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Recent studies indicated severe decline of insect diversity and abundance across major parts of Central Europe.
Theoretical studies showed that the drivers behind biodiversity loss vary considerably over time. However, these scenarios so far have been insufficiently approved by long-term and large-scale data.
In this study we analysed the temporal trends of butterflies and Zygaenid moths across the federal state of Salzburg, northern Austria, from 1920 to 2019. Our study area covers a large variety of habitats and altitudes.
Various changes of land use and intensification occurred during and shortly before our studied period, with a first wave of habitat destruction starting in the late 19th century, followed by the deterioration of habitat quality since the mid-20th century. We used 59,870 presence-only data of 168 butterfly and burnet moth species.
Each of these species was classified according to ecological characteristics. Break point analyses for non-linear temporal trends in the community composition returned two major time windows.
These time windows coincide with periods characterized by severe habitat destruction and the deterioration of habitat quality due to agricultural intensification. We found significant reductions of the proportion of species requiring specific habitats since 1920 and until today.
We identified additional break points for species requiring high habitat qualities, endangered butterfly species, and sedentary species, particularly after a main break point in the 1960s.
Our findings underline that, apart from habitat destruction, the deterioration of habitat quality is a main driver of biodiversity loss in general.
Therefore, nature conservation should focus on maintaining the highest possible habitat quality.
Describing the heterogeneous structure of forests is often challenging.
One possibility is to analyze forest biomass in different plots and to derive plot-based frequency distributions.
However, these frequency distributions depend on the plot size and thus are scale dependent.
This study provides insights about transferring them between scales. Understanding the effects of scale on distributions of biomass is particularly important for comparing information from different sources such as inventories, remote sensing and modeling, all of which can operate at different spatial resolutions. Reliable methods to compare results of vegetation models at a grid scale with field data collected at smaller scales are still missing.
The scaling of biomass and variables, which determine the forest biomass, was investigated for a tropical forest in Panama. Based on field inventory data from Barro Colorado Island, spanning 50 ha over 30 years, the distributions of aboveground biomass, biomass gain and mortality were derived at different spatial resolutions, ranging from 10 to 100 m. Methods for fitting parametric distribution functions were compared.
Further, it was tested under which assumptions about the distributions a simple stochastic simulation forest model could best reproduce observed biomass distributions at all scales. Also, an analytical forest model for calculating biomass distributions at equilibrium and assuming mortality as a white shot noise process was tested.
Scaling exponents of about 0.47 were found for the standard deviations of the biomass and gain distributions, while mortality showed a different scaling relationship with an exponent of 0.3. Lognormal and gamma distribution functions fitted with the moment matching estimation method allowed for consistent parameter transfers between scales. Both forest models (stochastic simulation and analytical solution) were able to reproduce observed biomass distributions across scales, when combined with the derived scaling relationships.
The study demonstrates a way of how to approach the scaling problem in model-data comparisons by providing a transfer relationship. Further research is needed for a better understanding of the mechanisms that shape the frequency distributions at the different scales.
The desiccation of the Aral Sea represents one of the largest human-made environmental regional disasters. The salt- and toxin-enriched dried-out basin provides a natural laboratory for studying ecosystem functioning and rhizosphere assembly under extreme anthropogenic conditions.
Here, we investigated the prokaryotic rhizosphere communities of the native pioneer plant Suaeda acuminata (C.A.Mey.) Moq. in comparison to bulk soil across a gradient of desiccation (5, 10, and 40 years) by metagenome and amplicon sequencing combined with quantitative PCR (qPCR) analyses. The rhizosphere effect was evident due to significantly higher bacterial abundances but less diversity in the rhizosphere compared to bulk soil. Interestingly, in the highest salinity (5 years of desiccation), rhizosphere functions were mainly provided by archaeal communities.
Along the desiccation gradient, we observed a significant change in the rhizosphere microbiota, which was reflected by (i) a decreasing archaeon-bacterium ratio, (ii) replacement of halophilic archaea by specific plant-associated bacteria, i.e., Alphaproteobacteria and Actinobacteria, and (iii) an adaptation of specific, potentially plant-beneficial biosynthetic pathways.
In general, both bacteria and archaea were found to be involved in carbon cycling and fixation, as well as methane and nitrogen metabolism.
Analysis of metagenome-assembled genomes (MAGs) showed specific signatures for production of osmoprotectants, assimilatory nitrate reduction, and transport system induction.
