Spix’s Macaw Cyanopsitta spixii (Wagler, 1832) population viability analysis

Bird Conservation International, Год журнала: 2023, Номер 33

Опубликована: Янв. 1, 2023

Summary Spix’s Macaw Cyanopsitta spixii is one of the most endangered Neotropical Psittacidae species. Extinct in wild year 2000, June 2022 first cohort C. was reintroduced to its original habitat. For a successful reintroduction species, it necessary examine viability population against natural and external threats environmental requirements for success. Thus, this paper presents “Population Viability Analysis” (PVA) Macaw. It used Vortex RangeShiftR software, biological data from bibliographic survey, information provided by field team responsible who work directly with species captivity. We found that minimum viable (MVP) 20 individuals. However, considering impact disease, drought, hunting, illegal trafficking, can only persist if release individuals captivity occurs annually over next years combined reforestation habitat support growth.

Язык: Английский

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Making virtual species less virtual by reverse engineering of spatiotemporal ecological models

Methods in Ecology and Evolution, Год журнала: 2023, Номер 14(9), С. 2376 - 2389

Опубликована: Июль 8, 2023

Abstract The virtual species (VS) and ecologist (VE) approaches are useful tools that allow testing different methodological aspects of distribution modelling. However, methods used to generate VS so far lack solutions can ensure a high degree biological realism, taking into account spatial temporal variability population densities. We have developed method for generating dynamic reconstruct their living prototypes in realistic way. framework consists fitting spatiotemporal model real abundance data, from over the entire study area spanning whole period, calibrating VS, obtaining VE data by sampling VS. effectiveness approach has been illustrated large‐scale long‐term bird monitoring, using whinchat Saxicola rubetra as system. evaluated how well ‘true’ system comparing response curves trends between those (i.e. what constitutes ‘truth’) estimated replicated instances data. In addition, we performed sensitivity analysis test varying effort affects accuracy trend estimation. synthetic thoroughly reconstructed monitoring Response generalized additive mixed models (GAMMs), fitted these two types showed concordance, indicated 95% confidence intervals coverage probability 87.7%–99.8% (mean 96.9%). accurately calculated (coverage probability: 82.3%). proposed reverse engineering ecological reproduces properties original system, substantially increasing realism simulation results. may further applications evaluating various modelling techniques range dynamics, where real‐world particular importance, like conservation invasion biology or climate change impact assessment.

Язык: Английский

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metaRange: A framework to build mechanistic range models

Methods in Ecology and Evolution, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 24, 2024

Abstract Mechanistic or process‐based models offer great insights into the range dynamics of species facing non‐equilibrium conditions, such as climate and land‐use changes invasive species. Their consideration underlying mechanisms relaxes species‐environment equilibrium assumed by correlative approaches, while also generating conservation‐relevant indicators, range‐wide abundance time series migration rates if demographically explicit. However, computational complexity mechanistic limits their development applicability to large spatiotemporal extents. We present R package “metaRange”: a modular framework build population‐based metabolically constrained models. provide catalogue biological functions calculate niche‐based suitability, metabolic scaling, population dynamics, biotic interactions kernel‐based dispersal, which may include directed movement. The framework's modularity enables user combine, extend, replace these functions, making it possible customize model ecology study system. supports an unlimited number static dynamic environmental factors input, including land use. As examples, we one single‐species application predict European wildcat ( Felis silvestris ) in Germany, theoretical simulated 100 virtual three scenarios: without competition, with competition under generalist‐specialist trade‐off. Due population‐level, can execute extensive simulation experiments on regular end‐user hardware short amount time. detailed technical documentation, both for individual well instructions how set up different types structures experimental designs. metaRange simulations multiple interacting high resolution low demand. believe that allows hypotheses testing about future real‐world species, better support conservation policies targeting biodiversity loss mitigation.

Язык: Английский

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Demography-environment relationships improve mechanistic understanding of range dynamics under climate change

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2022, Номер unknown

Опубликована: Сен. 26, 2022

Abstract Species responses to climate change are widely detected as range and abundance changes. To better explain predict them, we need a mechanistic understanding of how the underlying demographic processes shaped by climatic conditions. We built spatially-explicit, process-based models for eight Swiss breeding bird populations. They jointly consider dispersal, population dynamics climate-dependence three - juvenile survival, adult survival fecundity. The were calibrated two-decade time-series in Bayesian framework. assessed goodness-of-fit discriminatory power with different metrics, indicating fair excellent model fit. most influential predictors performance mean breeding-season temperature total winter precipitation. Maps overall growth rate highlighted demographically suitable areas. Further, benefits from contemporary typical mountain birds, whereas lowland birds adversely affected. Embedding generic solid statistical framework improves our allows disentangling abiotic biotic processes. For future research, advocate stronger integration experimental empirical measurements more detailed order generate precise insights into which affects

Язык: Английский

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

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2022, Номер unknown

Опубликована: Сен. 27, 2022

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, cSDMs fundamental assumption distributions in equilibrium with their environment rarely met real data limits applicability dynamic projections. Process-based, SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics enhance spatio-temporal transferability. Software implementing dSDMs become increasingly available, yet 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 non-equilibrium situation. We construct an individual-based model RangeShiftR modelling platform use Bayesian inference calibration. allows integration heterogeneous sources, such estimates from published literature well 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 observed two decades. Results suggest that reproductive success was key factor driving expansion. According model, kite fills large parts its current but potential further density. demonstrate practicality validation RangeShifteR. 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, may greatly help conservation management actions. Open Research statement submission uses novel code provided external repository. All required replicate analyses private-for-peer review via public GitHub repository under following link: https://github.com/UP-macroecology/Malchow_IBMcalibration_2023 Upon acceptance, will archived versioned Zenodo DOI provided. For study, tagged development version R package available at: https://github.com/RangeShifter/RangeShiftR-package/releases/tag/v.1.1-beta.0

Язык: Английский

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