Scale effects on the performance of niche-based models of freshwater fish distributions

Predicting Potential Habitat Changes of Two Invasive Alien Fish Species with Climate Change at a Regional Scale

Developing national-level policies related to climate change induced expansions of invasive species requires predictive modelling at a regional scale level. This study aimed to predict future changes in the habitat distributions of two major invasive alien fish species, Micropterus salmoides and Lepomis macrochirus, in South Korea. An ensemble system with multiple species distribution models was used for the prediction, and gridded water portion data from the linear-structure information on river channels inputted as habitat characteristics of freshwater ecosystem into the models. Bioclimatic variables at 20-year intervals from 2001 to 2100 were generated from predicted temperature and precipitation data under the representative concentration pathway 4.5 and 8.5 scenarios. The overall distribution probabilities of the potential habitats increased with time in both climate change scenarios, and the potential habitats were predicted to expand to upstream areas. Combined with regional ecological value information, such as biodiversity in freshwater ecosystems, these results can be an important basis for deriving regional priority information for managing alien species in climate change. Additionally, the modelling approach is highly applicable to various national-level policies for ecosystem conservation since it is not greatly restricted by spatial scales.

Modelling sexually deceptive orchid species distributions under future climates: the importance of plant-pollinator interactions

Biotic interactions play an important role in species distribution models, whose ignorance may cause an overestimation of species' potential distributions. Species of the family Orchidaceae are almost totally dependent on mycorrhizal symbionts and pollinators, with sexually deceptive orchids being often highly specialized, and thus the interactions with their pollinators are expected to strongly affect distribution predictions. We used Maxent algorithm to explore the extent of current and future habitat suitability for two Greek endemic sexually deceptive orchids (Ophrys argolica and Ophrys delphinensis) in relation to the potential distribution of their unique pollinator (Anthophora plagiata). Twelve climate change scenarios were used to predict future distributions. Results indicated that the most important factors determining potential distribution were precipitation seasonality for O. argolica and geological substrate for O. delphinensis. The current potential distribution of the two orchids was almost of the same extent but spatially different, without accounting for their interaction with A. plagiata. When the interaction was included in the models, their potentially suitable area decreased for both species. Under future climatic conditions, the effects of the orchid-pollinator interaction were more intense. Specifically, O. argolica was restricted in specific areas of southern Greece, whereas O. delphinensis was expected to become extinct. Our findings highlighted the significant role of plant–pollinator interactions in species distribution models. Failing to study such interactions might expose plant species to serious conservation issues.

Modeling the Spatial Distribution of Swordfish (Xiphias gladius) Using Fishery and Remote Sensing Data: Approach and Resolution

Swordfish, Xiphias gladius (Linnaeus, 1758), is a commercially important species that is widely distributed throughout three oceans. This species inhabits oceanic waters with preferred environmental ranges and migrates vertically to the surface layer for feeding. However, the spatial distribution pattern and habitat preferences of swordfish have been rarely studied in the Pacific Ocean due to the wide geographic range of this species. This study examined the spatial distribution and preferred ranges of environmental variables for swordfish using two approaches, generalized additive models and habitat suitability index methods, with different spatio-temporal data resolution scales. Results indicated that sea surface temperature is the most important factor determining swordfish spatial distribution. Habitat spatial pattern and preferred environmental ranges, estimated using various modeling approaches, were robust relative to the spatio-temporal data resolution scales. The models were validated by examining the consistency between predictions and untrained actual observations, which all predicted a high relative density of swordfish in the tropical waters of the central Pacific Ocean, with no obvious seasonal movement. Results from this study, based on fishery and remote sensing data with wide spatial coverage, could benefit the conservation and management of fisheries for highly migratory species such as swordfish and tuna.

Predicting impacts of future climate change and hydropower development towards habitats of native and non-native fishes

Climate change and hydropower development are two primary stressors affecting riverine ecosystems and both stressors facilitate invasions by non-native species. However, little study has focused on how habitats of native and non-native fishes may be affected by independent or combined impacts of such stressors. Here we used the Jinsha River as an example to predict habitat change and distributional shift of native and non-native fishes with species distribution models. The Jinsha River Basin has nearly 40 cascade dams constructed or planned and located in the Tibetan Plateau, which is sensitive to future climate change. Two climate change scenarios and future hydropower development were combined to produce five scenarios of future changes. Under the impacts of independent extreme climate change or hydropower development, non-native fishes showed greater habitat gain in total, while native fishes shifted their distribution into tributaries and higher elevations, and impacts were stronger in combined scenarios. Habitat overlap between the two groups also increased in future scenarios. Certain fish traits correlated with stressors in habitat change prediction. River basins with hydropower development were shown to face higher risk of non-native fishes invasion under future climate change. As the most biodiverse river basins globally are threatened by hydropower development, our results emphasize the importance of regulating non-native fish introduction in reservoirs. Our approaches are also applicable to other systems globally to better understand how hydropower development and climate change may increase invasion risk, and therefore help conserve native species effectively.

From topography to hydrology-The modifiable area unit problem impacts freshwater species distribution models

Species distribution models (SDMs) are statistical tools to identify potentially suitable habitats for species. For SDMs in river ecosystems, species occurrences and predictor data are often aggregated across subcatchments that serve as modeling units. The level of aggregation (i.e., model resolution) influences the statistical relationships between species occurrences and environmental predictors-a phenomenon known as the modifiable area unit problem (MAUP), making model outputs directly contingent on the model resolution. Here, we test how model performance, predictor importance, and the spatial congruence of species predictions depend on the model resolution (i.e., average subcatchment size) of SDMs. We modeled the potential habitat suitability of 50 native fish species in the upper Danube catchment at 10 different model resolutions. Model resolutions were derived using a 90-m digital-elevation model by using the GRASS-GIS module r.watershed. Here, we decreased the average subcatchment size gradually from 632 to 2 km2. We then ran ensemble SDMs based on five algorithms using topographical, climatic, hydrological, and land-use predictors for each species and resolution. Model evaluation scores were consistently high, as sensitivity and True Skill Statistic values ranged from 86.1-93.2 and 0.61-0.73, respectively. The most contributing predictor changed from topography at coarse, to hydrology at fine resolutions. Climate predictors played an intermediate role for all resolutions, while land use was of little importance. Regarding the predicted habitat suitability, we identified a spatial filtering from coarse to intermediate resolutions. The predicted habitat suitability within a coarse resolution was not ported to all smaller, nested subcatchments, but only to a fraction that held the suitable environmental conditions. Across finer resolutions, the mapped predictions were spatially congruent without such filter effect. We show that freshwater SDM predictions can have consistently high evaluation scores while mapped predictions differ significantly and are highly contingent on the underlying subcatchment size. We encourage building freshwater SDMs across multiple catchment sizes, to assess model variability and uncertainties in model outcomes emerging from the MAUP.