Land use changes could modify future negative effects of climate change on old-growth forest indicator species

Predicting the impact of climate change and land use change on the potential distribution of two economic forest trees in Northeastern China

Young shoots of Aralia elata and young leaves of Eleutherococcus senticosus are two major non-timber forest products in northeastern China. However, human activities and climate change have resulted in serious threats to the habitats of two trees, which greatly limits resource conservation and exploitation of economic forest trees. We used the MaxEnt model to predict the suitable habitats of the two economic trees and analyzed the dominant factors affecting their distribution. The results showed that the suitable habitat areas of A. elata and E. senticosus in the current period were 159950 km2 and 123449 km2, respectively, and the suitable habitats of both economic forest trees were located in the eastern part of the northeast region. Climate factors (Annual precipitation, Precipitation Seasonality) and land use factors are important variables influencing changes in suitable habitat for both trees. With the change of climate and land use in the future, the overall trend of suitable habitat for both economic forest trees shows a northward and then a southward migration. These results may provide assistance in developing strategies for resource conservation and sustainable use of A. elata and E. senticosus, and we suggest that stable and suitable habitats should be selected as areas for in situ conservation and breeding of the two economic forest trees.

Alternative measures of trait-niche relationships: A test on dispersal traits in saproxylic beetles

Functional trait approaches are common in ecology, but a lack of clear hypotheses on how traits relate to environmental gradients (i.e., trait-niche relationships) often makes uncovering mechanisms difficult. Furthermore, measures of community functional structure differ in their implications, yet inferences are seldom compared among metrics. Community-weighted mean trait values (CWMs), a common measure, are largely driven by the most common species and thus do not reflect community-wide trait-niche relationships per se. Alternatively, trait-niche relationships can be estimated across a larger group of species using hierarchical joint species distribution models (JSDMs), quantified by a parameter Γ. We investigated how inferences about trait-niche relationships are affected by the choice of metric. Using deadwood-dependent (saproxylic) beetles in fragmented Finnish forests, we followed a protocol for investigating trait-niche relationships by (1) identifying environmental filters (climate, forest age, and deadwood volume), (2) relating these to an ecological function (dispersal ability), and (3) identifying traits related to this function (wing morphology). We tested 18 hypothesized dispersal relationships using both CWM and Γ estimates across these environmental gradients. CWMs were more likely than Γ to show support for trait-niche relationships. Up to 13% of species' realized niches were explained by dispersal traits, but the directions of effects were consistent with fewer than 11%-39% of our 18 trait-niche hypotheses (depending on the metric used). This highlights the difficulty in connecting morphological traits and ecological functions in insects, despite the clear conceptual link between landscape connectivity and flight-related traits. Caution is thus warranted in hypothesis development, particularly where apparent trait-function links are less clear. Inferences differ when CWMs versus Γ estimates are used, necessitating the choice of a metric that reflects study questions. CWMs help explain the effects of environmental gradients on community trait composition, whereas the effects of traits on species' niches are better estimated using hierarchical JSDMs.

Modeling the Wintering Habitat Distribution of the Black Stork in Shaanxi, China: A Hierarchical Integration of Climate and Land Use/Land Cover Data

Species distribution models (SDMs) are effective tools for wildlife conservation and management, as they employ the quantification of habitat suitability and environmental niches to evaluate the patterns of species distribution. The utilization of SDMs at various scales in a hierarchical approach can provide additional and complementary information, significantly improving decision-making in local wildlife conservation initiatives. In this study, we considered the appropriate spatial scale and data resolution to execute species distribution modeling, as these factors greatly influence the modeling procedures. We developed SDMs for wintering black storks at both the regional and local scales. At the regional scale, we used climatic and climate-driven land use/land cover (LULC) variables, along with wintering occurrence points, to develop models for mainland China. At the local scale, we used local environmental variables and locally gathered wintering site data to develop models for Shaanxi province. The predictions from both the regional and local models were then combined at the provincial level by overlapping suitable areas based on climatic and local conditions. We compared and evaluated the resulting predictions using seven statistical metrics. The national models provide information on the appropriate climatic conditions for the black stork during the wintering period throughout China, while the provincial SDMs capture the important local ecological factors that influence the suitability of habitats at a finer scale. As anticipated, the national SDMs predict a larger extent of suitable areas compared to the provincial SDMs. The hierarchical prediction approach is considered trustworthy and, on average, yields better outcomes than non-hierarchical methods. Our findings indicate that human-driven LULC changes have a significant and immediate impact on the wintering habitat of the black stork. However, the effects of climate change seem to be reducing the severity of this impact. The majority of suitable wintering habitats lie outside the boundaries of protected areas, highlighting the need for future conservation and management efforts to prioritize addressing these conservation gaps and focusing on the protection of climate refuges.

