Multi-Scenario Species Distribution Modeling

Unravelling the impact of climate change on honey bees: An ensemble modelling approach to predict shifts in habitat suitability in Queensland, Australia

Honey bees play a vital role in providing essential ecosystem services and contributing to global agriculture. However, the potential effect of climate change on honey bee distribution is still not well understood. This study aims to identify the most influential bioclimatic and environmental variables, assess their impact on honey bee distribution, and predict future distribution. An ensemble modelling approach using the biomod2 package in R was employed to develop three models: a climate-only model, an environment-only model, and a combined climate and environment model. By utilising bioclimatic data (radiation of the wettest and driest quarters and temperature seasonality) from 1990 to 2009, combined with observed honey bee presence and pseudo absence data, this model predicted suitable locations for honey bee apiaries for two future time spans: 2020-2039 and 2060-2079. The climate-only model exhibited a true skill statistic (TSS) value of 0.85, underscoring the pivotal role of radiation and temperature seasonality in shaping honey bee distribution. The environment-only model, incorporating proximity to floral resources, foliage projective cover, and elevation, demonstrated strong predictive performance, with a TSS of 0.88, emphasising the significance of environmental variables in determining habitat suitability for honey bees. The combined model had a higher TSS of 0.96, indicating that the combination of climate and environmental variables enhances the model's performance. By the 2020-2039 period, approximately 88% of highly suitable habitats for honey bees are projected to transition from their current state to become moderate (14.84%) to marginally suitable (13.46%) areas. Predictions for the 2060-2079 period reveal a concerning trend: 100% of highly suitable land transitions into moderately (0.54%), marginally (17.56%), or not suitable areas (81.9%) for honey bees. These results emphasise the critical need for targeted conservation efforts and the implementation of policies aimed at safeguarding honey bees and the vital apiary industry.

A Methodology for Bridging the Gap between Regional- and City-Scale Climate Simulations for the Urban Thermal Environment

The main objective of this study is to bridge the gap between regional- and city-scale climate simulations, with the focus given to the thermal environment. A dynamic-statistical downscaling methodology for defining daily maximum (Tmax) and minimum (Tmin) temperatures is developed based on artificial neural networks (ANNs) and multiple linear regression models (MLRs). The approach involves the use of simulations from two EURO-CORDEX regional climate models (RCMs) (at approximately 12 km × 12 km) that are further downscaled to a finer resolution (1 km × 1 km). A feature selection methodology is applied to select the optimum subset of parameters for training the machine learning models. The downscaling methodology is initially applied to two RCMs, driven by the ERA-Interim reanalysis (2008–2011) and high-resolution urban climate model simulations (UrbClims). The performance of the relationships is validated and found to successfully simulate the spatiotemporal distribution of Tmax and Tmin over Athens. Finally, the relationships that were extracted by the models are further used to quantify changes for Tmax and Tmin in high resolution, between the historical period (1971–2000) and mid-century (2041–2071) climate projections for two different representative concentration pathways (RCP4.5 and RCP8.5). Based on the results, both mean Tmax and Tmin are estimated to increase by 1.7 °C and 1.5 °C for RCP4.5 and 2.3 °C and 2.1 °C for RCP8.5, respectively, with distinct spatiotemporal patterns over the study area.

Two Sides of the Same Desert: Floristic Connectivity and Isolation Along the Hyperarid Coast and Precordillera in Peru and Chile

In this study we aim at refining our understanding of the floristic connectivity of the loma- and precordillera floras of southern Peru and northern Chile and the parameters determining vegetation cover in this region. We used multivariate analyses to test for floristic- and environmental similarity across 53 precordillera and loma locations in Peru and Chile. We propose the use of predictive modeling in estimating the extent of desert vegetation as a complementary method to remote sensing. We created habitat suitability models for the vegetation on the coast and in the precordillera based on a combination of latent bioclimatic variables and additional environmental predictors using Maxent. We found Peruvian and Chilean lomas to be strongly floristically differentiated, as are the Chilean precordillera and lomas. Conversely, there is clear connectivity between both the Peruvian loma- and precordillera floras on the one hand and the Peruvian and Chilean precordillera floras on the other. Divergent environmental conditions were retrieved as separating the precordillera and lomas, while environmental conditions are not differentiated between Peruvian and Chilean lomas. Peruvian and Chilean precordilleras show a gradual change in environmental conditions. Habitat suitability models of vegetation cover retrieve a gap for the loma vegetation along the coast between Peru and Chile, while a continuous belt of suitable habitats is retrieved along the Andean precordillera. Unsuitable habitat for loma vegetation north and south of the Chilean and Peruvian border likely represents an ecogeographic barrier responsible for the floristic divergence of Chilean and Peruvian lomas. Conversely, environmental parameters change continuously along the precordilleras, explaining the moderate differentiation of the corresponding floras. Our results underscore the idea of the desert core acting as an ecogeographic barrier separating the coast from the precordillera in Chile, while it has a more limited isolating function in Peru. We also find extensive potentially suitable habitats for both loma- and precordillera vegetation so far undetected by methods of remote sensing.