Our results provide evidence that rhizosphere assembly by cofiltering specific taxa with distinct traits is a mechanism which allows plants to thrive under extreme conditions. Overall, our findings highlight a function-based rhizosphere assembly, the importance of plant-microbe interactions in salinated soils, and their exploitation potential for ecosystem restoration approaches.IMPORTANCE
The desertification of the Aral Sea basin in Uzbekistan and Kazakhstan represents one of the most serious anthropogenic environmental disasters of the last century. Since the 1960s, the world's fourth-largest inland body of water has been constantly shrinking, which has resulted in an extreme increase of salinity accompanied by accumulation of many hazardous and carcinogenic substances, as well as heavy metals, in the dried-out basin.
Here, we investigated bacterial and archaeal communities in the rhizosphere of pioneer plants by combining classic molecular methods with amplicon sequencing as well as metagenomics for functional insights.
By implementing a desiccation gradient, we observed (i) remarkable differences in the archaeon-bacterium ratio of plant rhizosphere samples, (ii) replacement of archaeal indicator taxa during succession, and (iii) the presence of specific, potentially plant-beneficial biosynthetic pathways in archaea present during the early stages.
In addition, our results provide hitherto-undescribed insights into the functional redundancy between plant-associated archaea and bacteria.
The desertification of the Aral Sea basin in Uzbekistan and Kazakhstan represents one of the most serious anthropogenic environmental disasters of the last century.
Since the 1960s, the world's fourth-largest inland body of water has been constantly shrinking, which has resulted in an extreme increase of salinity accompanied by accumulation of many hazardous and carcinogenic substances, as well as heavy metals, in the dried-out basin.
Lakes act as important sinks for inorganic and organic sediment components. However, investigations of sedimentary carbon budgets within glacial lakes are currently absent from Arctic Siberia. The aim of this paper is to provide the first reconstruction of accumulation rates, sediment and carbon budgets from a lacustrine sediment core from Lake Rauchuagytgyn, Chukotka (Arctic Siberia). We combined multiple sediment biogeochemical and sedimentological parameters from a radiocarbon-dated 6.5m sediment core with lake basin hydroacoustic data to derive sediment stratigraphy, sediment volumes and infill budgets. Our results distinguished three principal sediment and carbon accumulation regimes that could be identified across all measured environmental proxies including early Marine Isotope Stage 2 (MIS2) (ca. 29-23.4 ka cal BP), mid-MIS2-early MIS1 (ca. 23.4-11.69 ka cal BP) and the Holocene (ca. 11.69-present). Estimated organic carbon accumulation rates (OCARs) were higher within Holocene sediments (average 3.53 gOCm(-2) a(-1)) than Pleistocene sediments (average 1.08 gOCm(-2) a(-1)) and are similar to those calculated for boreal lakes from Quebec and Finland and Lake Baikal but significantly lower than Siberian thermokarst lakes and Alberta glacial lakes. Using a bootstrapping approach, we estimated the total organic carbon pool to be 0.26 +/- 0.02 Mt and a total sediment pool of 25.7 +/- 1.71 Mt within a hydroacoustically derived sediment volume of ca. 32 990 557m(3). The total organic carbon pool is substantially smaller than Alaskan yedoma, thermokarst lake sediments and Alberta glacial lakes but shares similarities with Finnish boreal lakes. Temporal variability in sediment and carbon accumulation dynamics at Lake Rauchuagytgyn is controlled predominantly by palaeoclimate variation that regulates lake ice-cover dynamics and catchment glacial, fluvial and permafrost processes through time. These processes, in turn, affect catchment and within-lake primary productivity as well as catchment soil development. Spatial differences compared to other lake systems at a trans-regional scale likely relate to the high-latitude, mountainous location of Lake Rauchuagytgyn.
Heat shock factor HSFA2 fine-tunes resetting of thermomemory via plastidic metalloprotease FtsH6
(2022)
The transcription factor HSFA2 fine-tunes a balance between prolongation and resetting of thermomemory in Arabidopsis via the regulation of both memory-supporting and memory-resetting genes.
Plants 'memorize' stressful events and protect themselves from future, often more severe, stresses. To maximize growth after stress, plants 'reset' or 'forget' memories of stressful situations, which requires an intricate balance between stress memory formation and the degree of forgetfulness.
