Assessing biophysical and socio-economic impacts of climate change on regional avian biodiversity

Socio-economic variables improve accuracy and change spatial predictions in species distribution models

In an era marked by increasing anthropogenic pressure, understanding the relations between human activities and wildlife is crucial for understanding ecological patterns, effective conservation, and management strategies. Here, we explore the potential and usefulness of socio-economic variables in species distribution modelling (SDM), focusing on their impact on the occurrence of wild mammals in Poland. Beyond the environmental factors commonly considered in SDM, like land-use, the study tests the importance of socio-economic characteristics of local human societies, such as age, income, working sector, gender, education, and village characteristics for explaining distribution of diverse mammalian groups, including carnivores, ungulates, rodents, soricids, and bats. The study revealed that incorporating socio-economic variables enhances the predictive power for >60 % of species and overall for most groups, with the exception being carnivores. For all the species combined, among the 10 predictors with highest predictive power, 6 belong to socio-economic group, while for specific species groups, socio-economic variables had similar predictive power as environmental variables. Furthermore, spatial predictions of species occurrence underwent changes when socio-economic variables were included in the model, resulting in a substantial mismatch in spatial predictions of species occurrence between environment-only models and models containing socio-economic variables. We conclude that socio-economic data has potential as useful predictors which increase prediction accuracy of wildlife occurrence and recommend its wider usage. Further, to our knowledge this is a first study on such a big scale for terrestrial mammals which evaluates performance based on presence or absence of socio-economic predictors in the model. We recognise the need for a more comprehensive approach in SDMs and that bridging the gap between human socio-economic dynamics and ecological processes may contribute to the understanding of the factors influencing biodiversity.

Habitat suitability maps for Australian flora and fauna under CMIP6 climate scenarios

BACKGROUND

Spatial information about the location and suitability of areas for native plant and animal species under different climate futures is an important input to land use and conservation planning and management. Australia, renowned for its abundant species diversity and endemism, often relies on modeled data to assess species distributions due to the country's vast size and the challenges associated with conducting on-ground surveys on such a large scale. The objective of this article is to develop habitat suitability maps for Australian flora and fauna under different climate futures.

RESULTS

Using MaxEnt, we produced Australia-wide habitat suitability maps under RCP2.6-SSP1, RCP4.5-SSP2, RCP7.0-SSP3, and RCP8.5-SSP5 climate futures for 1,382 terrestrial vertebrates and 9,251 vascular plants vascular plants at 5 km2 for open access. This represents 60% of all Australian mammal species, 77% of amphibian species, 50% of reptile species, 71% of bird species, and 44% of vascular plant species. We also include tabular data, which include summaries of total quality-weighted habitat area of species under different climate scenarios and time periods.

CONCLUSIONS

The spatial data supplied can help identify important and sensitive locations for species under various climate futures. Additionally, the supplied tabular data can provide insights into the impacts of climate change on biodiversity in Australia. These habitat suitability maps can be used as input data for landscape and conservation planning or species management, particularly under different climate change scenarios in Australia.

The Impact of the COVID-19 Pandemic on the Global Value Chain of the Manufacturing Industry

This paper adopts the GDYN model to estimate the dynamic impact of the COVID-19 pandemic on global manufacturing industry and the value chain. Our simulation finds that (1) In the short run, the low-tech manufacturing industries will suffer greater shocks, with a decline of output growth in 2021 by 6.0%. The growth rate of the high-tech manufacturing industry showed an increasing trend of 3.7% in 2021. (2) In the post-epidemic period, the total manufacturing output will return to the baseline level, from which the growth rate of low-tech manufacturing will rebound, demonstrating a V-shaped development trajectory. (3) From the perspective of Global Value Chain (GVC), the participation in GVCs of manufacturers in countries along the Belt and Road, the European Union and the United States will weaken, while China’s manufacturing industry has witnessed an obvious improvement in export competitiveness. The import added value of China has decreased, which shows that its ability to meet domestic demand has been improving. This indicates that the COVID-19 pandemic is providing a crucial opportunity for China to upgrade its manufacturing value chain, which contributes to the accelerated construction of a new dual-cycle development pattern.