Quantifying the impact of vegetation-based metrics on species persistence when choosing offsets for habitat destruction

Developers are often required by law to offset environmental impacts through targeted conservation actions. Most offset policies specify metrics for calculating offset requirements, usually by assessing vegetation condition. Despite widespread use, there is little evidence to support the effectiveness of vegetation-based metrics for ensuring biodiversity persistence. We compared long-term impacts of biodiversity offsetting based on area only; vegetation condition only; area x habitat suitability; and condition x habitat suitability in development and restoration simulations for the Hunter Region of New South Wales, Australia. We simulated development and subsequent offsetting through restoration within a virtual landscape, linking simulations to population viability models for 3 species. Habitat gains did not ensure species persistence. No net loss was achieved when performance of offsetting was assessed in terms of amount of habitat restored, but not when outcomes were assessed in terms of persistence. Maintenance of persistence occurred more often when impacts were avoided, giving further support to better enforce the avoidance stage of the mitigation hierarchy. When development affected areas of high habitat quality for species, persistence could not be guaranteed. Therefore, species must be more explicitly accounted for in offsets, rather than just vegetation or habitat alone. Declines due to a failure to account directly for species population dynamics and connectivity overshadowed the benefits delivered by producing large areas of high-quality habitat. Our modeling framework showed that the benefits delivered by offsets are species specific and that simple vegetation-based metrics can give misguided impressions on how well biodiversity offsets achieve no net loss.Peer reviewe

iflint1/roadkill_manuscript: Maximising the informativeness of new records in spatial sampling design

<p>This repository reproduces our results from the Supplementary Materials of our manuscript on Maximising the informativeness of new records in spatial sampling design (2023) published in the Methods in Ecology and Evolution journal.</p&gt

Modeling the Spatial Variation of Urban Land Surface Temperature in Relation to Environmental and Anthropogenic Factors: A Case Study of Tehran, Iran

Spatial variation of Urban Land Surface Temperature (ULST) is a complex function of environmental, climatic, and anthropogenic factors. It thus requires specific techniques to quantify this phenomenon and its influencing factors. In this study, four models, Random Forest (RF), Generalized Additive Model (GAM), Boosted Regression Tree (BRT), and Support Vector Machine (SVM), are calibrated to simulate the ULST based on independent factors, i.e., land use/land cover (LULC), solar radiation, altitude, aspect, distance to major roads, and Normalized Difference Vegetation Index (NDVI). Additionally, the spatial influence and the main interactions among the influential factors of the ULST are explored. Landsat-8 is the main source for data extraction and Tehran metropolitan area in Iran is selected as the study area. Results show that NDVI, LULC, and altitude explained 86% of the ULST C variation. Unexpectedly, lower LST is observed near the major roads, which was due to the presence of vegetation along the streets and highways in Tehran. The results also revealed that variation in the ULST was influenced by the interaction between altitude - NDVI, altitude - road, and LULC - altitude. This indicates that the individual examination of the underlying factors of the ULST variation might be unilluminating. Performance evaluation of the four models reveals a close performance in which their R2 and Root Mean Square Error (RMSE) fall between 60.6-62.1% and 2.56-2.60 C, respectively. However, the difference between the models is not statistically significant. This study evaluated the predictive performance of several models for ULST simulation and enhanced our understanding of the spatial influence and interactions among the underlying driving forces of the ULST variations

Integrating species metrics into biodiversity offsetting calculations to improve long-term persistence

