Guidelines for Using Movement Science to Inform Biodiversity Policy

Abstract

Substantial advances have been made in our understanding of the movement of species, including processes such as dispersal and migration. This knowledge has the potential to improve decisions about biodiversity policy and management, but it can be difficult for decision makers to readily access and integrate the growing body of movement science. This is, in part, due to a lack of synthesis of information that is sufficiently contextualized for a policy audience. Here, we identify key species movement concepts, including mechanisms, types, and moderators of movement, and review their relevance to (1) national biodiversity policies and strategies, (2) reserve planning and management, (3) threatened species protection and recovery, (4) impact and risk assessments, and (5) the prioritization of restoration actions. Based on the review, and considering recent developments in movement ecology, we provide a new framework that draws links between aspects of movement knowledge that are likely the most relevant to each biodiversity policy category. Our framework also shows that there is substantial opportunity for collaboration between researchers and government decision makers in the use of movement science to promote positive biodiversity outcomes.

Sighting demographics of the West Indian manatee Trichechus manatus in the north-central Gulf of Mexico supported by citizen-sourced data

“…Basic demographic data are needed to quantify and evaluate range-wide distribution and habitat use changes, understand ecological effects, and support ongoing recovery efforts for manatees and other endangered species experiencing range shifts (USFWS 2001, Barton et al 2015. For example, demographic data may help determine whether increased manatee population density may prompt some manatees in the US to expand their range in search of more abundant or suitable resources (Powell & Rathbun 1984, Bonde & Lefebvre 1999.…”

Silver eel downstream migration in fragmented rivers: use of a Bayesian model to track movements triggering and duration★

“…However, obstacles can have many other consequences (Budy et al, 2002), including stress, disease, injury, increased energy costs, migration delay (Muir et al, 2006;Caudill et al, 2007;Marschall et al, 2011) overpredation, and overfishing (Briand et al, 2003;Garcia De Leaniz, 2008) of populations that often suffer intense exploitation (McDowall, 1999). In view of this, understanding diadromous fish migration is a critical issue for conservation (McDowall, 1999) and can inform biodiversity policy (Barton et al, 2015). This is especially true for catadromous European eels (Anguilla anguilla), which spawn in the Sargasso Sea (Schmidt, 1923;Tesch, 2003) and grow in European continental waters after a few years long larval drift (Bonhommeau et al, 2009).…”

Can AI predict animal movements? Filling gaps in animal trajectories using inverse reinforcement learning

“…Environmentally dependent suitability and costs have been modeled as niche models (Kearney 2006), energy landscapes (Wilson et al 2012), and landscapes of fear (Gallagher et al 2017), all of which are likely to predict route choice and species distribution. Thus, IRL can integrate energy landscapes, landscapes of fear, and any other drivers of animal distribution in space to predict how animals are distributed (Strandburg-Peshkin et al 2017) and to provide information for conservation applications (Barton et al 2015). A big advantage of IRL is its ability to construct reward maps for movement in heterogeneous environments by observing demonstrators (i.e., animals) rather than by manually specifying a reward function.…”