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Habitat loss and fragmentation result in significant landscape changes that ultimately affect plant diversity and add uncertainty to how natural areas will respond to future global change. This uncertainty is important given that the loss of biodiversity often includes losing key ecosystem functions. Few studies have explored the effects of landscape changes on plant functional diversity and evidence so far has shown far more pervasive effects than previously reported by species richness and composition studies. Here we present a review on the impact of habitat loss and fragmentation on (1) individual functional traits—related to persistence, dispersal and establishment—and (2) functional diversity. We also discuss current knowledge gaps and propose ways forward. From the literature review we found that studies have largely focused on dispersal traits, strongly impacted by habitat loss and fragmentation, while traits related to persistence were the least studied. Furthermore, most studies did not distinguish habitat loss from spatial fragmentation and were conducted at the plot or fragment-level, which taken together limits the ability to generalize the scale-dependency of landscape changes on plant functional diversity. For future work, we recommend (1) clearly distinguishing the effects of habitat loss from those of fragmentation, and (2) recognizing the scale-dependency of predicted responses when functional diversity varies in time and space. We conclude that a clear understanding of the effects of habitat loss and fragmentation on functional diversity will improve predictions of the resiliency and resistance of plant communities to varying scales of disturbance. [ABSTRACT FROM AUTHOR]

Ecological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect the outcomes of ecological models used to infer the geographic distribution of species and diversity. There is a vast heterogeneity of methods and approaches to assess and measure spatial bias; this paper aims at addressing the spatial component of data-driven biases in species distribution modelling, and to propose potential solutions to explicitly test and account for them. Our major goal is not to propose methods to remove spatial bias from the modelling procedure, which would be impossible without proper knowledge of all the processes generating it, but rather to propose alternatives to explore and handle it. In particular, we propose and describe three main strategies that may provide a fair account of spatial bias, namely: (i) how to represent spatial bias; (ii) how to simulate null models based on virtual species for testing biogeographical and species distribution hypotheses; and (iii) how to make use of spatial bias - in particular related to sampling effort - as a leverage instead of a hindrance in species distribution modelling. We link these strategies with good practice in accounting for spatial bias in species distribution modelling., (© 2023. The Author(s).)