-clima te.org/chels acrut s/ and http://world clim.org/ version2.