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          "family_name": "Santana",
          "given_name": "Joana",
          "name": "Santana, Joana",
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        "affiliations": [
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            "name": "Spatial Biology Lab, Faculdade de Ci\u00eancias da Universidade do Porto, Porto, Portugal"
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          "name": "Sillero, Neftal\u00ed",
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      {
        "affiliations": [
          {
            "name": "BIOPOLIS Program in Genomics, Biodiversity and Land Planning, Vair\u00e3o, Portugal|Universidade de Lisboa, Lisboa, Portugal|Universidade do Porto, Vair\u00e3o, Portugal"
          }
        ],
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          "name": "Ribeiro, Joana",
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          {
            "name": "Laborat\u00f3rio Associado Terra, Lisboa, Portugal|Universidade de Lisboa, Lisboa, Portugal"
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        ],
        "person_or_org": {
          "family_name": "Capinha",
          "given_name": "C\u00e9sar",
          "name": "Capinha, C\u00e9sar",
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      {
        "affiliations": [
          {
            "name": "Universidade do Porto, Vair\u00e3o, Portugal|BIOPOLIS Program in Genomics, Biodiversity and Land Planning, Vair\u00e3o, Portugal|Universidade de Lisboa, Lisboa, Portugal"
          }
        ],
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          "family_name": "Lopes",
          "given_name": "Ricardo Jorge",
          "name": "Lopes, Ricardo Jorge",
          "type": "personal"
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          {
            "name": "Universidade do Porto, Vair\u00e3o, Portugal|Universidade de Lisboa, Lisboa, Portugal|BIOPOLIS Program in Genomics, Biodiversity and Land Planning, Vair\u00e3o, Portugal"
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    "description": "<p>Ecological niche models (ENMs) are a powerful tool to predict the spread of invasive alien species (IAS) and support the implementation of actions aiming to reduce the impact of biological invasions. While calibrating ENMs with distribution data from species' native ranges can underestimate the invasion potential due to possible niche shifts, using distribution data combining species' native and invasive ranges may overestimate the invasion potential due to a reduced fitness and environmental tolerance of species in invaded ranges. An alternative may be using the increasingly available distribution data of IAS as they spread their invaded ranges, to iteratively forecast invasions as they unfold. However, while this approach accounts for possible niche shifts, it may also underestimate the species' potential range, particularly at the early stages of the invasion when the most suitable conditions may not yet be represented in the distribution range data set. Here, we evaluate the capacity of ENMs to forecast the distribution of IAS based on distribution data on invaded ranges as these data become available. We further use dispersion models to assess the expansion process using the predicted potential distributions. Specifically, we used the common waxbill (<i>Estrilda astrild</i>) in the Iberian Peninsula as a model system. We built ENMs with 10\u00d710 km grid cells distribution records cumulatively for each decade from 1960 to 2019, and yearly bioclimatic variables, to forecast the species potential range in the coming decades. Then, we assessed the performance of the models for each decade in forecasting the species' observed range expansion in the following decades and evaluated how the number of distribution records determined the quality of the forecasts. Finally, we performed dispersal estimates (based on species traits, topography, climate and land cover) to analyse the prediction capacity of models as their uncertainty may be reduced when projecting them to the next decades. Our results show that invasion-only ENMs successfully forecasted the species' range expansion over three decades after invasion, while dispersion models were not important in forecasting common waxbill expansion. Our study highlights the importance of constantly monitoring alien species, suggesting that iterative updating of ENMs with observed distribution data may accurately forecast the range expansion of alien species.</p>",
    "publication_date": "2024-09-04",
    "publisher": "Pensoft Publishers",
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