TY - JOUR A1 - Radchuk, Viktoriia A1 - Johst, Karin A1 - Gröneveld, Juergen A1 - Grimm, Volker A1 - Schtickzelle, Nicolas T1 - Behind the scenes of population viability modeling predicting butterfly metapopulation dynamics under climate change JF - Ecological modelling : international journal on ecological modelling and engineering and systems ecolog N2 - Studies explaining the choice of model structure for population viability analysis (PVA) are rare and no such study exists for butterfly species, a focal group for conservation. Here, we describe in detail the development of a model to predict population viability of a glacial relict butterfly species, Boloria eunomia, under climate change. We compared four alternative formulations of an individual-based model, differing in the environmental factors acting on the survival of immature life stages: temperature (only temperature impact), weather (temperature, precipitation, and sunshine), temperature and parasitism, and weather and parasitism. Following pattern-oriented modeling, four observed patterns were used to contrast these models: one qualitative (response of population size to habitat parameters) and three quantitative ones describing population dynamics during eight years (mean and variability of population size, and magnitude of the temporal autocorrelation in yearly population growth rates). The four model formulations were not equally able to depict population dynamics under current environmental conditions; the model including only temperature was selected as the most parsimonious model sufficiently well reproducing the empirical patterns. We used all four model formulations to test a range of climate change scenarios that were characterized by changes in both mean and variability of the weather variables. All models predicted adverse effects of climate change and resulted in the same ranking of mean climate change scenarios. However, models differed in their absolute values of population viability measures, underlining the need to explicitly choose the most appropriate model formulation and avoid arbitrary usage of environmental drivers in a model. We conclude that further applications of pattern-oriented modeling to butterfly and other species are likely to help in identifying the key factors impacting the viability of certain taxa, which, ultimately, will aid and speed up informed management decisions for endangered species under climate change. KW - Individual-based model KW - Population viability analysis KW - Glacial relict species KW - Life cycle KW - Boloria eunomia KW - Pattern-oriented modeling KW - Model structure Y1 - 2013 U6 - https://doi.org/10.1016/j.ecolmodel.2013.03.014 SN - 0304-3800 VL - 259 IS - 2 SP - 62 EP - 73 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Leins, Johannes A. A1 - Banitz, Thomas A1 - Grimm, Volker A1 - Drechsler, Martin T1 - High-resolution PVA along large environmental gradients to model the combined effects of climate change and land use timing BT - lessons from the large marsh grasshopper JF - Ecological modelling : international journal on ecological modelling and systems ecology N2 - Both climate change and land use regimes affect the viability of populations, but they are often studied separately. Moreover, population viability analyses (PVAs) often ignore the effects of large environmental gradients and use temporal resolutions that are too coarse to take into account that different stages of a population's life cycle may be affected differently by climate change. Here, we present the High-resolution Large Environmental Gradient (HiLEG) model and apply it in a PVA with daily resolution based on daily climate projections for Northwest Germany. We used the large marsh grasshopper (LMG) as the target species and investigated (1) the effects of climate change on the viability and spatial distribution of the species, (2) the influence of the timing of grassland mowing on the species and (3) the interaction between the effects of climate change and grassland mowing. The stageand cohort-based model was run for the spatially differentiated environmental conditions temperature and soil moisture across the whole study region. We implemented three climate change scenarios and analyzed the population dynamics for four consecutive 20-year periods. Climate change alone would lead to an expansion of the regions suitable for the LMG, as warming accelerates development and due to reduced drought stress. However, in combination with land use, the timing of mowing was crucial, as this disturbance causes a high mortality rate in the aboveground life stages. Assuming the same date of mowing throughout the region, the impact on viability varied greatly between regions due to the different climate conditions. The regional negative effects of the mowing date can be divided into five phases: (1) In early spring, the populations were largely unaffected in all the regions; (2) between late spring and early summer, they were severely affected only in warm regions; (3) in summer, all the populations were severely affected so that they could hardly survive; (4) between late summer and early autumn, they were severely affected in cold regions; and (5) in autumn, the populations were equally affected across all regions. The duration and start of each phase differed slightly depending on the climate change scenario and simulation period, but overall, they showed the same pattern. Our model can be used to identify regions of concern and devise management recommendations. The model can be adapted to the life cycle of different target species, climate projections and disturbance regimes. We show with our adaption of the HiLEG model that high-resolution PVAs and applications on large environmental gradients can be reconciled to develop conservation strategies capable of dealing with multiple stressors. KW - Climate change KW - Land use KW - Population viability analysis KW - Stage-based model KW - High resolution KW - Environmental gradients Y1 - 2020 U6 - https://doi.org/10.1016/j.ecolmodel.2020.109355 SN - 0304-3800 SN - 1872-7026 VL - 440 PB - Elsevier CY - Amsterdam ER -