HEAT SHOCK PROTEIN 21 (HSP21) encodes a small heat shock protein in plastids of Arabidopsis thaliana. HSP21 functions as a key component of thermomemory, which requires a sustained elevated level of HSP21 during recovery from heat stress. A heat-induced metalloprotease, filamentation temperature-sensitive H6 (FtsH6), degrades HSP21 to its pre-stress abundance, thereby resetting memory during the recovery phase. The transcription factor heat shock factor A2 (HSFA2) activates downstream genes essential for mounting thermomemory, acting as a positive regulator in the process.
Here, using a yeast one-hybrid screen, we identify HSFA2 as an upstream transactivator of the resetting element FtsH6. Constitutive and inducible overexpression of HSFA2 increases expression of FtsH6, whereas it is drastically reduced in the hsfa2 knockout mutant. Chromatin immunoprecipitation reveals in planta binding of HSFA2 to the FtsH6 promoter. Importantly, overexpression of HSFA2 improves thermomemory more profoundly in ftsh6 than wild-type plants.
Thus, by activating both memory-supporting and memory-resetting genes, HSFA2 acts as a cellular homeostasis factor during thermomemory.
Mitochondrial stress-induced GFRAL signaling controls diurnal food intake and anxiety-like behavior
(2022)
Growth differentiation factor 15 (GDF15) is a mitochondrial stressinduced cytokine that modulates energy balance in an endocrine manner.
However, the importance of its brainstem-restricted receptor GDNF family receptor alpha-like (GFRAL) to mediate endocrine GDF15 signaling to the brain uponmitochondrial dysfunction is still unknown. Using a mouse model with muscle-specific mitochondrial dysfunction, we here show that GFRAL is required for activation of systemic energy metabolism via daytime-restricted anorexia but not responsible for muscle wasting.
We further find that muscle mitochondrial stress response involves a GFRAL-dependent induction of hypothalamic corticotropin-releasing hormone, without elevated corticosterone levels.
Finally, we identify that GFRAL signaling governs an anxiety-like behavior in male mice with muscle mitochondrial dysfunction, with females showing a less robust GFRAL-dependent anxiety-like phenotype.
Together, we here provide novel evidence of a mitochondrial stress-induced muscle-brain crosstalk via the GDF15-GFRAL axis to modulate food intake and anxiogenic behavior.
A key in controlling the SARS-CoV-2 pandemic is the assessment of the immune status of the population. We explored the utility of SARS-CoV-2 virus-like particles (VLPs) as antigens to detect specific humoral immune reactions in an enzyme-linked immunosorbent assay (ELISA).
For this purpose, SARS-CoV-2 VLPs were produced from an engineered cell line and characterized by Western blot, ELISA, and nanoparticle tracking analysis.
Subsequently, we collected 42 serum samples from before the pandemic (2014), 89 samples from healthy subjects, and 38 samples from vaccinated subjects. Seventeen samples were collected less than three weeks after infection, and forty-four samples more than three weeks after infection.
All serum samples were characterized for their reactivity with VLPs and the SARS-CoV-2 N- and S-protein.
Finally, we compared the performance of the VLP-based ELISA with a certified in vitro diagnostic device (IVD). In the applied set of samples, we determined a sensitivity of 95.5% and a specificity of 100% for the certified IVD.
There were seven samples with an uncertain outcome. Our VLP-ELISA demonstrated a superior performance, with a sensitivity of 97.5%, a specificity of 100%, and only three uncertain outcomes.
This result warrants further research to develop a certified IVD based on SARS-CoV-2 VLPs as an antigen.
Simple Summary
Urokinase-type plasminogen activator (urokinase, uPA) is a widely discussed biomarker for cancer prognosis and diagnosis. The gold standard for the determination of protein biomarkers in physiological samples is the enzyme-linked immunosorbent assay (ELISA). Here, antibodies are used to detect the specific protein.
In our study, recently published urokinase aptamers were tested for their use in a sandwich assay format as alternative specific recognition elements. Different aptamer combinations were used for the detection of uPA in a sandwich-assay format and a combination of aptamers and antibodies additionally allowed the differentiation of human high and low molecular weight- (HMW- and LMW-) uPA. Hence, uPA aptamers offer a valuable alternative as specific recognition elements for analytical purposes. Since aptamers are easy to synthesize and modify, they can be used as a cost-effective alternative in sandwich assay formats for the detection of uPA in physiological samples.
