Summary For most organisms, early life‐history stages are the most sensitive to environmental stress and so transgenerational phenotypic plasticity, whereby the parental environment and offspring environment interact to alter the phenotype of the offspring, is viewed as key to promoting persistence in the face of environmental change. While there has been long‐standing interest in the role of transgenerational plasticity via the maternal line (traditionally the field of maternal effects), increasingly it appears that paternal effects can also play a role. Despite the emerging role of paternal effects in studies of global change, key knowledge gaps remain: first, whether paternal effects act to increase or decrease offspring performance remains largely unexplored; second, the relative roles of maternal and paternal effects are rarely disentangled; and third, the role of environmental variation, a key determinant of the benefits of transgenerational plasticity, has not been explored with regard to paternal effects. Here, we explore all three issues using the marine tubeworm Galeolaria caespitosa, an important habitat‐forming species in southern Australia. We found that both paternal and maternal experiences affected key stages of offspring performance (fertilization and larval development) and, surprisingly, paternal effects were often stronger than maternal effects. Furthermore, we found that paternal effects often reduced offspring performance, especially when environments varied compared with when environments were stable. Our results suggest that, while transgenerational plasticity may play an important role in modifying the impacts of global change, these effects are not uniformly positive. Importantly, paternal effects can be as strong, or stronger, than maternal effects and environmental variability strongly alters the impacts of paternal effects.

Supplementary Figure 1. Landscape extent and artificial population structure. The population membership coefficient was manually drawn in order to represent a textbook demographic set-up. The greyscale digital image used to represent the population membership coefficient (spanning from 0 to 255) is shown in the bottom-right corner. The image was geo-referenced and the pixel values were assigned to the real geographic units. The resulting structure displays two different populations located in the north-west and south-east of the map, respectively. In between, individuals are progressively admixed. Population Membership Coefficient 0 255 Original gradient picture

The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in the use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; <1 m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aim of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant, Arabis alpina, in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs, up to a spatial resolution of at least 1 m, rivalled the accuracy of LiDAR DEMs, largely owing to the customizability of PHOTO DEMs to the study sites compared to commercially available LiDAR DEMs. We obtained DEMs at spatial resolutions of 6.25 cm–8 m for PHOTO and 50 cm–32 m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32 m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50 cm in such studies.

The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; &lt;1m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aims of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant Arabis alpina in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs rivalled the accuracy of LiDAR, putting the current paradigm of LiDAR being the more accurate of the two methods into question. We obtained DEMs at spatial resolutions of 6.25cm-8m for PHOTO and 50cm-32m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, we found that SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50cm in such studies.

No abstract