Spatially explicit models for decision‐making in animal conservation and restoration

Ecography, Journal Year: 2021, Volume and Issue: 2022(4)

Published: Oct. 8, 2021

Models are useful tools for understanding and predicting ecological patterns processes. Under ongoing climate biodiversity change, they can greatly facilitate decision‐making in conservation restoration help designing adequate management strategies an uncertain future. Here, we review the use of spatially explicit models decision support to identify key gaps current modelling restoration. Of 650 reviewed publications, 217 publications had a clear application were included our quantitative analyses. Overall, studies biased towards static (79%), species population level (80%) (rather than restoration) applications (71%). Correlative niche most widely used model type. Dynamic as well gene‐to‐individual community‐to‐ecosystem underrepresented, cost optimisation approaches only 10% studies. We present new typology selecting animal restoration, characterising types according organisational levels, biological processes interest desired applications. This will more closely link goals. Additionally, future efforts need overcome important challenges related data integration, integration decision‐making. conclude with five recommendations, suggesting that wider usage be achieved by 1) developing toolbox multiple, easier‐to‐use methods, 2) improving calibration validation dynamic 3) best‐practise guidelines applying these models. Further, robust 4) combining multiple assess uncertainty, 5) placing at core adaptive management. These must accompanied long‐term funding monitoring, improved communication between research practise ensure optimal outcomes.

Language: Английский

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RangeShifter 2.0: an extended and enhanced platform for modelling spatial eco‐evolutionary dynamics and species' responses to environmental changes

Ecography, Journal Year: 2021, Volume and Issue: 44(10), P. 1453 - 1462

Published: Aug. 29, 2021

Process‐based models are becoming increasingly used tools for understanding how species likely to respond environmental changes and potential management options. RangeShifter is one such modelling platform, which has been address a range of questions including identifying effective reintroduction strategies, patterns expansion assessing population viability across complex landscapes. Here we introduce new version, 2.0, incorporates important functionality. It now possible simulate dynamics over user‐specified, temporally changing Additionally, integrated genetic module, notably introducing an explicit architecture, allows simulation neutral adaptive processes. Furthermore, emigration, transfer settlement traits can all evolve, allowing sophisticated the evolution dispersal. We illustrate application 2.0's functionality by two examples. The first illustrates virtual dynamically UK landscape. second demonstrates software be explore concept evolving connectivity in response land‐use modification, examining movement rules come under selection landscapes different structure composition. 2.0 built using object‐oriented C++ providing computationally efficient individual‐based, eco‐evolutionary models. code redeveloped enable use operating systems, on high performance computing clusters, Windows graphical user interface enhanced. will facilitate development in‐silico assessments options conserving or controlling them. By making available open source, hope inspire further collaborations extensions ecological community.

Language: Английский

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rangr: An R package for mechanistic, spatially explicit simulation of species range dynamics

Methods in Ecology and Evolution, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

Abstract Global change driven by human activities is causing profound shifts in species distributions. Understanding the mechanisms that influence these dynamics crucial for biodiversity management. Several mechanistic, spatially explicit models have been proposed to address this issue, but they do not cover full range of potential functionalities. We present a new open‐source R package called rangr , which integrates population and dispersal into mechanistic virtual simulator. The can be used study effects environmental on growth shifts. It extends capabilities previously available simulators allowing simple straightforward definition (including positive density dependence), extensive possibilities defining kernels ability generate ecologist data. showcased functionality simulating invasion collared dove ( Streptopelia decaocto ). First, we demonstrated how set up simulation with different investigating role long‐distance events colonisation outcome. Second, showed use assess an Allee effect impede biological invasion. Finally, framework determine timeframe required detect spread invasive species. package, comes documentation vignettes, easy up, flexible, fast, fully configurable capable emulating observation process. These features make particularly well suited generating data replicate existing wildlife monitoring programmes.

Language: Английский

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Coupling eco‐evolutionary mechanisms with deep‐time environmental dynamics to understand biodiversity patterns

Ecography, Journal Year: 2022, Volume and Issue: 2023(4)

Published: July 27, 2022

Pioneer naturalists such as Whewell, Lyell, Humboldt, Darwin and Wallace acknowledged the interactions between ecological evolutionary forces, well roles of continental movement, mountain formation climate variations, in shaping biodiversity patterns. Recent developments computer modelling paleo‐environmental reconstruction have made it possible for scientists to study silico how emerges from eco‐evolutionary environmental dynamic processes their interactions. Simulating emergent enables experimentation multiple interconnected hypotheses a largely fragmented scientific landscape, with final objective successfully approximating natural mechanisms (i.e. hypothetical spatio–temporally unrestricted generalizations that hold across empirical patterns). This new interdisciplinary approach opens unprecedented pathways, facilitating communication contemplation causal implications complex In this review I provide comprehensive overview available population‐based spatially explicit mechanistic models (MEEMs) rely on reconstructions, critically discussing relevance limitations our understanding biodiversity. To achieve this, first introduce diverse contextualize MEEMs. Second, define MEEMs synthesize major insights studies using combined deep‐time dynamics (> 0.1 Ma). Lastly, discuss challenges perspectives solving long‐standing enigmas by coupling dynamics. Studies show linking environments is necessary reproduce large‐scale patterns simultaneously. Mechanisms related adaptations (e.g. niche evolution), dispersal abilities other those resulting speciation or extinction events) universal importance, although signatures spatial temporal scales remain unknown. Investigations MEEMS spanning levels complexity space time foster cooperation sciences promise some Earth's

