Genetic diversity is a key part of biodiversity, threatened by human activities that lead to loss of gene flow and reduction of effective population sizes. Gene flow is a result of both landscape connectivity and demographic processes determining the number of dispersing individuals in space and time. Thus, the effect of human impact on processes determining the level of genetic diversity must be interpreted in the context of basic ecological conditions affecting survival and recruitment. When the intensity of human impact and habitat suitability correlate, the effect on genetic diversity and gene flow may be challenging to predict. We compared genetic diversity, gene flow and landscape resistance in two contrasting landscapes in Norway for the pond‐breeding amphibian Triturus cristatus: a highly human‐impacted, agricultural landscape with ecologically productive habitats, and a forested landscape with less productive habitats and lower levels of human impact. Our results show that genetic diversity was higher and gene flow lower within the forested landscape. Microclimatic moisture conditions and vegetation cover were important determinants of landscape resistance to gene flow within both landscapes. There were indications that landscape resistance was increased by minor roads in the forested landscape, which was not the case for the agricultural landscape, suggesting a higher vulnerability to human interference within the landscape matrix for the populations in less productive habitats. Our findings suggest that the effect of human impact on genetic diversity may not be straightforward but modulated by the ecological conditions underlying local demographic processes. Populations within both landscapes seem to be vulnerable to loss of genetic diversity, but due to different mechanisms. This has implications for the choice of relevant management actions, that is, increasing population stability may be more relevant within an agricultural landscape still permeable for dispersal, while conserving dispersal corridors may be more appropriate in the forested landscape, to avoid isolation and increased genetic drift.

Remote sensing data comprise a valuable information source for many ecological landscape studies that may be under-utilized because of an overwhelming amount of processing methods and derived variables. These complexities, combined with a scarcity of quality control studies, make the selection of appropriate remote sensed variables challenging. Quality control studies are necessary to evaluate the predictive power of remote sensing data and also to develop parsimonious models underpinned by functional variables, i.e., cause rather than solely correlation. Cause-based models yield superior model transferability across different landscapes and ecological settings. We propose two basic guidelines for conducting such quality control studies that increase transferability and predictive power. The first is to favor predictors that are causally related to the response. The second is to include additional variables controlling variation in the property of interest and testing for optimum processing method and/or scale. Here, we evaluated these principles in predicting ground vegetation cover, soil moisture and pH under challenging conditions with forest canopies hindering direct remote sensing of the ground. Our model using lidar data combined with natural resource maps explained most of the observed variation in soil pH and moisture, and somewhat less variation of ground vegetation cover. Soil pH was best predicted by topographic position, sediment type and site index (R² = 0.90). Soil moisture was best predicted by topographic position, radiation load, sediment type and site index (R² = 0.83). The best model for predicting ground vegetation cover was a combination of lidar-based estimates for light availability below canopy and forest type, including an interaction between these two variables (R² = 0.65).

Landscape context can influence demographic rates and, consequently, the population genetic processes determining levels of genetic diversity. However, the role of landscape context is not always sufficiently considered in conservation management. For species with complex life cycles, such as amphibians, landscape context is particularly relevant because they rely on both aquatic and terrestrial habitats, the quality of which can vary across landscapes. Understanding how landscape context may influence these genetic processes is therefore important for effective management. We used population viability analysis (PVA) to simulate how landscape context may influence genetic and demographic outcomes for the great crested newt ( Triturus cristatus ) using two landscape‐specific models representing boreal forest and agricultural landscapes. We assessed the sensitivity of model outcomes to parameter perturbations and evaluated management scenarios and potential threats at the metapopulation level. A key result was that environmental stochasticity strongly influenced genetic dynamics in both landscapes. The low‐stochasticity forest model retained genetic diversity, responded strongly to management actions, and relied less on gene flow. In contrast, the high‐stochasticity agricultural model experienced stronger genetic drift, responded weakly to management actions, and relied more on continuous gene flow. Our findings suggest that enhancing gene flow alone may be insufficient for maintaining genetic diversity, and that improving resilience to environmental stochasticity should be considered an important part of genetic management.