Monitoring landscape changes in Caucasian black grouse (Tetrao mlokosiewiczi) habitat in Iran during the last two decades.

Caucasian black grouse (Tetrao mlokosiewiczi) is on the 'red' list of species of high conservation concern as nearest threatened (NT) and also in level (I) of Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). The black grouse distribution range in Iran is restricted to the Arasbaran region, Northwest of Iran, and the populations and range of this specialist bird species have been declining over the last decades. Management of forest and grassland structures is important for black grouse population survival. The main goals of this study were to monitor and quantify the landscape pattern changes in Caucasian black grouse habitat in the Arasbaran biosphere reserve in two periods of 14 years (1987-2001) and 10 years (2001-2011). For quantifying landscape pattern changes, various landscape metrics were derived by spatial analysis software FRAGSTATS 3.3, including NP (number of habitat patches), LPI (largest patch index) and TE (total edge). The results indicated that the proportion of forest decreased from 39.95 to 31.95% and the proportion of grassland decreased from 44.45 to 38.44% in the 24-year span. NP of forests increased in the first period and decreased in the second period of study. TE of dense forest at altitude above 1800 m decreased. Reduction of forest edge is an indicator of reduction in habitat availability for Caucasian black grouse which use the forest edge for living, lekking and hatching in upland. Our results provided quantitative data on habitat loss and fragmentation in the Arasbaran biosphere reserve and indicated negative impacts of the landscape structure changes on Black grouse habitat.

Exploring biodiversity patterns at the landscape scale by linking landscape energy and land use/land cover heterogeneity.

Agricultural Biodiversity dynamics has been evaluated by social metabolism or by landscape structure-function analysis. In this study, by using ELIA modeling, we used both methods in combination to understand how the interplay between social metabolism and landscape structure-function can affect biodiversity pattern distribution. We used energy reinvestment (E) as an indicator of social metabolism and landscape heterogeneity (Le) as an indicator of landscape structure-function. We propose a research hypothesis to analyze biodiversity patterns considering four different clusters identified based on high or low E or Le. As cluster 1, we defined E as high and Le as low and associated natural ecosystems to it. These ecosystems are expected to contain high species abundance but low richness. As cluster 2, both E and Le were defined as high and semi-natural ecosystems were associated to it, where nature friendly farm system developed. In these ecosystems, high species abundance and richness are expected. Cluster 3 with low E and Le was associated intensive farmland, which is due to the simplification of the landscape. Here, low energy reinvestment and landscape heterogeneity confirm that ecosystem services related to biodiversity have been drastically reduced. Lastly, cluster 4 with low E but high Le refers to intensive mosaics of farmland and pasture. In this cluster, the biodiversity richness index is high due to spatial landscape diversity, but the biodiversity abundance index is low due to the lack of energy reinvestment. We evaluate the proposed hypothesis for biodiversity analysis in the Qazvin province, emphasizing the interplay between energy availability and landscape heterogeneity in shaping ecological communities. This study highlights the importance of understanding biodiversity patterns at spatial scale and emphasizes the need for interdisciplinary research to address conservation and sustainability challenges. Our approach would be very useful where there is lack of biodiversity data.

An energy-landscape integrated analysis to evaluate agroecological scarcity.

Agrarian landscapes theoretically provide ecosystem services that meet the demands of a wide range of socioecological processes. Consequently, any landscape agroecology approach must tackle the dynamic interaction of land-use distribution and associated social metabolism at different spatiotemporal scales. An agroecological scarcity case study explores how driven agricultural energy flows interact with landscape complexity in arid landscapes of 46 counties in the Qazvin Province (Iran). An Energy-Landscape Integrated Analysis (ELIA) was performed to correlate the energy reinvestment (E) and energy redistribution (I) present within the social metabolism network, with landscape complexity (Le) measured in terms of spatial patterns and related ecological processes. As well, a cluster analysis was run to establish agrarian landscape typologies based on the ELIA indicators. The results of this study provide an explicit sketch of the four strategies that society in Qazvin Province has developed within the dry environments that sustain it. Our findings confirm the hypothesis that there is a positive relationship between optimizing non-dissipative internal energy loops and landscape complexity, which can explain agroecosystem sustainability. This research enables us to define spatially informed agroecological transitions from a territorially explicit socioecological perspective and will make a significant contribution to decisions on agricultural policies given different land-use strategies, especially under scenarios of ecological scarcity.