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Investigating diversity gradients helps to understand biodiversity drivers and threats. However, one diversity gradient is rarely assessed, namely how plant species distribute along the depth gradient of lakes. Here, we provide the first comprehensive characterization of depth diversity gradient (DDG) of alpha, beta, and gamma species richness of submerged macrophytes across multiple lakes. We characterize the DDG for additive richness components (alpha, beta, gamma), assess environmental drivers, and address temporal change over recent years. We take advantage of yet the largest dataset of macrophyte occurrence along lake depth (274 depth transects across 28 deep lakes) as well as of physiochemical measurements (12 deep lakes from 2006 to 2017 across Bavaria), provided publicly online by the Bavarian State Office for the Environment. We found a high variability in DDG shapes across the study lakes. The DDGs for alpha and gamma richness are predominantly hump‐shaped, while beta richness shows a decreasing DDG. Generalized additive mixed‐effect models indicate that the depth of the maximum richness (Dmax) is influenced by light quality, light quantity, and layering depth, whereas the respective maximum alpha richness within the depth gradient (Rmax) is significantly influenced by lake area only. Most observed DDGs seem generally stable over recent years. However, for single lakes we found significant linear trends for Rmax and Dmax going into different directions. The observed hump‐shaped DDGs agree with three competing hypotheses: the mid‐domain effect, the mean–disturbance hypothesis, and the mean–productivity hypothesis. The DDG amplitude seems driven by lake area (thus following known species–area relationships), whereas skewness depends on physiochemical factors, mainly water transparency and layering depth. Our results provide insights for conservation strategies and for mechanistic frameworks to disentangle competing explanatory hypotheses for the DDG.

1. Current modelling approaches to predict spatially explicit biodiversity responses to climate change mainly focus on the direct effects of climate on species. Integration of spatiotemporal land-cover scenarios is still limited. Current approaches either regard land cover as constant boundary conditions, or rely on general, typically globally defined land-use scenarios. This is problematic as it disregards the complex synergistic effects of climate and land use on biodiversity at the regional scale, as biophysical, economic, and social issues important for regional land-use decisions are also affected by climate change. To realistically predict climate impacts on biodiversity, it is therefore necessary to consider both, the direct effect of climate change on biodiversity, and its indirect effect on biodiversity via land-use change. 2. In this review and perspective paper, we outline how biodiversity models could be better integrated with regional, climate-driven land-use models. We provide an overview of empirical and modelling approaches to both land-use (LU) and biodiversity (BD) change, focusing on how integration has been attempted. We then analyse how LU and BD model properties, such as scales, inputs, and outputs, can be matched and identify potential integration challenges and opportunities. 3. We found LU integration in BD models has been frequently attempted. By contrast, integrating the role of BD in models of LU decisions is largely lacking. As a result, bi-directional effects remain largely understudied. Only few integrated LU-BD socio-ecological models have assessed climate change effects on LU and no study has yet investigated the relative contribution of direct vs. indirect effects of climate change on BD. 4. There is a large potential for model integration given the overlap on spatial scales, although challenges remain with respect to spatial scale, temporal dynamics, investigation of indirect effects, and bi-directionality, including feeding back to climate models. Efforts to better understand human decisions, eco-evolutionary dynamics, connection between terrestrial and aquatic systems, and format standardization of modelling outputs and empirical data should improve future models. Integrating biodiversity feedbacks into land-use and climate models requires modelling innovations, but should be feasible.

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Current approaches to project spatial biodiversity responses to climate change mainly focus on the direct effects of climate on species while regarding land use and land cover as constant or prescribed by global land‐use scenarios. However, local land‐use decisions are often affected by climate change and biodiversity on top of socioeconomic and policy drivers. To realistically understand and predict climate impacts on biodiversity, it is, therefore, necessary to integrate both direct and indirect effects (via climate‐driven land‐use change) of climate change on biodiversity. In this perspective paper, we outline how biodiversity models could be better integrated with regional, climate‐driven land‐use models. We initially provide a short, non‐exhaustive review of empirical and modelling approaches to land‐use and land‐cover change (LU) and biodiversity (BD) change at regional scales, which forms the base for our perspective about improved integration of LU and BD models. We consider a diversity of approaches, with a special emphasis on mechanistic models. We also look at current levels of integration and at model properties, such as scales, inputs and outputs, to further identify integration challenges and opportunities. We find that LU integration in BD models is more frequent than the other way around and has been achieved at different levels: from overlapping predictions to simultaneously coupled simulations (i.e. bidirectional effects). Of the integrated LU‐BD socio‐ecological models, some studies included climate change effects on LU, but the relative contribution of direct vs. indirect effects of climate change on BD remains a key research challenge. Important research avenues include concerted efforts in harmonizing spatial and temporal resolution, disentangling direct and indirect effects of climate change on biodiversity, explicitly accounting for bidirectional feedbacks, and ultimately feeding socio‐ecological systems back into climate predictions. These avenues can be navigated by matching models, plugins for format and resolution conversion, and increasing the land‐use forecast horizon with adequate uncertainty. Recent developments of coupled models show that such integration is achievable and can lead to novel insights into climate–land use–biodiversity relations. Read the free Plain Language Summary for this article on the Journal blog.

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