Abstract
Urokinase-type plasminogen activator (urokinase, uPA) is a frequently discussed biomarker for prognosis, diagnosis, and recurrence of cancer.
In a previous study, we developed ssDNA aptamers that bind to different forms of human urokinase, which are therefore assumed to have different binding regions.
In this study, we demonstrate the development of aptamer-based sandwich assays that use different combinations of these aptamers to detect high molecular weight- (HMW-) uPA in a micro titer plate format.
By combining aptamers and antibodies, it was possible to distinguish between HMW-uPA and low molecular weight- (LMW-) uPA.
For the best performing aptamer combination, we calculated the limit of detection (LOD) and limit of quantification (LOQ) in spiked buffer and urine samples with an LOD up to 50 ng/mL and 138 ng/mL, respectively.
To show the specificity and sequence dependence of the reporter aptamer uPAapt-02-FR, we have identified key nucleotides within the sequence that are important for specific folding and binding to uPA using a fluorescent dye-linked aptamer assay (FLAA). Since uPA is a much-discussed marker for prognosis and diagnosis in various types of cancers, these aptamers and their use in a micro titer plate assay format represent a novel, promising tool for the detection of uPA and for possible diagnostic applications.
Microviridins are a prominent family of ribosomally synthesized and posttranslationally modified peptides (RiPPs) featuring characteristic lactone and lactam rings. Their unusual cage-like architecture renders them highly potent serine protease inhibitors of which individual variants specifically inhibit different types of proteases of pharmacological interest.
While posttranslational modifications are key for the stability and bioactivity of RiPPs, additional attractive properties can be introduced by functional tags.
To date - although highly desirable - no method has been reported to incorporate functional tags in microviridin scaffolds or the overarching class of graspetides.
In this study, a chemoenzymatic in vitro platform is used to introduce functional tags in various microviridin variants yielding biotinylated, dansylated or propargylated congeners.
This straightforward approach paves the way for customized protease inhibitors with built-in functionalities that can help to unravel the still elusive ecological roles and targets of this remarkable class of compounds and to foster applications based on protease inhibition.
In this topical review, we give an overview of the structure and dynamics of a single polymer chain in active baths, Gaussian or non-Gaussian.
The review begins with the discussion of single flexible or semiflexible linear polymer chains subjected to two noises, thermal and active.
The active noise has either Gaussian or non-Gaussian distribution but has a memory, accounting for the persistent motion of the active bath particles. This finite persistence makes the reconfiguration dynamics of the chain slow as compared to the purely thermal case and the chain swells.
The active noise also results superdiffusive or ballistic motion of the tagged monomer. We present all the calculations in details but mainly focus on the analytically exact or almost exact results on the topic, as obtained from our group in recent years.
In addition, we briefly mention important works of other groups and include some of our new results. The review concludes with pointing out the implications of polymer chains in active bath in biologically relevant context and its future directions.
Studies investigating the effect of aboveground herbivory on plants often use clipping to simulate the effects of herbivores, for practical reasons. However, herbivore movements and transfer of oral secretions during herbivory may cause a different response in plant physiology and morphology compared to clipping.
While studies have compared effects of real herbivory vs. clipping on biomass production, plant physiology, and shoot morphology, no study has compared such effects on root morphology.
Therefore, we investigated the effect of herbivory by grasshoppers, herbivory simulated by clipping, and no herbivory on root morphological traits of ten grassland plant species. Root morphological traits were differently affected by the two herbivory treatments. Grasshopper herbivory significantly changed root morphology toward thinner roots with increased specific root length and root area, and decreased root tissue density compared to untreated control plants.
Clipping had mostly similar, but weaker effects on root morphology than grasshopper herbivory.
On the species level, grasshopper herbivory led to strongest changes in root morphology in almost all cases. In contrast, depending on the species, clipping resulted in varying root morphological trait values similar to grasshopper-damaged plants, or in some cases, more closely aligned with control plants.
Though clipping was partly able to mimic the effects of herbivory by grasshoppers, results also indicate that, depending on the species, grasshopper herbivory had different but mostly stronger effects. We, therefore, recommend that future studies apply herbivory with real herbivores to better reflect natural responses in plants and related processes that root morphological traits mediate.
The BEEHAVE model simulates the population dynamics and foraging activity of a single honey bee colony (Apis mellifera) in great detail. Although it still makes numerous simplifying assumptions, it appears to capture a wide range of empirical observations.
