First assessment of habitat suitability and connectivity for the golden jackal in north-eastern Italy

AbstractCompared with the rapid expansion across Europe, the golden jackal colonization of Italy is still limited and slow. No study focused on the habitat selection or landscape connectivity for this species was performed in Italy; thus, the potential distribution and dispersal patterns in the country remain unknown. Our objectives were to evaluate the suitability of the Friuli-Venezia Giulia region (north-eastern Italy) for the golden jackal, as well as to identify the ecological corridors connecting the areas currently occupied by the species. Corridors modelling allowed us both to hypothesize the dispersal dynamics occurring in the study region and to identify possible obstacles to future range expansion. We surveyed golden jackal presence in two study areas, covering an area of 500 km2, from March 2017 to February 2018. Using collected data, we modelled the species home-range scale habitat suitability based on an ensemble modelling approach. Subsequently, a habitat suitability prediction at a finer scale was used to estimate landscape resistance, starting from which, we modelled dispersal corridors among areas currently occupied by the species using a factorial least cost path and a cumulative resistant kernel approach. Our results indicated a moderate potential for large parts of the study region to support the occurrence of golden jackal family groups, whose presence seems to be mainly driven by the presence of wide areas covered by broadleaved forests and shrublands and by the absence of wide intensive agricultural areas. The predicted connectivity networks showed that three main permeable corridors are likely to connect golden jackal occurrence areas within the study region, while all the other corridors are characterized by a very low path density. Both the habitat selection and connectivity analyses showed a strong negative impact of the intensive cultivated plain on species stable presence and movement providing critical information for the conservation of the golden jackal in Italy

Toward community predictions : Multi-scale modelling of mountain breeding birds' habitat suitability, landscape preferences, and environmental drivers

Across a large mountain area of the western Swiss Alps, we used occurrence data (presence-only points) of bird species to find suitable modelling solutions and build reliable distribution maps to deal with biodiversity and conservation necessities of bird species at finer scales. We have performed a multi-scale method of modelling, which uses distance, climatic, and focal variables at different scales (neighboring window sizes), to estimate the efficient scale of each environmental predictor and enhance our knowledge on how birds interact with their complex environment. To identify the best radius for each focal variable and the most efficient impact scale of each predictor, we have fitted univariate models per species. In the last step, the final set of variables were subsequently employed to build ensemble of small models (ESMs) at a fine spatial resolution of 100 m and generate species distribution maps as tools of conservation. We could build useful habitat suitability models for the three groups of species in the national red list. Our results indicate that, in general, the most important variables were in the group of bioclimatic variables including "Bio11" (Mean Temperature of Coldest Quarter), and "Bio 4" (Temperature Seasonality), then in the focal variables including "Forest", "Orchard", and "Agriculture area" as potential foraging, feeding and nesting sites. Our distribution maps are useful for identifying the most threatened species and their habitat and also for improving conservation effort to locate bird hotspots. It is a powerful strategy to improve the ecological understanding of the distribution of bird species in a dynamic heterogeneous environment.Peer reviewe

Toward community predictions: Multi‐scale modelling of mountain breeding birds' habitat suitability, landscape preferences, and environmental drivers

Across a large mountain area of the western Swiss Alps, we used occurrence data (presence‐only points) of bird species to find suitable modeling solutions and build reliable distribution maps to deal with biodiversity and conservation necessities of bird species at finer scales. We have performed a multi‐scale method of modeling, which uses distance, climatic, and focal variables at different scales (neighboring window sizes), to estimate the efficient scale of each environmental predictor and enhance our knowledge on how birds interact with their complex environment. To identify the best radius for each focal variable and the most efficient impact scale of each predictor, we have fitted univariate models per species. In the last step, the final set of variables were subsequently employed to build an ensemble of small models (ESMs) at a fine spatial resolution of 100 m and generate species distribution maps as tools of conservation. We could build useful habitat suitability models for the three groups of species in the national red list. Our results indicate that, in general, the most important variables were in the group of bioclimatic variables including “Bio11” (Mean Temperature of Coldest Quarter), and “Bio 4” (Temperature Seasonality), then in the focal variables including “Forest”, “Orchard”, and “Agriculture area” as potential foraging, feeding and nesting sites. Our distribution maps are useful for identifying the most threatened species and their habitat and also for improving conservation effort to locate bird hotspots. It is a powerful strategy to improve the ecological understanding of the distribution of bird species in a dynamic heterogeneous environment

