Habitats have been undergoing significant changes due to environmental processes and human impact that lead into habitat fragmentation and connectivity loss. To improve quality habitats and maintain ecological connectivity, elements that improve the connectivity of habitats need to be identified. To meet this goal, finding optimal pathways locations plays a key role for designing corridors for biodiversity conservation. Conducted in the Castilla y León region of Spain, this paper aims to determine optimal pathways and to enhance the connectivity of protected areas. To this end, three different scenarios were developed including the Natura 2000 network and their surroundings (Natura 2000, Level 0, and Level 1). We used Restoration Planner (RP) available in GuidosToolbox to analyze the network and detect pairwise optimum restoration pathways between the five largest network objects. Our results demonstrate that connector density varies across the region for each scenario. There was also a large variability in the length of connectors. Connectors were found mainly distributed around the center and northwestern part of Castilla y León. This paper also suggests that proposed new restoration pathways should increase in the study area. Thus, the findings can be used effectively for extensive planning and interpretation in biodiversity conservation.

Walkability is a modern concept that has become important in recent years due to the doubtless effects it has on aspects such as health and wellbeing, sustainable development, climate change, and tourism. It is necessary, therefore, that urban development strategies aim to achieve walkable cities. The main objective of this study is to define a methodology to calculate the walkability index in tourist cities and to predict the effects of climate change on this index, which is applied to three World Heritage cities in central Spain: Salamanca, Ávila, and Segovia. The methodology is developed in three phases. Phase I focus on the calculation of walkability, considering the following factors: facilities and services, accessibility, sidewalk width, population density, green areas, and urban trees. In Phase II, walkability in 2020, climate-related variables were added to the previous result: temperatures, solar radiation, and shadows. Finally, the third phase, walkability under climate change pressure in 2030, 2050, and 2100, establish predictions for different climate scenarios. The results show excellent walkability indices (higher) in city centers and newly built neighborhoods and low values in the rest of the peripheral areas, industrial estates, and neighborhoods. Climate predictions showed a generalized decrease in walkability over time, even higher in the scenario with high greenhouse gas emissions. Likewise, the models can be an excellent tool for the tourist management of cities since they show the most walkable areas and, therefore, the most suitable for tourist routes.

The Mediterranean region is one of the 36 hotspots of the world that will be most affected by climate change, with river ecosystems being among the most sensitive to these effects. Therefore, it is necessary to understand and monitor the effects that are occurring through the use of aquatic macroinvertebrates as bioindicators of climate change. To study the use of macroinvertebrates as bioindicators, a systematic literature review was conducted using the PRISMA method. The obtained bibliography was analyzed alongside other known studies to determine the response of these organisms to temperature increases and decreases and alterations in precipitation, as well as their reaction to extreme drought and flood events. The results show that different taxa of macroinvertebrates respond differently to the effects of climate change, always leading to a community alteration with changes in the abundance, richness, phenology, and composition. Therefore, aquatic macroinvertebrates are good bioindicators of the changes caused by climate change, as they respond clearly to the alterations induced by climate change.

Climate change poses a severe environmental crisis where many European urban centers face increased risks, among others, of flooding and/or water scarcity due to intense rainfall and prolonged droughts. Urgent adaptation measures are required to enhance resilience in urban, peri-urban, and agricultural areas against extreme weather events. This article describes a method for categorizing urban areas based on climate-related risks as a targeting and prioritization system for allocating climate resilience measures in cities. The method allows for calculating a climate vulnerability index value, considering temperature, precipitation, wind patterns, population density, altitude, and urban area. Focused on the Mediterranean region, particularly Sicily, the index application generates quantifiable vulnerability values for urban settlements, enabling comparison and prioritization. The reproducible and scalable method provides a valuable tool for urban analysis beyond the Mediterranean and can facilitate decision-making processes to initiate future studies and projects.

This paper presents a multi-criteria methodology for the detection of optimal locations for solar photovoltaic installations connected to the electrical grid. The proposed methodology has been applied to the island of Tenerife, as it is one of the territories in Spain with the greatest solar potential. This methodology integrates an Aptitude Model (which covers variables such as connections to the electrical grid, accessibility, cloudiness, solar irradiation and slope) together with an Impact Model (which considers variables such as landscape vulnerability, land use and hydrology). Each one of the variables considered has been transformed into standardized decision criteria, which have been weighted by means of Saaty’s pair method, having also assigned them relative weights by means of expert consultation. The integration of both Models in a Hosting Capacity Model makes it possible to consider urban and environmental constraints in different possible scenarios. Finally, the Hosting Capacity Model generated is implemented through a Geographic Information System (GIS) on the island of Tenerife, so that it has been possible to detect the optimum locations for each municipality and region.