It could, therefore, in principle, also be used as a tool in beekeeper education, as it allows the implementation and comparison of different management options.
Here, we focus on treatments aimed at controlling the mite Varroa destructor. However, since BEEHAVE was developed in the UK, mite treatment includes the use of a synthetic acaricide, which is not part of Good Beekeeping Practice in Germany.
A practice that consists of drone brood removal from April to June, treatment with formic acid in August/September, and treatment with oxalic acid in November/December. We implemented these measures, focusing on the timing, frequency, and spacing between drone brood removals.
The effect of drone brood removal and acid treatment, individually or in combination, on a mite-infested colony was examined. We quantify the efficacy of Varroa mite control as the reduction of mites in treated bee colonies compared to untreated bee colonies. We found that drone brood removal was very effective, reducing mites by 90% at the end of the first simulation year after the introduction of mites. This value was significantly higher than the 50-67% reduction expected by bee experts and confirmed by empirical studies.
However, literature reports varying percent reductions in mite numbers from 10 to 85% after drone brood removal. The discrepancy between model results, empirical data, and expert estimates indicate that these three sources should be reviewed and refined, as all are based on simplifying assumptions.
These results and the adaptation of BEEHAVE to the Good Beekeeping Practice are a decisive step forward for the future use of BEEHAVE in beekeeper education in Germany and anywhere where organic acids and drone brood removal are utilized.
How predictable is the next move of an animal? Specifically, which factors govern the short- and long-term motion patterns and the overall dynamics of land-bound, plant-eating animals in general and ruminants in particular? To answer this question, we here study the movement dynamics of springbok antelopes Antidorcas marsupialis. We propose several complementary statistical-analysis techniques combined with machine-learning approaches to analyze—across multiple time scales—the springbok motion recorded in long-term GPS tracking of collared springboks at a private wildlife reserve in Namibia. As a result, we are able to predict the springbok movement within the next hour with a certainty of about 20%. The remaining about 80% are stochastic in nature and are induced by unaccounted factors in the modeling algorithm and by individual behavioral features of springboks. We find that directedness of motion contributes approximately 17% to this predicted fraction. We find that the measure for directedeness is strongly dependent on the daily cycle of springbok activity. The previously known daily affinity of springboks to their water points, as predicted from our machine-learning algorithm, overall accounts for only about 3% of this predicted deterministic component of springbok motion. Moreover, the resting points are found to affect the motion of springboks at least as much as the formally studied effects of water points. The generality of these statements for the motion patterns and their underlying behavioral reasons for other ruminants can be examined on the basis of our statistical-analysis tools in the future.
The ability to catalyze diverse reactions with relevance for chemical and pharmaceutical research and industry has led to an increasing interest in fungal enzymes.
There is still an enormous potential considering the sheer amount of new enzymes from the huge diversity of fungi.
Most of these fungal enzymes have not been characterized yet due to the lack of high throughput synthesis and analysis methods.
This bottleneck could be overcome by means of cell-free protein synthesis. In this study, cell-free protein synthesis based on eukaryotic cell lysates was utilized to produce a functional glycoside hydrolase (GH78) from the soft-rot fungus Xylaria polymorpha (Ascomycota).
The enzyme was successfully synthesized under different reaction conditions.
We characterized its enzymatic activities and immobilized the protein via FLAG-Tag interaction. Alteration of several conditions including reaction temperature, template design and lysate supplementation had an influence on the activity of cell-free synthesized GH78.
Consequently this led to a production of purified GH78 with a specific activity of 15.4 U mg? 1.
The results of this study may be foundational for future high throughput fungal enzyme screenings, including substrate spectra analysis and mutant screenings.
Step selection analysis (SSA) is a common framework for understanding animal movement and resource selection using telemetry data. Such data are, however, inherently autocorrelated in space, a complication that could impact SSA‐based inference if left unaddressed. Accounting for spatial correlation is standard statistical practice when analysing spatial data, and its importance is increasingly recognized in ecological models (e.g. species distribution models). Nonetheless, no framework yet exists to account for such correlation when analysing animal movement using SSA.
Here, we extend the popular method integrated step selection analysis (iSSA) by including a Gaussian field (GF) in the linear predictor to account for spatial correlation. For this, we use the Bayesian framework R‐INLA and the stochastic partial differential equations (SPDE) technique.