Several methods of measuring biodiversity in development-offset trades exist. However, there is little consensus on which biodiversity metrics should be used for quantifying development impacts and assigning offsets. We simulated development impacts in a virtual landscape and offset these impacts using six biodiversity metrics: vegetation area, vegetation condition, habitat suitability, species abundance, metapopulation connectivity and rarity-weighted richness. We tested long-term impacts of metric choice during offsetting by combining simulated landscapes with population viability analyses. No net loss or net gains in habitat were achieved using all metrics except vegetation area and condition. Limited habitat and like-for-like requirements resulted in offsets exhausting available habitat in each vegetation class before offset requirements were met when using vegetation-based metrics. We also found that impact avoidance was an important driver in how much compensation offsets could deliver. When impacts avoided high-suitability habitats, all six metrics achieved no net loss or net gains for most species. However, when core habitats were developed, none of the metrics were able to consistently prevent population declines. Synthesis and application. When impacts on high-quality habitat were avoided, and assuming the protection and restoration benefits can occur in practice, vegetation-based metrics may produce offsets which deliver gains in species abundance equivalent to species-specific metrics. However, species-specific metrics outperformed vegetation-based metrics when core habitats were lost. Applying avoidance measures as a first step to minimise biodiversity impacts during development will significantly improve offset outcomes for species and result in greater long-term population benefits delivered through offsetting.Peer reviewe

Predicting climate heating impacts on riverine fish species diversity in a biodiversity hotspot region

Abstract Co-occurring biodiversity and global heating crises are systemic threats to life on Earth as we know it, especially in relatively rare freshwater ecosystems, such as in Iran. Future changes in the spatial distribution and richness of 131 riverine fish species were investigated at 1481 sites in Iran under optimistic and pessimistic climate heating scenarios for the 2050s and 2080s. We used maximum entropy modeling to predict species’ potential distributions by hydrologic unit (HU) occupancy under current and future climate conditions through the use of nine environmental predictor variables. The most important variable determining fish occupancy was HU location, followed by elevation, climate variables, and slope. Thirty-seven species were predicted to decrease their potential habitat occupancy in all future scenarios. The southern Caspian HU faces the highest future species reductions followed by the western Zagros and northwestern Iran. These results can be used by managers to plan conservational strategies to ease the dispersal of species, especially those that are at the greatest risk of extinction or invasion and that are in rivers fragmented by dams

The conservation impacts of ecological disturbance:Time-bound estimates of population loss and recovery for fauna affected by the 2019–2020 Australian megafires

Aim: After environmental disasters, species with large population losses may need urgent protection to prevent extinction and support recovery. Following the 2019-2020 Australian megafires, we estimated population losses and recovery in fire-affected fauna, to inform conservation status assessments and management. Location: Temperate and subtropical Australia. Time period 2019-2030 and beyond. Major taxa: Australian terrestrial and freshwater vertebrates; one invertebrate group. Methods: From > 1,050 fire-affected taxa, we selected 173 whose distributions substantially overlapped the fire extent. We estimated the proportion of each taxon's distribution affected by fires, using fire severity and aquatic impact mapping, and new distribution mapping. Using expert elicitation informed by evidence of responses to previous wildfires, we estimated local population responses to fires of varying severity. We combined the spatial and elicitation data to estimate overall population loss and recovery trajectories, and thus indicate potential eligibility for listing as threatened, or uplisting, under Australian legislation. Results: We estimate that the 2019-2020 Australian megafires caused, or contributed to, population declines that make 70-82 taxa eligible for listing as threatened; and another 21-27 taxa eligible for uplisting. If so-listed, this represents a 22-26% increase in Australian statutory lists of threatened terrestrial and freshwater vertebrates and spiny crayfish, and uplisting for 8-10% of threatened taxa. Such changes would cause an abrupt worsening of underlying trajectories in vertebrates, as measured by Red List Indices. We predict that 54-88% of 173 assessed taxa will not recover to pre-fire population size within 10 years/three generations. Main conclusions We suggest the 2019-2020 Australian megafires have worsened the conservation prospects for many species. Of the 91 taxa recommended for listing/uplisting consideration, 84 are now under formal review through national processes. Improving predictions about taxon vulnerability with empirical data on population responses, reducing the likelihood of future catastrophic events and mitigating their impacts on biodiversity, are critical