Disentangling the impacts of climate and land cover changes on habitat suitability of common pheasant Phasianus colchicus along elevational gradients in Iran

Climate and land cover change are critical drivers of avian species range shift. Thus, predicting avian species' response to the land and climate changes and identifying their future suitable habitats can help their conservation planning. The common pheasant (Phasianus colchicus) is a species of conservation concern in Iran and is included in the list of Iran's protected avian species. The species faces multiple threats such as habitat destruction, land cover change, and overhunting in the country. In this study, we model the potential impacts of future climate and land cover change on the habitat suitability of common pheasant (Phasianus colchicus) along elevational gradients in Mazandaran province in Iran. We used shared socioeconomic pathways (SSP) scenarios and the 2015-2020 trend to generate possible future land cover projections for 2050. As for climate change projections, we used representative concentration pathway (RCP) scenarios. Next, we applied current and future climate and land cover projections to investigate how habitat suitability of common pheasant will change between 2020 and 2050 using species distribution modeling (SDM). Our results show that the species has 6000 km² suitable habitat; however, between 900 and 1965 km² of its habitat may be reduced by 2050. Furthermore, we found that the severity of the effects of climate and land cover change varies at different altitudes. At low altitudes, the impact of changing land structure is superior. Instead, climate change has a critical role in habitat loss at higher altitudes and imposes a limiting role on the potential range shifts. Overall, this study demonstrates the vital role of land cover and climate change in better understanding the potential alterations in avian distribution.

More future synergies and less trade-offs between forest ecosystem services with natural climate solutions instead of bioeconomy solutions

To reach the Paris Agreement, societies need to increase the global terrestrial carbon sink. There are many climate change mitigation solutions (CCMS) for forests, including increasing bioenergy, bioeconomy, and protection. Bioenergy and bioeconomy solutions use climate-smart, intensive management to generate high quantities of bioenergy and bioproducts. Protection of (semi-)natural forests is a major component of "natural climate solution" (NCS) since forests store carbon in standing biomass and soil. Furthermore, protected forests provide more habitat for biodiversity and non-wood ecosystem services (ES). We investigated the impacts of different CCMS and climate scenarios, jointly or in isolation, on future wood ES, non-wood ES, and regulating ES for a major wood provider for the international market. Specifically, we projected future ES given by three CCMS scenarios for Sweden 2020-2100. In the long term, fulfilling the increasing wood demand through bioenergy and bioeconomy solutions will decrease ES multifunctionality, but the increased stand age and wood stocks induced by rising greenhouse gas (GHG) concentrations will partially offset these negative effects. Adopting bioenergy and bioeconomy solutions will have a greater negative impact on ES supply than adopting NCS. Bioenergy or bioeconomy solutions, as well as increasing GHG emissions, will reduce synergies and increase trade-offs in ES. NCS, by contrast, increases the supply of multiple ES in synergy, even transforming current ES trade-offs into future synergies. Moreover, NCS can be considered an adaptation measure to offset negative climate change effects on the future supplies of non-wood ES. In boreal countries around the world, forestry strategies that integrate NCS more deeply are crucial to ensure a synergistic supply of multiple ES.

Uncertainty, Complexity and Constraints: How Do We Robustly Assess Biological Responses under a Rapidly Changing Climate?

How robust is our assessment of impacts to ecosystems and species from a rapidly changing climate during the 21st century? We examine the challenges of uncertainty, complexity and constraints associated with applying climate projections to understanding future biological responses. This includes an evaluation of how to incorporate the uncertainty associated with different greenhouse gas emissions scenarios and climate models, and constraints of spatiotemporal scales and resolution of climate data into impact assessments. We describe the challenges of identifying relevant climate metrics for biological impact assessments and evaluate the usefulness and limitations of different methodologies of applying climate change to both quantitative and qualitative assessments. We discuss the importance of incorporating extreme climate events and their stochastic tendencies in assessing ecological impacts and transformation, and provide recommendations for better integration of complex climate–ecological interactions at relevant spatiotemporal scales. We further recognize the compounding nature of uncertainty when accounting for our limited understanding of the interactions between climate and biological processes. Given the inherent complexity in ecological processes and their interactions with climate, we recommend integrating quantitative modeling with expert elicitation from diverse disciplines and experiential understanding of recent climate-driven ecological processes to develop a more robust understanding of ecological responses under different scenarios of future climate change. Inherently complex interactions between climate and biological systems also provide an opportunity to develop wide-ranging strategies that resource managers can employ to prepare for the future.