Evaluation of Land Suitability Methods with Reference to Neglected and Underutilised Crop Species: A Scoping Review

In agriculture, land use and land classification address questions such as "where", "why" and "when" a particular crop is grown within a particular agroecology. To date, there are several land suitability analysis (LSA) methods, but there is no consensus on the best method for crop suitability analysis. We conducted a scoping review to evaluate methodological strategies for LSA. Secondary to this, we assessed which of these would be suitable for neglected and underutilised crop species (NUS). The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multi-criteria decision-making (MCDM) methods such as analytical hierarchy process (AHP) (14.9%) and fuzzy methods (12.9%); crop simulation models (9.9%) and machine learning related methods (25.7%) are gaining popularity over traditional methods. The MCDM methods, namely AHP and fuzzy, are commonly applied to LSA while crop models and machine learning related methods are gaining popularity. A total of 67 parameters from climatic, hydrology, soil, socio-economic and landscape properties are essential in LSA. Unavailability and the inclusion of categorical datasets from social sources is a challenge. Using big data and Internet of Things (IoT) improves the accuracy and reliability of LSA methods. The review expects to provide researchers and decision-makers with the most robust methods and standard parameters required in developing LSA for NUS. Qualitative and quantitative approaches must be integrated into unique hybrid land evaluation systems to improve LSA.

Multi-criteria suitability analysis for neglected and underutilised crop species in South Africa

Several neglected and underutilised species (NUS) provide solutions to climate change and creating a Zero Hunger world, the Sustainable Development Goal 2. Several NUS are drought and heat stress-tolerant, making them ideal for improving marginalised cropping systems in drought-prone areas. However, owing to their status as NUS, current crop suitability maps do not include them as part of the crop choices. This study aimed to develop land suitability maps for selected NUS [sorghum, (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth and taro (Colocasia esculenta)] using Analytic Hierarchy Process (AHP) in ArcGIS. Multidisciplinary factors from climatic, soil and landscape, socio-economic and technical indicators overlaid using Weighted Overlay Analysis. Validation was done through field visits, and area under the curve (AUC) was used to measure AHP model performance. The results indicated that sorghum was highly suitable (S1) = 2%, moderately suitable (S2) = 61%, marginally suitable (S3) = 33%, and unsuitable (N1) = 4%, cowpea S1 = 3%, S2 = 56%, S3 = 39%, N1 = 2%, amaranth S1 = 8%, S2 = 81%, S3 = 11%, and taro S1 = 0.4%, S2 = 28%, S3 = 64%, N1 = 7%, of calculated arable land of SA (12 655 859 ha). Overall, the validation showed that the mapping exercises exhibited a high degree of accuracies (i.e. sorghum AUC = 0.87, cowpea AUC = 0.88, amaranth AUC = 0.95 and taro AUC = 0.82). Rainfall was the most critical variable and criteria with the highest impact on land suitability of the NUS. Results of this study suggest that South Africa has a huge potential for NUS production. The maps developed can contribute to evidence-based and site-specific recommendations for NUS and their mainstreaming. Also, the maps can be used to design appropriate production guidelines and to support existing policy frameworks which advocate for sustainable intensification of marginalised cropping systems through increased crop diversity and the use of stress-tolerant food crops.

ResNet-Locust-BN Network-Based Automatic Identification of East Asian Migratory Locust Species and Instars from RGB Images

Locusts are agricultural pests found in many parts of the world. Developing efficient and accurate locust information acquisition techniques helps in understanding the relation between locust distribution density and structural changes in locust communities. It also helps in understanding the hydrothermal and vegetation growth conditions that affect locusts in their habitats in various parts of the world as well as in providing rapid and accurate warnings on locust plague outbreak. This study is a preliminary attempt to explore whether the batch normalization-based convolutional neural network (CNN) model can be applied used to perform automatic classification of East Asian migratory locust (AM locust), Oxya chinensis (rice locusts), and cotton locusts. In this paper, we present a way of applying the CNN technique to identify species and instars of locusts using the proposed ResNet-Locust-BN model. This model is based on the ResNet architecture and involves introduction of a BatchNorm function before each convolution layer to improve the network’s stability, convergence speed, and classification accuracy. Subsequently, locust image data collected in the field were used as input to train the model. By performing comparison experiments of the activation function, initial learning rate, and batch size, we selected ReLU as the preferred activation function. The initial learning rate and batch size were set to 0.1 and 32, respectively. Experiments performed to evaluate the accuracy of the proposed ResNet-Locust-BN model show that the model can effectively distinguish AM locust from rice locusts (93.60% accuracy) and cotton locusts (97.80% accuracy). The model also performed well in identifying the growth status information of AM locusts (third-instar (77.20% accuracy), fifth-instar (88.40% accuracy), and adult (93.80% accuracy)) with an overall accuracy of 90.16%. This is higher than the accuracy scores obtained by using other typical models: AlexNet (73.68%), GoogLeNet (69.12%), ResNet 18 (67.60%), ResNet 50 (80.84%), and VggNet (81.70%). Further, the model has good robustness and fast convergence rate.