We show through a simulation study that our method provides accurate fixed effects estimates, quantifies their uncertainty well and improves the predictions. In addition, we demonstrate the practical utility of our method by applying it to three wolverine (Gulo gulo) tracks.
Our method solves the problems of assuming spatially independent residuals in the SSA framework. In addition, it offers new possibilities for making long‐term predictions of habitat usage.
Protist grazing pressure plays a major role in controlling aquatic bacterial populations, affecting energy flow through the microbial loop and biogeochemical cycles. Predator-escape mechanisms might play a crucial role in energy flow through the microbial loop, but are yet understudied. For example, some bacteria can use planktonic as well as surface-associated habitats, providing a potential escape mechanism to habitat-specific grazers.
We investigated the escape response of the marine bacterium Marinobacter adhaerens in the presence of either planktonic (nanoflagellate: Cafeteria roenbergensis) or surface-associated (amoeba: Vannella anglica) protist predators, following population dynamics over time.
In the presence of V. anglica, M. adhaerens cell density increased in the water, but decreased on solid surfaces, indicating an escape response towards the planktonic habitat. In contrast, the planktonic predator C. roenbergensis induced bacterial escape to the surface habitat. While C. roenbergensis cell numbers dropped substantially after a sharp initial increase, V. anglica exhibited a slow, but constant growth throughout the entire experiment.
In the presence of C. roenbergensis, M. adhaerens rapidly formed cell clumps in the water habitat, which likely prevented consumption of the planktonic M. adhaerens by the flagellate, resulting in a strong decline in the predator population.
Our results indicate an active escape of M. adhaerens via phenotypic plasticity (i.e., behavioral and morphological changes) against predator ingestion.
This study highlights the potentially important role of behavioral escape mechanisms for community composition and energy flow in pelagic environments, especially with globally rising particle loads in aquatic systems through human activities and extreme weather events.
The pathogenesis of influenza A viruses (IAVs) is influenced by several factors, including IAV strain origin and reassortment, tissue tropism and host type. While such factors were mostly investigated in the context of virus entry, fusion and replication, little is known about the viral-induced changes to the host lipid membranes which might be relevant in the context of virion assembly. In this work, we applied several biophysical fluorescence microscope techniques (i.e., Förster energy resonance transfer, generalized polarization imaging and scanning fluorescence correlation spectroscopy) to quantify the effect of infection by two IAV strains of different origin on the plasma membrane (PM) of avian and human cell lines. We found that IAV infection affects the membrane charge of the inner leaflet of the PM. Moreover, we showed that IAV infection impacts lipid–lipid interactions by decreasing membrane fluidity and increasing lipid packing. Because of such alterations, diffusive dynamics of membrane-associated proteins are hindered. Taken together, our results indicate that the infection of avian and human cell lines with IAV strains of different origins had similar effects on the biophysical properties of the PM.
RangeShifter 2.0
(2021)
Process-based models are becoming increasingly used tools for understanding how species are likely to respond to environmental changes and to potential management options. RangeShifter is one such modelling platform, which has been used to address a range of questions including identifying effective reintroduction strategies, understanding patterns of range expansion and assessing population viability of species across complex landscapes. Here we introduce a new version, RangeShifter 2.0, which incorporates important new functionality. It is now possible to simulate dynamics over user-specified, temporally changing landscapes. Additionally, we integrated a new genetic module, notably introducing an explicit genetic modelling architecture, which allows for simulation of neutral and adaptive genetic processes. Furthermore, emigration, transfer and settlement traits can now all evolve, allowing for sophisticated simulation of the evolution of dispersal. We illustrate the potential application of RangeShifter 2.0's new functionality by two examples. The first illustrates the range expansion of a virtual species across a dynamically changing UK landscape. The second demonstrates how the software can be used to explore the concept of evolving connectivity in response to land-use modification, by examining how movement rules come under selection over landscapes of different structure and composition. RangeShifter 2.0 is built using object-oriented C++ providing computationally efficient simulation of complex individual-based, eco-evolutionary models. The code has been redeveloped to enable use across operating systems, including on high performance computing clusters, and the Windows graphical user interface has been enhanced. RangeShifter 2.0 will facilitate the development of in-silico assessments of how species will respond to environmental changes and to potential management options for conserving or controlling them. By making the code available open source, we hope to inspire further collaborations and extensions by the ecological community.