Relationships between survival and habitat suitability of semi- aquatic mammals

Spatial distribution and habitat selection are integral to the study of animal ecology. Habitat selection may optimize the fitness of individuals. Hutchinsonian niche theory posits the fundamental niche of species would support the persistence or growth of populations. Although niche-based species distribution models (SDMs) and habitat suitability models (HSMs) such as maximum entropy (Maxent) have demonstrated fair to excellent predictive power, few studies have linked the prediction of HSMs to demographic rates. We aimed to test the prediction of Hutchinsonian niche theory that habitat suitability (i.e., likelihood of occurrence) would be positively related to survival of American beaver (Castor canadensis), a North American semi-aquatic, herbivorous, habitat generalist. We also tested the prediction of ideal free distribution that animal fitness, or its surrogate, is independent of habitat suitability at the equilibrium. We estimated beaver monthly survival probability using the Barker model and radio telemetry data collected in northern Alabama, United States from January 2011 to April 2012. A habitat suitability map was generated with Maxent for the entire study site using landscape variables derived from the 2011 National Land Cover Database (30-m resolution). We found an inverse relationship between habitat suitability index and beaver survival, contradicting the predictions of niche theory and ideal free distribution. Furthermore, four landscape variables selected by American beaver did not predict survival. The beaver population on our study site has been established for 20 or more years and, subsequently, may be approaching or have reached the carrying capacity. Maxent-predicted increases in habitat use and subsequent intraspecific competition may have reduced beaver survival. Habitat suitability-fitness relationships may be complex and, in part, contingent upon local animal abundance. Future studies of mechanistic SDMs incorporating local abundance and demographic rates are needed

Predicting the past, present and future distributions of an endangered marsupial in a semi‐arid environment

This is the final version. Available on open access from Wiley via the DOI in this recordGlobally, the impacts of anthropogenic climate change can displace species into more favourable climates. Semi‐arid desert specialists, such as the sandhill dunnart, Sminthopsis psammophila, are typically susceptible to rainfall deficits, wildfires and extreme temperatures caused by anthropogenic climate change. We first used maximum entropy (MaxEnt) species distribution models (SDMs) to predict the current distribution of S. psammophila. Between 2016 and 2018, we ground validated the model’s predictions throughout Western Australia, confirming S. psammophila in 18 locations in which it was predicted to occur. The predicted distribution of S. psammophila appears mostly constrained to within its known range. However, S. psammophila was verified 150 km north of its range in Western Australia and connectivity between the South Australian populations was correctly predicted. In 2019, we used updated occurrence data to project SDMs for S. psammophila during the mid‐Holocene, present day and under two future representative concentration pathways (RCPs) of RCP 4.5 (an optimistic emissions scenario) and RCP 8.5 (“business as usual”) for 2050 and 2070. By 2050 (RCP 8.5), almost all Western Australian Great Victoria Desert (WAGVD) habitat is predicted to be unsuitable for S. psammophila. By 2070 (RCP 8.5), the climates of the WAGVD and Yellabinna Regional Reserve populations are predicted to become unsuitable, and the species’ geographical range is predicted to contract in Australia by 80%. However, the 2070 (RCP 4.5) scenario predicts that this contraction could be halved. As a sandy desert specialist, the distribution of S. psammophila is geographically limited at its southern bounds due to the cessation of suitable spinifex (Triodia spp.) habitats, and so further extension of the range southwards is not possible. Sympatric desert species may be similarly affected, and we suggest that SDMs will be a useful tool in helping to predict the effects of climate change on their distributions.Goldfields Environmental Management Group (GEMG

Modeling the geographic spread and proliferation of invasive alien plants (IAPs) into new ecosystems using multi-source data and multiple predictive models in the Heuningnes catchment, South Africa

The geographic spread and proliferation of Invasive Alien Plants (IAPs) into new ecosystems requires accurate, constant, and frequent monitoring particularly under the changing climate to ensure the integrity and resilience of affected as well as vulnerable ecosystems. This study thus aimed to understand the distribution and shifts of IAPs and the factors influencing such distribution at the catchment scale to minimize their risks and impacts through effective management. Three machine learning Species Distribution Modeling (SDM) techniques, namely, Random Forest (RF), Maximum Entropy (MaxEnt), Boosted Regression Trees (BRT) and their respective ensemble model were used to predict the potential distribution of IAPs within the catchment. The current and future bioclimatic variables, environmental and Sentinel-2 Multispectral Instrument satellite data were used to fit the models to predict areas at risk of IAPs invasions in the Heuningnes catchment, South Africa. The present and two future climatic scenarios from the Community Climate System Model (CCSM4) were considered in modeling the potential distribution of these species. The two future scenarios represented the minimum and maximum atmospheric carbon Representative Concentration Pathways (RCP) 2.6 and 8.5 for 2050 (average for 2041–2060)