Language: Английский

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MetaRange.jl: A Dynamic and Metabolic Species Range Model for Plant Species

Ecology and Evolution, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 1, 2025

Process-based models for range dynamics are urgently needed due to increasing intensity of human-induced biodiversity change. Despite a few existing that focus on demographic processes, their use remains limited compared the widespread application correlative approaches. This slow adoption is largely challenges in calibrating biological parameters and high computational demands large-scale applications. Moreover, number simulated processes (i.e., mechanistic complexity) may further exacerbate those reasons delay. Therefore, balancing complexity effectiveness process-based key area improvement. A promising research direction expand demographically explicit metapopulation by integrating metabolic constraints. We translated expanded previously developed R model Julia language published it as module. The integrates species-specific such preferred environmental conditions, biomass dispersal ability with rates (e.g., reproductive mortality rates) derived from local temperature via theory ecology. provide simple example which we illustrate typical case predicting future occurrence

Language: Английский

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Fitting individual‐based models of spatial population dynamics to long‐term monitoring data

Ecological Applications, Journal Year: 2024, Volume and Issue: 34(4)

Published: April 17, 2024

Abstract Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative models (cSDMs) are among the most widely used tools this purpose. However, fundamental assumption cSDMs, that distributions in equilibrium with their environment, rarely fulfilled real data limits applicability cSDMs dynamic projections. Process‐based, SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics enhance spatiotemporal transferability. Software implementing dSDMs becoming increasingly available, but parameter estimation can be complex. Here, we test feasibility calibrating validating dSDM using long‐term monitoring Swiss red kites ( Milvus milvus ). This population has shown strong increases abundance progressive range expansion over last decades, indicating nonequilibrium situation. We construct an individual‐based model RangeShiftR modeling platform use Bayesian inference calibration. allows integration heterogeneous sources, such estimates from published literature observational schemes, coherent assessment uncertainty. Our encompass counts breeding pairs at 267 sites across Switzerland 22 years. validate our spatial‐block cross‐validation scheme assess predictive performance rank‐correlation coefficient. showed very good accuracy projections represented well observed two decades. Results suggest reproductive success was key factor driving expansion. According model, kite fills large parts its current potential further density. demonstrate practicality validation RangeShiftR. approach improve compared cSDMs. The workflow presented here adopted any which some prior knowledge on demographic dispersal parameters observations or presence/absence available. fitted provides improved quantitative insights into ecology species, greatly aid conservation management efforts.

Language: Английский

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Disequilibrium in plant distributions: Challenges and approaches for species distribution models

Journal of Ecology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 13, 2025

Abstract Environmental conditions are dynamic, and plants respond to those dynamics on multiple time scales. Disequilibrium occurs when a response more slowly than the driving environmental changes. We review evidence regarding disequilibrium in plant distributions, including their responses paleoclimate changes, recent climate change new species introductions. There is strong that distributions often some with conditions. This poses challenge projecting future using distribution models (SDMs). Classically, SDMs assume set of occurrences an unbiased sample suitable However, environment may have higher‐than‐expected occurrence probabilities (e.g. due extinction debts) or lower‐than‐expected dispersal limitation) different areas. If unaccounted for, this will lead biased estimates suitability. methods for avoiding such biases SDMs, ranging from simple thinning dataset complex dynamic process‐based models. Such require large data inputs, natural history knowledge technical expertise, so implementing them can be challenging. Despite this, we advocate increased use, since provide best potential account model training then represent occupancy as ranges shift. Synthesis . Occurrence records climate. trained produce species' niche unless addressed modelling. A range tools, spanning wide gradient complexity realism, resolve bias.

Language: Английский

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Establishing viable European bison metapopulations in Central Europe

Biological Conservation, Journal Year: 2025, Volume and Issue: 305, P. 111074 - 111074

Published: March 10, 2025

Language: Английский

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The need for an individual-based global change ecology

Published: March 26, 2025

Biodiversity loss and widespread ecosystem degradation are among the most pressing challenges of our time, requiring urgent action. Yet understanding their causes remains limited because prevailing ecological concepts approaches often overlook underlying complex interactions individuals same or different species, interacting with each other environment. We propose a paradigm shift in science, moving from simplifying frameworks that use population community averages to an integrative approach recognizes individual organisms as fundamental agents change. The urgency biodiversity crisis requires such advance ecology towards predictive science by elucidating causal mechanisms linking variation adaptive behaviour emergent properties populations, communities, ecosystems, human interventions. Recent advances computational technologies, sensors, analytical tools now offer unprecedented opportunities overcome past lay foundation for truly integrated Individual-Based Global Change Ecology (IBGCE). Unravelling potential role variability global change impact analyses will require systematic combination empirical, experimental modelling studies across systems, while taking into account multiple drivers interactions. Key priorities include refining theoretical frameworks, developing benchmark models standardized toolsets, systematically incorporating empirical field work, experiments models. emerging synergies between individual-based modelling, big data approaches, machine learning hold great promise addressing inherent complexity ecosystems. Each step development IBGCE must balance perspective parsimony, efficiency, feasibility. aims unravel predict dynamics Anthropocene through comprehensive study organisms, It provide critical considering future conservation sustainability management, individual-to-ecosystem pathways feedbacks.

Language: Английский

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Effects of defaunation of large seed dispersers, habitat loss and fragmentation on the population expansion of a tropical palm

Landscape Ecology, Journal Year: 2025, Volume and Issue: 40(4)

Published: March 25, 2025

Language: Английский

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