Effects of climate change and land cover on the distributions of a critical tree family in the Philippines

Southeast Asian forests are dominated by the tree family Dipterocarpaceae, whose abundance and diversity are key to maintaining the structure and function of tropical forests. Like most biodiversity, dipterocarps are threatened by deforestation and climate change, so it is crucial to understand the potential impacts of these threats on current and future dipterocarp distributions. We developed species distribution models (SDMs) for 19 species of dipterocarps in the Philippines, which were projected onto current and two 2070 representative concentration pathway (RCP) climate scenarios, RCP 4.5 and 8.5. Current land cover was incorporated as a post-hoc correction to restrict projections onto intact habitats. Land cover correction alone reduced current species distributions by a median 67%, and within protected areas by 37%. After land cover correction, climate change reduced distributions by a median 16% (RCP 4.5) and 27% (RCP 8.5) at the national level, with similar losses in protected areas. There was a detectable upward elevation shift of species distributions, consisting of suitable habitat losses below 300 m and gains above 600 m. Species-rich stable areas of continued habitat suitability (i.e., climate macrorefugia) fell largely outside current delineations of protected areas, indicating a need to improve protected area planning. This study highlights how SDMs can provide projections that can inform protected area planning in the tropics.

Downscaling of Long-Term Global Scenarios to Regions with a Forest Sector Model

Research Highlights: Long-term global scenarios give insights on how social and economic developments and international agreements may impact land use, trade, product markets, and carbon balances. They form a valuable basis for forming national forest policies. Many aspects related to long-term management of forests and consequences for biodiversity and ecosystem services can only be addressed at regional and landscape levels. In order to be attended to in the policy process, there is a need for a method that downscales national scenarios to these finer levels. Background and Objectives: Regional framework conditions depend on management activities in the country as a whole. The aim of this study is to evaluate the use of a forest sector model (FSM) as a method for downscaling national scenarios results to regional level. The national FSM takes the global scenario data (e.g., harvest level and market prices over time) and solves the national problem. The result for the region of interest is taken as framework conditions for the regional study. Materials and Methods: Two different specifications are tested. One lets product volumes and prices represent endogenous variables in the FSM model. The other takes volumes and prices from the global scenario as exogenous parameters. The first specification attains a maximum net social payoff whereas the second specification means that net present value is maximized under a harvest constraint. Results: The maximum net social payoff specification conforms better to economic factors than the maximum net present value specification but could give national harvest volume trajectories that deviates from what is derived from the global model. This means that regional harvest activity can deviate considerably from the national average, attesting to the benefit of the use of the FSM-based method

Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica

Six bias correction (BC) methods; delta-method (DT), linear scaling (LS), power transformation of precipitation (PTR), empirical quantile mapping (EQM), gamma quantile mapping (GQM) and gamma-pareto quantile mapping (GPQM) were applied to adjust the biases of historical monthly precipitation outputs from five General Circulation Models (GCMs) dynamically downscaled by two Regional Climate Models (RCMs) for a total of seven different GCM-RCM pairs over Costa Rica. High-resolution gridded precipitation observations were used for the control period 1951–1980 and validated over the period 1981–1995. Results show that considerable biases exist between uncorrected GCM-RCM outputs and observations, which largely depend on GCM-RCM pair, seasonality, climatic region and spatial resolution. After the application of bias correction, substantial biases reductions and comparable performances among most BC methods were observed for most GCM-RCM pairs; with EQM and DT marginally outperforming the remaining methods. Consequently, EQM and DT were selectively applied to correct the biases of precipitation projections from each individual GCM-RCM pair for a near-future (2011–2040), mid-future (2041–2070) and far-future (2071–2100) period under Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 using the control period 1961–1990. Results from the bias-corrected future ensemble-mean anticipate a marked decreasing trend in precipitation from near to far-future periods during the dry season (December, January, February (DJF) and March, April, May (MAM)) for RCP4.5 and 8.5; with pronounced drier conditions for those climatic regions draining towards the Pacific Ocean. In contrast, mostly wetter conditions are expected during the dry season under RCP2.6, particularly for the Caribbean region. In most of the country, the greatest decrease in precipitation is projected at the beginning of the rainy season (June, July, August (JJA)) for the far-future period under RCP8.5, except for the Caribbean region where mostly wetter conditions are anticipated. Regardless of future period, slight increases in precipitation with higher radiative forcing are expected for SON excluding the Caribbean region, where precipitation is likely to increase with increasing radiative forcing and future period. This study demonstrates that bias correction should be considered before direct application of GCM-RCM precipitation projections over complex territories such as Costa Rica.