Flood susceptibility mapping to improve models of species distributions

As significant ecosystem disturbances flooding events are expected to increase in both frequency and severity due to climate change, underscoring the critical need to understand their impact on biodiversity. In this study, we employ advanced remote sensing and machine learning methodologies to investigate the effects of flooding on biodiversity, from individual species to broader ecological communities. Specifically, we utilized Sentinel-1 synthetic aperture radar (SAR) images and an ensemble of machine-learning algorithms to derive a flood susceptibility indicator. Our primary objective is to investigate the potential benefits of incorporating flood susceptibility, as a proxy for flood risk, into species distribution models (SDMs). By doing so, we aim to improve the performance of SDMs and gain deeper insights into the consequences of floods to biodiversity. Within the biodiverse landscape of the Zagros Mountains, a crucial Irano-Anatolian biodiversity hotspots, we examined the sensitivity of mammals, amphibians, and reptiles’ distributions to flooding. Our analysis compared the performance of models that combined flood susceptibility with climate variables against models relying solely on climate variables. The results indicate that the inclusion of flood susceptibility significantly improves the capacity of models to explain and map species distributions for 67% of the species in our study region. Notably, amphibians and mammals are more profoundly affected by flooding compared to reptiles. The study highlights the importance of incorporating flood susceptibility as a predictor variable in species distribution models to improve the baseline characterization of potential species distributions. The importance of this variable will obviously depend on the regional context and the species studied but its relevance is likely to increase with climate change. In summary, our research demonstrates the integration of remote sensing and machine learning as a potent approach to advance biodiversity data science, monitoring, and conservation in the face of climate-induced flooding

Maximum entropy modeling of giant pangolin Smutsia gigantea (Illiger, 1815) habitat suitability in a protected forest-savannah transition area of central Cameroon

Across the planet, biodiversity is facing ever-growing threats including habitat loss, climate change, overexploitation, and pollution. Pangolins of the order Pholidota are the only scaly mammal species worldwide and are considered the most trafficked wild mammals in the world, being widely exploited for their meat and scales. The giant pangolin (Smutsia gigantea, GP) is one of the least studied species of this order, with little being known about their response to environmental and anthropogenic variables, as well as their distribution patterns in forest-savannah transition areas. Our study aimed to increase ecological knowledge about GP by investigating the environmental factors associated with the distribution of suitable habitat for GP within a protected forest/savannah transition area of Cameroon. Using data on the locations of GP resting burrows collected using line transects and employing a maximum entropy (MaxEnt) modelling approach, we explored GP habitat suitability within a forest-savannah transition area of Cameroon. Our model revealed a good level of accuracy based on the average test area under the Receiver Operator Curve metric. The jackknife test found that Euclidian distance to the national park’s boundaries, normalized difference vegetation index, elevation, and distance to river were the most important predictors determining the distribution of GP burrows. Areas predicted to be suitable for GP burrows were patchily distributed within dense forests, ecotone and savannah, with 19.24% of the study area being suitable and 1% very suitable. Overall, our study shows the possible importance of habitat suitability modeling for understanding GP distribution, as well as planning and prioritising their conservation actions

Expanding or shrinking? range shifts in wild ungulates under climate change in Pamir-Karakoram mountains, Pakistan

Climate change is expected to impact a large number of organisms in many ecosystems, including several threatened mammals. A better understanding of climate impacts on species can make conservation efforts more effective. The Himalayan ibex (Capra ibex sibirica) and blue sheep (Pseudois nayaur) are economically important wild ungulates in northern Pakistan because they are sought-after hunting trophies. However, both species are threatened due to several human-induced factors, and these factors are expected to aggravate under changing climate in the High Himalayas. In this study, we investigated populations of ibex and blue sheep in the Pamir-Karakoram mountains in order to (i) update and validate their geographical distributions through empirical data; (ii) understand range shifts under climate change scenarios; and (iii) predict future habitats to aid long-term conservation planning. Presence records of target species were collected through camera trapping and sightings in the field. We constructed Maximum Entropy (MaxEnt) model on presence record and six key climatic variables to predict the current and future distributions of ibex and blue sheep. Two representative concentration pathways (4.5 and 8.5) and two-time projections (2050 and 2070) were used for future range predictions. Our results indicated that ca. 37% and 9% of the total study area (Gilgit-Baltistan) was suitable under current climatic conditions for Himalayan ibex and blue sheep, respectively. Annual mean precipitation was a key determinant of suitable habitat for both ungulate species. Under changing climate scenarios, both species will lose a significant part of their habitats, particularly in the Himalayan and Hindu Kush ranges. The Pamir-Karakoram ranges will serve as climate refugia for both species. This area shall remain focus of future conservation efforts to protect Pakistan’s mountain ungulates