Wetlands in arid or semi‐arid zones are vital for maintaining biodiversity but face growing threats. Flooding regime variability is a key driver of ecological dynamism in these systems, dictating primary productivity on a large spatial scale. Functional composition or diversity of wetland‐dependent bird species has been found to be sensitive to fluctuations in hydrological regimes and can thus be indicative of cascading ecosystem responses associated with climate change. In this paper we investigate whether large‐scale changes in inundation and fire – a significant additional biodiversity determinant in (semi‐)arid landscapes – were reliable predictors of functional group responses of wetland‐dependent birds along a perennial channel of the Okavango Delta, Botswana. We fit generalized additive models (GAM) to six years of bird survey data collected along ~190‐km‐long annual transects and use remotely sensed landscape‐level inundation estimates, as well as spatio‐temporal distance to fire, to predict responsiveness of seven trait‐based functional group abundances. During the surveys a total of 89 different wetland‐dependent bird species were recorded, including 76 residents, across all years, with below‐surface feeding waders consistently the most abundant functional group. Including estimated spatio‐temporal variability in flooding and fire, as well as their interactions, improved model fit for all seven functional groups, explaining between 46.8% and 68.3% of variability in functional group abundances. Covariates representing longer‐term variability in inundation generally performed better than shorter‐term ones. For example, variability in inundation over the 5 months preceding a survey best predicted the responses of all functional groups, which also all exhibited responsiveness to the interaction between flooding and fire. We were able to interpret the responses of individual functional groups, based on the resource exploitation assumption. Overall, our results suggest that perennial waters in dryland wetlands offer functional refugia to wetland‐dependent birds and highlight the indicative power of large‐scale trait‐based bird monitoring. Our findings demonstrate the potential utility of such a monitoring regime for dryland wetland ecosystems vulnerable to industrial‐scale anthropogenic pressure and associated climate change.ABSTRACT (Setswana)Morethetho wa go tshela ga metsi a makgobokgobo a a fitlhelwang mo nageng tsa komelelo, o rotloetsa mekgwa e dinonyane tse di fitlhelwang mo meeleng e e sa kgaleng ya makgobokgobo, di tsibogelang diemo ka teng.Makgobokgobo a a fitlhelwang mo dikgaolong tsa komelelo a botlhokwa mo go tshegetseng mefutafuta ya ditshidi, mme a lebaganywe ke matshosetsi. Go farologana ga ka fa metsi a makgobokgobo a tshelang ka teng, ke ntlha ya konokono e e tlhotlheletsang diphetogo mo nageng tse, ebile e laola ka fa dimela di dirang ka teng mo nageng eo ka bophara. Mefutafuta ya dinonyane tse di ikaegileng ka makgobokgobo e fitlhetswe e amiwa thata ke diemo tsa metsi tse di fetogafetogang, ka jalo mefuta e, e ka nna kaedi ya mekgwa e botshelo bo fetogang ka teng, segolobogolo fa re lebile phetogo e e sa tlwaelesegang ya loapi (Climate change). Mo mokwalong o, re sekaseka gore a diphetogo tsa maphashaphasha a metsi le melelo ya naga‐ nngwe ya dintlha tsa botlhokwa tse di rotloetsang mefutafuta ya ditshidi mo dinageng tsa komelelo ‐ ele bokao jo bo netlameng go ka supa ka fa mefuta ya dinoyane tse di ikaegileng ka makgobokgobo di tsibogelang diemo ka teng mo moeleng wa makgobokgobo a Okavango, mo Botswana. Go re thusa ka ditshekatsheko tse, re dirisitse boranyane jwa ditlhaka (Generalized Additive Models) go rarabolola patlisitso e e dirilweng mo dingwageng tse thataro ka dinonyane tse di bonweng mo tseleng ya sekgele sa boleele ja 190 km. Tsela e ya tshekatsheko e ne e tsamaiwa gangwefela mo ngwageng. Re ne gape ra dirisa maranyane a lefaufau (Remote sensing) go akanyetsa bophara jwa maphashaphasha a Okavango le go meta sekgele le sebaka sa molelo wa naga. Metlhale e ya tshekatsheko, e ne ya dirisiwa go akanyetsa pele phetogo ya dipalo tsa mefuta e supa ya dinonyane tsa makgokgobobo. Dipatlisiso di supile fa mo dingwageng tse thataro go gatisitswe mefuta e e farologanyeng ya dinonyane tse di ikaegileng ka makgobokgobo di le masome a a ferang bobedi le borobabongwe (89), go akarediwa tsa sennela ruri di le masome a supa le borataro (76). Go supegile gape fa mofuta wa dinonyane tse di jang dijo ko tlase ga metsi e le tsone di palo ntsi. Tharabololo ya maranyane a dipalo (model fit) e kgonne go tlhalosa gore fa gare ga masome mane le borataro le ntlha tse robabobedi mo lekgolong (46.8%) le masome marataro le boferabobedi le ntlha tse tharo mo lekgolong (68.3%) ya diphetogo tse di bonweng tsa mefuta ya dinonyane e bakilwe ke diphetogo tse di farologanyeng tsa go tshela ga metsi a makgobokgobo le molelo wa naga le ka fa dintlha tse di amanang ka teng. Mabaka a a rotloetsang diphetogo tsa maphashaphasha a metsi ka lebaka lo leleele, ka kakaretso a supegile a dira botoka go supa tsibogo ya dinonyane gona le a lebaka le le khutshwane. Sekai, diphetogo tsa selekanyo sa maphashaphasha a metsi mo kgweding tse tlhano (5 months) pele ga dipatlisiso di akanyeditse‐pele botoka ka fa ditlhopha tsotlhe tsa dinoyana di tsibogelang diemo ka teng. E supile tsibogelo diemo ya dinonyane e e bakiwang ke kamano magareng ga go tshela ga metsi le melelo wa naga. Re ne ra kgona go tlhalosa ka fa ditlhopha tsa dinonyane ka bongwe ja tsone di tsibogelang diemo ka teng, re dirisa kakanyetsong ya mekgwa ya tiriso ya ditsa tlholego. Ka kakaretso, maduo a rona a kaya fa metsi a sennela ruri a a bonwang mo makgobokgobong a naga ya komelelo, a a fa dinonyane tse di ikaegileng ka makgobokgobo lefelo la botshabelo, gape a senotse nonofo ya ditshekatsheko tsa peoleitho tse di dirwang ka dinonyane ka lebaka le selekanyo se segolo ebile se tseneletse. Maduo a rona a supile botlhokwa jwa tiriso ya ditlhotlhomiso tsa mofuta o mo makgobokgobong a a bonwang mo nageng tsa komelelo ebile a le mo diphatseng tsa kgotlelesego go tswa mo ditlhabololong le tse di amanang le phetogo e e sa tlwaelesegang ya loapi.
