Species 2 Expansion

[Hyun Orth]

Urbanland expansion of up to 1.53 million square kilometers of new land will threaten the survival of more than 800 species but a focus on urban planning that protects habitats can mitigate the impact.

Expansion is projected to result in up to 1.53 million square kilometers of new urban land, directly threatening 855 species, according to the findings of a new PNAS study co-authored by Karen Seto, Yale School of the Environment Frederick C. Hixon Professor of Geography and Urbanization Science, YSE PhD student Rohan Simkin, Walter Jetz, director of the Yale Center for Biodiversity and Global Change and professor of ecology and evolutionary biology, and Robert McDonald, lead scientist for nature-based solutions at The Nature Conservancy.

The study identified hotspot cities that are predicted to have particularly large impacts on species habitats. Many of the hotspot cities are in equatorial regions where urban growth coincides with biodiverse habitats. The cities that pose the greatest threat to species due to expansion are predominately located in the developing tropical regions of sub-Saharan Africa, South America, Mesoamerica, and Southeast Asia.

The report comes as the 15th Conference of Parties convenes in April to decide the new post 2020 biodiversity conservation framework. The study demonstrates the need for global conservation efforts to include policies to preserve species in urban lands.

Species under the most expansion pressure are concentrated in areas from central Mexico through Central America, the Caribbean, Haiti, Nigeria, Cameroon, Sri Lanka, Indonesia, Malaysia, Thailand, Brazil, and Ecuador.

Global agreements on biodiversity and conservation that focus on protecting the habitat of species that are predicted to be the most vulnerable, investments from the Global Environment Facility and targeted action at local scales can help mitigate impact on species.

Seto is the coordinating lead author of the urban mitigation chapter of the upcoming Intergovernmental Panel on Climate Change (IPCC) report, which will address the most up-to-date physical understanding of the global climate system and climate change.

Even when you examine hypothesis 1, you have to somehow filter out other possible environmental changes or vectors of spread. E.g., you somehow have to prove that the species could not have survived in the new regions under historical climate regimes (as opposed to it could have survived, but was limited by other factors, such as dispersal ability).

I suspect that with the expansion of iNat users is in a big way due to two major things: the re-discovery of nature, by many who never spent as much time outdoors until the pandemic arrived; the dramatic increased technical accessibility created by more powerful, more affordable, less technical camera technology either as smartphone cams, or others.

Copyright: 2018 Barbet-Massin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All the data used in this study are available from the INPN database, an aggregated and heterogeneous database about biodiversity. Aggregated data (number of nests by 10x10 km grid cells) are freely available, but the precise GPS records cannot be made publicly available as they come from citizen science programs. The GPS locations could enable anyone to discover each citizen identity. In order to ensure anonymity, anyone wanting to use these data must fill in the INPN form. Data requests can be made by filling in the request form available at =en. As one of the authors works for the INPN and is the head of this monitoring program (QR), the authors had special access to the data. In addition to the restrictions explained above, the authors cannot provide these data freely due to French regulations. However, the authors confirm that interested, qualified researchers will be able to access the data without restriction after filling in the request form and specifying that the data will not be used for commercial purposes, agreeing to provide attribution in any published papers, and that they will not be distribute these data to third parties.

We used the same eight climatic variables as in previous studies for the niche modelling of V. v. nigrithorax [50,52]. We considered: (1) annual mean temperature, (2) mean temperature of the warmest month, (3) mean temperature of the coldest month, (4) temperature seasonality, (5) annual precipitation, (6) precipitation of the wettest month, (7) precipitation of the driest month and (8) precipitation seasonality. The seasonality is the coefficient of variation of the monthly means. Current data were downloaded from the worldclim database [53] ( ) as 2.5 arc-min grids (subset of the 19 bioclim variables). These data are interpolations from observed data representative of current climatic conditions.

As results might depend on the cut-off year chosen to split the invasive data into calibration data and evaluation data, sensitivity analyses were carried out by applying different cut-off years. With that in mind, all analyses (SDM calibration and SDM evaluation) were carried out nine times, with cut-off years going from 2006 to 2014.

The climatic niche occupied by V. v. nigrithorax in its invasion range clearly extended during the past few years (Fig 1A and 1B), as there is only a 45% overlap between the climatic niche occupied between 2004 and 2010 and the climatic niche occupied between 2011 and 2015. Statistical tests show that both niches are similar but not equivalent (Fig 1D and 1E). As the climatic niche first occupied by the species in its invasion range is still occupied, we could have expected both niches to be similar. Both niches not being equivalent further shows that part of the climatic niche occupied by the species between 2011 and 2015 was not occupied between 2004 and 2010. This means that in 2010 the species was not yet at equilibrium with its environment in Europe.

Furthermore, even though modeling methods provide good predictions, the predictions still differ according to whether or not native records were taken into account (Fig 2 & S2 Fig). We can thus further investigate whether one option provides more accurate results than the other. In our case study, percentiles of validation points were significantly higher when the climate suitability was predicted by models accounting for invasive data only (for all cut-off years, except for 2006, opposite result) (Fig 3 & S1 Table). Overall, models thus seem to have a better predictive accuracy when accounting for invasive data only.

Percentiles of validation points (further than 150km from the first invasion record) depending on whether or not native data was accounted for to calibrate the models and on the cut-off year that was used to split the invasive data into calibration and evaluation data. Percentiles are obtained by comparing the predicted climate suitability of a given validation point to the distribution of climate suitability values of all points being at the same distance from the first invasion record than the validation point (i.e., grey points in Fig 2B). Percentiles higher than 50th thus mean that the predicted climate suitability of the validation point is higher than expected given its distance to the first invasion record. For all cut-off years, paired t-test were computed to assess the difference between models with and without native data: a red star indicates significantly higher values (S1 Table).

Using the unique features of an invasion closely monitored in space and time, we demonstrated that despite some known limitations, SDMs can be a powerful tool to predict where invasive species will spread next. In fact, our case study does show that V. v. nigrithorax is not at equilibrium with its environment in its European invaded ranges (Fig 1). This finding is consistent with studies focusing on other invasive species [30,36]. The equilibrium hypothesis being an important assumption, its violation needs to be acknowledged when interpreting SDMs predictions [26]. Indeed, violating the equilibrium hypothesis has some consequences when modeling species distributions, among which underestimating the potential climatic niche of the species, which can in turn lead to underestimating the geographical area the species can invade [36]. However, predicting the full potential invasive range of an invasive species may not be as relevant as accurately predicting the areas that are more likely to be colonized next. Indeed, given the cost of species monitoring and surveillance for the early detection of invasive species, it is more relevant to predict areas that might be invaded next rather than all potential areas that could be reached by the invader if the species achieved its climate equilibrium. Information regarding the areas that might be invaded next could indeed be used by managers for a cost-effective effort on monitoring and controlling such areas. For example, in the case of V. v. nigrithorax, whose invasion can be most efficiently controlled by an early detection followed by nest removal [43,60], monitoring efforts need to be implemented within the highest suitable areas within the already invaded range, as well as within the highest suitable areas that are the closest to the already invaded range. Improved detection techniques would further increase the efficiency and decrease the costs of monitoring/controlling the invasion [61]. Therefore, even if invasive species distribution models cannot predict the full potential invasion range of an invasive species that has just established [36,62], they can still be very valuable for invasive species management. Yet, validation is needed for model reliability and credibility, especially when management decisions are based upon it [63].

3a8082e126