Predicting the distribution of the invasive species Hyptis suaveolens in Nigeria

David, Oyinade A.; Akomolafe, Gbenga Festus; Onwusiri, Kenneth Chidinma; Fabolude, Gift Oluwatodimu

doi:10.14712/23361964.2020.11

Cited by 6 publications

(4 citation statements)

References 22 publications

(26 reference statements)

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“…Maxent is used in modelling species distributions and is characterized by a high predictive performance, even in the case of very small sample sizes [61,62,88,89]. The model uses presence-only occurrence data and a set of environmental variables, and by using the maximum entropy principle it computes habitat suitability values over the entire study area.…”

Section: Predictive Distribution Modelmentioning

confidence: 99%

Suitability of Habitats in Nepal for Dactylorhiza hatagirea Now and under Predicted Future Changes in Climate

Shrestha

Tsiftsis

Chapagain

et al. 2021

Plants

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Dactylorhiza hatagirea is a terrestrial orchid listed in Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and classified as threatened by International Union for Conservation of Nature (IUCN). It is endemic to the Hindu-Kush Himalayan region, distributed from Pakistan to China. The main threat to its existence is climate change and the associated change in the distribution of its suitable habitats to higher altitudes due to increasing temperature. It is therefore necessary to determine the habitats that are suitable for its survival and their expected distribution after the predicted changes in climate. To do this, we use Maxent modelling of the data for its 208 locations. We predict its distribution in 2050 and 2070 using four climate change models and two greenhouse gas concentration trajectories. This revealed severe losses of suitable habitat in Nepal, in which, under the worst scenario, there will be a 71–81% reduction the number of suitable locations for D. hatagirea by 2050 and 95–98% by 2070. Under the most favorable scenario, this reduction will be 65–85% by 2070. The intermediate greenhouse gas concentration trajectory surprisingly would result in a greater reduction by 2070 than the worst-case scenario. Our results provide important guidelines that local authorities interested in conserving this species could use to select areas that need to be protected now and in the future.

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Section: Predictive Distribution Modelmentioning

confidence: 99%

Suitability of Habitats in Nepal for Dactylorhiza hatagirea Now and under Predicted Future Changes in Climate

Shrestha

Tsiftsis

Chapagain

et al. 2021

Plants

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“…This approach is widely used for predicting current as well as future distributions of species from a set of occurrence records and environmental variables (Yi et al, 2016;Tsiftsis & Djordjević, 2020). A great advantage of this method is that it has a high predictive performance even for very small sample sizes (Hernandez et al, 2006;Elith & Leathwick, 2009;David et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Is the GBIF appropriate for use as input in models of predicting species distributions? Study from the Czech Republic

Štípková,

Tsiftsis,

Kindlmann

2024

Nat. Conserv. Res.

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Questions concerning species diversity have attracted ecologists and biogeographers for over a century, mainly because the diversity of life on Earth is in rapid decline, which is expected to continue in the future. One of the most important current database on species distribution data is the Global Biodiversity Information Facility (GBIF), which contains more than 2 billion occurrences for all organisms, and this number is continuously increasing with the addition of new data and by combining with other applications. Such data also exist in several national databases, most of which are unfortunately often not freely available and not included in GBIF. We suspected that the national databases, mostly professionally maintained by governmental organisations, may be more comprehensive than GBIF, which is not centrally organised and therefore the national databases may give more accurate predictions than GBIF. To test our assumptions, we have compared: (i) the amount of data included in the Czech database called Nálezová databáze ochrany přírody (NDOP, Discovery database of nature protection) with the amount of data in GBIF after its restriction to the Czech Republic, and (ii) the overlap of the predictions of species distributions for the Czech Republic, based on these two databases. We have used the family Orchidaceae as a model group. We found that: (i) there is a significantly larger number of records per studied region (Czech Republic) in NDOP, compared with GBIF, and (ii) the predictions of Maxent based on orchid records in NDOP are overlapping to a great degree with the predictions based on data based on orchid records in GBIF. Bearing in mind these results, we suggest that if only one database is available for the region studied, we must use this one. If more databases are available for the region studied, we should use the database containing most locations (usually some of the local ones, like NDOP), because using more locations implies larger significance of predictions of species distributions.

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“…Its invasiveness is expressed in its ability to displace native plant species with the formation of dense colonies. Several countries including the Philippines, Singapore, Hawaii, India, Australia, Nigeria ( David et al . 2021 ) and so on have reported the incidence and severity of its invasion.…”

Section: Introductionmentioning

confidence: 99%

“…2007 ). In Nigeria, H. suaveolens has been predicted to have the potential to spread to several parts of the country with different ecological attributes ( David et al . 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Soil seed bank dynamics of two invasive alien plants in Nigeria: implications for ecosystem restoration

Akomolafe,

Rosazlina,

Omomoh

2024

AoB PLANTS

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The assessment of seed banks could provide useful hints towards ensuring restoration planning and invasive species management. In this study, the impacts of two invaders, Hyptis suaveolens and Urena lobata on the soil seed banks were investigated. We also assessed the seed characteristics of the invaders at the invaded sites. This was achieved using 10 sites each for H. suaveolens, U. lobata invaded habitats and non-invaded habitats making a total of 30 sites. We collected 200 soil samples in each habitat type. A seedling emergence method was used to determine the seed bank recruitment of both invasive plants. The diversity indices of the above-ground vegetation of sites invaded by the two plants were significantly lower than those of the non-invaded sites. Only two plant species emerged from the seed banks of H. suaveolens and five plants from those of U. lobata as compared with non-invaded sites where 53 species emerged. A larger portion of the seeds was located in the soil's lower layer at all the sites invaded by H. suaveolens while those of U. lobata and non-invaded sites were found in the upper layers and there are significant associations between the habitats . The lower soil layers of the two species have the highest percentage of viable seeds. These results help to understand more about the invasiveness of both species as related to their impacts on the seed banks and native vegetation. It also indicates that the native species that emerged from the invaded seed banks could be used for the restoration of the invaded habitats.

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Predicting the distribution of the invasive species Hyptis suaveolens in Nigeria

Cited by 6 publications

References 22 publications

Suitability of Habitats in Nepal for Dactylorhiza hatagirea Now and under Predicted Future Changes in Climate

Suitability of Habitats in Nepal for Dactylorhiza hatagirea Now and under Predicted Future Changes in Climate

Is the GBIF appropriate for use as input in models of predicting species distributions? Study from the Czech Republic

Soil seed bank dynamics of two invasive alien plants in Nigeria: implications for ecosystem restoration

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